Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Changes in profile of lipids and adipokines in patients with newly diagnosed hypothyroidism and hyperthyroidism

PubMed Central

Chen, Yanyan; Wu, Xiafang; Wu, Ruirui; Sun, Xiance; Yang, Boyi; Wang, Yi; Xu, Yuanyuan

2016-01-01

Changes in profile of lipids and adipokines have been reported in patients with thyroid dysfunction. But the evidence is controversial. The present study aimed to explore the relationships between thyroid function and the profile of lipids and adipokines. A cross-sectional study was conducted in 197 newly diagnosed hypothyroid patients, 230 newly diagnosed hyperthyroid patients and 355 control subjects. Hypothyroid patients presented with significantly higher serum levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDLC), fasting insulin, resistin and leptin than control (p < 0.05). Hyperthyroid patients presented with significantly lower serum levels of high-density lipoprotein cholesterol, LDLC and leptin, as well as higher levels of fasting insulin, resistin, adiponectin and homeostasis model insulin resistance index (HOMA-IR) than control (p < 0.05). Nonlinear regression and multivariable linear regression models all showed significant associations of resistin or adiponectin with free thyroxine and association of leptin with thyroid-stimulating hormone (p < 0.001). Furthermore, significant correlation between resistin and HOMA-IR was observed in the patients (p < 0.001). Thus, thyroid dysfunction affects the profile of lipids and adipokines. Resistin may serve as a link between thyroid dysfunction and insulin resistance. PMID:27193069

Incorporating TPC observed parameters and QuikSCAT surface wind observations into hurricane initialization using 4D-VAR approaches

NASA Astrophysics Data System (ADS)

Park, Kyungjeen

This study aims to develop an objective hurricane initialization scheme which incorporates not only forecast model constraints but also observed features such as the initial intensity and size. It is based on the four-dimensional variational (4D-Var) bogus data assimilation (BDA) scheme originally proposed by Zou and Xiao (1999). The 4D-Var BDA consists of two steps: (i) specifying a bogus sea level pressure (SLP) field based on parameters observed by the Tropical Prediction Center (TPC) and (ii) assimilating the bogus SLP field under a forecast model constraint to adjust all model variables. This research focuses on improving the specification of the bogus SLP indicated in the first step. Numerical experiments are carried out for Hurricane Bonnie (1998) and Hurricane Gordon (2000) to test the sensitivity of hurricane track and intensity forecasts to specification of initial vortex. Major results are listed below: (1) A linear regression model is developed for determining the size of initial vortex based on the TPC observed radius of 34kt. (2) A method is proposed to derive a radial profile of SLP from QuikSCAT surface winds. This profile is shown to be more realistic than ideal profiles derived from Fujita's and Holland's formulae. (3) It is found that it takes about 1 h for hurricane prediction model to develop a conceptually correct hurricane structure, featuring a dominant role of hydrostatic balance at the initial time and a dynamic adjustment in less than 30 minutes. (4) Numerical experiments suggest that track prediction is less sensitive to the specification of initial vortex structure than intensity forecast. (5) Hurricane initialization using QuikSCAT-derived initial vortex produced a reasonably good forecast for hurricane landfall, with a position error of 25 km and a 4-h delay at landfalling. (6) Numerical experiments using the linear regression model for the size specification considerably outperforms all the other formulations tested in terms of the intensity prediction for both Hurricanes. For examples, the maximum track error is less than 110 km during the entire three-day forecasts for both hurricanes. The simulated Hurricane Gordon using the linear regression model made a nearly perfect landfall, with no position error and only 1-h error in landfalling time. (7) Diagnosis of model output indicates that the initial vortex specified by the linear regression model produces larger surface fluxes of sensible heat, latent heat and moisture, as well as stronger downward angular momentum transport than all the other schemes do. These enhanced energy supplies offset the energy lost caused by friction and gravity wave propagation, allowing for the model to maintain a strong and realistic hurricane during the entire forward model integration.

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

NASA Technical Reports Server (NTRS)

Ledsham, W. H.; Staelin, D. H.

1978-01-01

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors.

PubMed

Yamakado, Minoru; Tanaka, Takayuki; Nagao, Kenji; Imaizumi, Akira; Komatsu, Michiharu; Daimon, Takashi; Miyano, Hiroshi; Tani, Mizuki; Toda, Akiko; Yamamoto, Hiroshi; Horimoto, Katsuhisa; Ishizaka, Yuko

2017-11-03

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.

Linear Regression Links Transcriptomic Data and Cellular Raman Spectra.

PubMed

Kobayashi-Kirschvink, Koseki J; Nakaoka, Hidenori; Oda, Arisa; Kamei, Ken-Ichiro F; Nosho, Kazuki; Fukushima, Hiroko; Kanesaki, Yu; Yajima, Shunsuke; Masaki, Haruhiko; Ohta, Kunihiro; Wakamoto, Yuichi

2018-06-08

Raman microscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra and transcriptomic profiles of Schizosaccharomyces pombe and Escherichia coli can be computationally connected and thus interpreted. We find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA sequencing can be reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Highly expressed non-coding RNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs in S. pombe. This demonstration of correspondence between cellular Raman spectra and transcriptomes is a promising step toward establishing spectroscopic live-cell omics studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Simulation of broadband ground motion including nonlinear soil effects for a magnitude 6.5 earthquake on the Seattle fault, Seattle, Washington

USGS Publications Warehouse

Hartzell, S.; Leeds, A.; Frankel, A.; Williams, R.A.; Odum, J.; Stephenson, W.; Silva, W.

2002-01-01

The Seattle fault poses a significant seismic hazard to the city of Seattle, Washington. A hybrid, low-frequency, high-frequency method is used to calculate broadband (0-20 Hz) ground-motion time histories for a M 6.5 earthquake on the Seattle fault. Low frequencies (1 Hz) are calculated by a stochastic method that uses a fractal subevent size distribution to give an ω-2 displacement spectrum. Time histories are calculated for a grid of stations and then corrected for the local site response using a classification scheme based on the surficial geology. Average shear-wave velocity profiles are developed for six surficial geologic units: artificial fill, modified land, Esperance sand, Lawton clay, till, and Tertiary sandstone. These profiles together with other soil parameters are used to compare linear, equivalent-linear, and nonlinear predictions of ground motion in the frequency band 0-15 Hz. Linear site-response corrections are found to yield unreasonably large ground motions. Equivalent-linear and nonlinear calculations give peak values similar to the 1994 Northridge, California, earthquake and those predicted by regression relationships. Ground-motion variance is estimated for (1) randomization of the velocity profiles, (2) variation in source parameters, and (3) choice of nonlinear model. Within the limits of the models tested, the results are found to be most sensitive to the nonlinear model and soil parameters, notably the over consolidation ratio.

Atmospheric refraction errors in laser ranging systems

NASA Technical Reports Server (NTRS)

Gardner, C. S.; Rowlett, J. R.

1976-01-01

The effects of horizontal refractivity gradients on the accuracy of laser ranging systems were investigated by ray tracing through three dimensional refractivity profiles. The profiles were generated by performing a multiple regression on measurements from seven or eight radiosondes, using a refractivity model which provided for both linear and quadratic variations in the horizontal direction. The range correction due to horizontal gradients was found to be an approximately sinusoidal function of azimuth having a minimum near 0 deg azimuth and a maximum near 180 deg azimuth. The peak to peak variation was approximately 5 centimeters at 10 deg elevation and decreased to less than 1 millimeter at 80 deg elevation.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

HT-FRTC: a fast radiative transfer code using kernel regression

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan; Lewis, Warren

2016-09-01

The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a Gaussian Process (GP) regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatterers. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling).

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.

PubMed

Zhang, Guosheng; Huang, Kuan-Chieh; Xu, Zheng; Tzeng, Jung-Ying; Conneely, Karen N; Guan, Weihua; Kang, Jian; Li, Yun

2016-05-01

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS). © 2016 WILEY PERIODICALS, INC.

Materials characterization on efforts for ablative materials

NASA Technical Reports Server (NTRS)

Tytula, Thomas P.; Schad, Kristin C.; Swann, Myles H.

1992-01-01

Experimental efforts to develop a new procedure to measure char depth in carbon phenolic nozzle material are described. Using a Shor Type D Durometer, hardness profiles were mapped across post fired sample blocks and specimens from a fired rocket nozzle. Linear regression was used to estimate the char depth. Results are compared to those obtained from computed tomography in a comparative experiment. There was no significant difference in the depth estimates obtained by the two methods.

Using Linear Equating to Map PROMIS(®) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3.

PubMed

Hays, Ron D; Revicki, Dennis A; Feeny, David; Fayers, Peter; Spritzer, Karen L; Cella, David

2016-10-01

Preference-based health-related quality of life (HR-QOL) scores are useful as outcome measures in clinical studies, for monitoring the health of populations, and for estimating quality-adjusted life-years. This was a secondary analysis of data collected in an internet survey as part of the Patient-Reported Outcomes Measurement Information System (PROMIS(®)) project. To estimate Health Utilities Index Mark 3 (HUI-3) preference scores, we used the ten PROMIS(®) global health items, the PROMIS-29 V2.0 single pain intensity item and seven multi-item scales (physical functioning, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, sleep disturbance), and the PROMIS-29 V2.0 items. Linear regression analyses were used to identify significant predictors, followed by simple linear equating to avoid regression to the mean. The regression models explained 48 % (global health items), 61 % (PROMIS-29 V2.0 scales), and 64 % (PROMIS-29 V2.0 items) of the variance in the HUI-3 preference score. Linear equated scores were similar to observed scores, although differences tended to be larger for older study participants. HUI-3 preference scores can be estimated from the PROMIS(®) global health items or PROMIS-29 V2.0. The estimated HUI-3 scores from the PROMIS(®) health measures can be used for economic applications and as a measure of overall HR-QOL in research.

Overhead longwave infrared hyperspectral material identification using radiometric models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zelinski, M. E.

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimalmore » atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.« less

Vegan diet and blood lipid profiles: a cross-sectional study of pre and postmenopausal women.

PubMed

Huang, Yee-Wen; Jian, Zhi-Hong; Chang, Hui-Chin; Nfor, Oswald Ndi; Ko, Pei-Chieh; Lung, Chia-Chi; Lin, Long-Yau; Ho, Chien-Chang; Chiang, Yi-Chen; Liaw, Yung-Po

2014-04-08

Vegan diet has been associated with lower risk of cardiovascular diseases and mortality, partly due to its effects on serum lipid profiles. Lipid profiles [high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C) and triglycerides (TG)] have not been fully elucidated either in pre and postmenopausal vegans or in ovo-lacto vegetarians. This study aimed to compare lipid profiles among vegans, ovo-lacto vegetarians and omnivores. Demographic data and lipid profiles were obtained from the 2002 Taiwanese Survey on Hypertension, Hyperglycemia and Hyperlipidemia. Multivariate linear regression analysis was used to examine factors significantly and independently associated with different categories of veganism and to estimate the β value of lipid profiles in the dietary types. A total of 2397 premenopausal and 1154 postmenopausal participants who did not receive lipid lowering drugs were enrolled. Premenopausal vegans had significantly lower HDL-C and higher TG, LDL-C/HDL-C, total cholesterol (TC)/HDL-C and TG/HDL-C compared with omnivores. For postmenopausal women, vegans had lower TC while ovo-lacto vegetarians were observed with low HDL-C when compared with omnivores. Multivariate linear regression analyses showed that vegan and ovo-lacto vegetarian diets decreased HDL-C levels in premenopausal women (β = -7.63, p = 0.001 and β = -4.87, p = 0.001, respectively). There were significant associations between lower LDL-C and ovo-lacto vegetarian diets (β = -7.14, p = 0.008) and also between TG and vegan diet (β = 23.37, p = 0.008), compared with omnivorous diet. Post-menopausal women reported to have consumed either a vegan or an ovo-lacto vegetarian diet were at the risk of having low HDL-C unlike those that consumed omnivorous diets (β = -4.88, p = 0.015 and β = -4.48, p = 0.047). There were no significant changes in LDL-C in both pre and postmenopausal vegans. Vegan diet was associated with reduced HDL-C level. Because of its effects on lowering HDL-C and LDL-C, ovo-lacto vegetarian diet may be more appropriate for premenopausal women.

A regression analysis of filler particle content to predict composite wear.

PubMed

Jaarda, M J; Wang, R F; Lang, B R

1997-01-01

It has been hypothesized that composite wear is correlated to filler particle content. There is a paucity of research to substantiate this theory despite numerous projects evaluating the correlation. The purpose of this study was to determine whether a linear relationship existed between composite wear and filler particle content of 12 composites. In vivo wear data had been previously collected for the 12 composites and served as basis for this study. Scanning electron microscopy and backscatter electron imaging were combined with digital imaging analysis to develop "profile maps" of the filler particle composition of the composites. These profile maps included eight parameters: (1) total number of filler particles/28742.6 microns2, (2) percent of area occupied by all of the filler particles, (3) mean filler particle size, (4) percent of area occupied by the matrix, (5) percent of area occupied by filler particles, r (radius) 1.0 < or = micron, (6) percent of area occupied by filler particles, r = 1.0 < or = 4.5 microns, (7) percent of area occupied by filler particles, r = 4.5 < or = 10 microns, and (8) percent of area occupied by filler particles, r > 10 microns. Forward stepwise regression analyses were used with composite wear as the dependent variable and the eight parameters as independent variables. The results revealed a linear relationship between composite wear and the filler particle content. A mathematical formula was developed to predict composite wear.

Impact of gluconic fermentation of strawberry using acetic acid bacteria on amino acids and biogenic amines profile.

PubMed

Ordóñez, J L; Sainz, F; Callejón, R M; Troncoso, A M; Torija, M J; García-Parrilla, M C

2015-07-01

This paper studies the amino acid profile of beverages obtained through the fermentation of strawberry purée by a surface culture using three strains belonging to different acetic acid bacteria species (one of Gluconobacter japonicus, one of Gluconobacter oxydans and one of Acetobacter malorum). An HPLC-UV method involving diethyl ethoxymethylenemalonate (DEEMM) was adapted and validated. From the entire set of 21 amino acids, multiple linear regressions showed that glutamine, alanine, arginine, tryptophan, GABA and proline were significantly related to the fermentation process. Furthermore, linear discriminant analysis classified 100% of the samples correctly in accordance with the microorganism involved. G. japonicus consumed glucose most quickly and achieved the greatest decrease in amino acid concentration. None of the 8 biogenic amines were detected in the final products, which could serve as a safety guarantee for these strawberry gluconic fermentation beverages, in this regard. Copyright © 2015 Elsevier Ltd. All rights reserved.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Deficient serum 25-hydroxyvitamin D is associated with an atherogenic lipid profile: The Very Large Database of Lipids (VLDL-3) study.

PubMed

Lupton, Joshua R; Faridi, Kamil F; Martin, Seth S; Sharma, Sristi; Kulkarni, Krishnaji; Jones, Steven R; Michos, Erin D

2016-01-01

Cross-sectional studies have found an association between deficiencies in serum vitamin D, as measured by 25-hydroxyvitamin D (25[OH]D), and an atherogenic lipid profile. These studies have focused on a limited panel of lipid values including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Our study examines the relationship between serum 25(OH)D and an extended lipid panel (Vertical Auto Profile) while controlling for age, gender, glycemic status, and kidney function. We used the Very Large Database of Lipids, which includes US adults clinically referred for analysis of their lipid profile from 2009 to 2011. Our study focused on 20,360 subjects who had data for lipids, 25(OH)D, age, gender, hemoglobin A1c, insulin, creatinine, and blood urea nitrogen. Subjects were split into groups based on serum 25(OH)D: deficient (<20 ng/mL), intermediate (≥ 20-30 ng/mL), and optimal (≥ 30 ng/mL). The deficient group was compared to the optimal group using multivariable linear regression. In multivariable-adjusted linear regression, deficient serum 25(OH)D was associated with significantly lower serum HDL-C (-5.1%) and higher total cholesterol (+9.4%), non-HDL-C (+15.4%), directly measured LDL-C (+13.5%), intermediate-density lipoprotein cholesterol (+23.7%), very low-density lipoprotein cholesterol (+19.0%), remnant lipoprotein cholesterol (+18.4%), and TG (+26.4%) when compared with the optimal group. Deficient serum 25(OH)D is associated with significantly lower HDL-C and higher directly measured LDL-C, intermediate-density lipoprotein cholesterol, very low-density lipoproteins cholesterol, remnant lipoprotein cholesterol, and TG. Future trials examining vitamin D supplementation and cardiovascular disease risk should consider using changes in an extended lipid panel as an additional outcome measurement. Copyright © 2016 National Lipid Association. Published by Elsevier Inc. All rights reserved.

Timeline trend profile and seasonal variations in nicotine present in ambient PM10 samples: A four year investigation from Delhi region, India

NASA Astrophysics Data System (ADS)

Yadav, Shweta; Tandon, Ankit; Attri, Arun K.

2014-12-01

The detection of nicotine, an organic tracer for Environmental Tobacco Smoke (ETS), in the collected PM10 samples from Delhi region's ambient environment, in a appropriately designed investigation was initiated over four years (2006-2009) to: (1) Comprehend seasonal and inter-annual variations in the nicotine present in PM10; (2) Extract regression based linear trend profile manifested by nicotine in PM10; (3) Determine the non-linear trend timeline from the nicotine data, and compare it with the obtained linear trend; (4) Suggest the possible use of the designed experiment and analysis to have a qualitative appraisal of Tobacco Smoking activity in the sampling region. The PM10 samples were collected in a monthly time-series sequence at a known receptor site. Quantitative estimates of nicotine (ng m-3) were made by using a Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GC/MS). The annual average concentrations of nicotine (ng m-3) were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006). The estimated linear trend of 5.4 ng m-3 month-1 corresponded to 16.3% per annum increase in the PM10 associated nicotine. The industrial production of India's tobacco index normalized to Delhi region's consumption, pegged an increase at 10.5% per annum over this period.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Trace element analysis of rough diamond by LA-ICP-MS: a case of source discrimination?

PubMed

Dalpé, Claude; Hudon, Pierre; Ballantyne, David J; Williams, Darrell; Marcotte, Denis

2010-11-01

Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications. © 2010 American Academy of Forensic Sciences.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Online shaft encoder geometry compensation for arbitrary shaft speed profiles using Bayesian regression

NASA Astrophysics Data System (ADS)

Diamond, D. H.; Heyns, P. S.; Oberholster, A. J.

2016-12-01

The measurement of instantaneous angular speed is being increasingly investigated for its use in a wide range of condition monitoring and prognostic applications. Central to many measurement techniques are incremental shaft encoders recording the arrival times of shaft angular increments. The conventional approach to processing these signals assumes that the angular increments are equidistant. This assumption is generally incorrect when working with toothed wheels and especially zebra tape encoders and has been shown to introduce errors in the estimated shaft speed. There are some proposed methods in the literature that aim to compensate for this geometric irregularity. Some of the methods require the shaft speed to be perfectly constant for calibration, something rarely achieved in practice. Other methods assume the shaft speed to be nearly constant with minor deviations. Therefore existing methods cannot calibrate the entire shaft encoder geometry for arbitrary shaft speeds. The present article presents a method to calculate the shaft encoder geometry for arbitrary shaft speed profiles. The method uses Bayesian linear regression to calculate the encoder increment distances. The method is derived and then tested against simulated and laboratory experiments. The results indicate that the proposed method is capable of accurately determining the shaft encoder geometry for any shaft speed profile.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

Vegan diet and blood lipid profiles: a cross-sectional study of pre and postmenopausal women

PubMed Central

2014-01-01

Background Vegan diet has been associated with lower risk of cardiovascular diseases and mortality, partly due to its effects on serum lipid profiles. Lipid profiles [high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C) and triglycerides (TG)] have not been fully elucidated either in pre and postmenopausal vegans or in ovo-lacto vegetarians. This study aimed to compare lipid profiles among vegans, ovo-lacto vegetarians and omnivores. Methods Demographic data and lipid profiles were obtained from the 2002 Taiwanese Survey on Hypertension, Hyperglycemia and Hyperlipidemia. Multivariate linear regression analysis was used to examine factors significantly and independently associated with different categories of veganism and to estimate the β value of lipid profiles in the dietary types. Results A total of 2397 premenopausal and 1154 postmenopausal participants who did not receive lipid lowering drugs were enrolled. Premenopausal vegans had significantly lower HDL-C and higher TG, LDL-C/HDL-C, total cholesterol (TC)/HDL-C and TG/HDL-C compared with omnivores. For postmenopausal women, vegans had lower TC while ovo-lacto vegetarians were observed with low HDL-C when compared with omnivores. Multivariate linear regression analyses showed that vegan and ovo-lacto vegetarian diets decreased HDL-C levels in premenopausal women (β = -7.63, p = 0.001 and β = -4.87, p = 0.001, respectively). There were significant associations between lower LDL-C and ovo-lacto vegetarian diets (β = -7.14, p = 0.008) and also between TG and vegan diet (β = 23.37, p = 0.008), compared with omnivorous diet. Post-menopausal women reported to have consumed either a vegan or an ovo-lacto vegetarian diet were at the risk of having low HDL-C unlike those that consumed omnivorous diets (β = -4.88, p = 0.015 and β = -4.48, p = 0.047). There were no significant changes in LDL-C in both pre and postmenopausal vegans. Conclusions Vegan diet was associated with reduced HDL-C level. Because of its effects on lowering HDL-C and LDL-C, ovo-lacto vegetarian diet may be more appropriate for premenopausal women. PMID:24712525

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Regression Analysis of Long-Term Profile Ozone Data Set from BUV Instruments

NASA Technical Reports Server (NTRS)

Stolarski, Richard S.

2005-01-01

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9, ll, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUT instrument, which was operational from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established based on comparisons with SSBUV shuttle flight data. Differences between NOAA 11, Nimbus 7 and NOAA 9 profile zonal means are within plus or minus 5% at all levels when averaged over the respective periods of data overlap. NOAA 16 SBUV/2 data have insufficient overlap with NOAA 11, so its calibration is based on pre-flight information. Mean differences over 4 months of overlap are within plus or minus 7%. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. Initial comparisons of coincident matches of N4 BUV and Arosa Umkehr data show mean differences of 0.5 (0.5)% at 30km; 7.5 (0.5)% at 35 km; and 11 (0.7)% at 40 km, where the number in parentheses is the standard error of the mean. In this study, we use the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. We use a standard linear regression model with proxies for the seasonal cycle, solar cycle, QBO, and ozone trend. To account for the non-linearity of stratospheric chlorine levels since the late 1990s, we use a time series of Effective Chlorine, defined as the global average of Chlorine + 50 * Bromine at 1 hPa, as the trend proxy. The Effective Chlorine data are taken from the 3-D Goddard CTM. We will show the latest trend results using this statistical model. In addition, the Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. From ground-based comparisons we will establish an uncertainty range for the Nimbus 4 data. We then extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the characteristics of the residual, defined as the difference between the data and statistical regression fit, during the Nimbus 4 time period and the 1978-2003 period over which the statistical model coefficients were estimated, and present these results.

Predictive and Feedback Performance Errors are Signaled in the Simple Spike Discharge of Individual Purkinje Cells

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2012-01-01

The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

A cross-sectional assessment of health-related quality of life in Chinese patients with chronic hepatitis c virus infection with EQ-5D.

PubMed

Huang, Rui; Rao, Huiying; Shang, Jia; Chen, Hong; Li, Jun; Xie, Qing; Gao, Zhiliang; Wang, Lei; Wei, Jia; Jiang, Jianning; Sun, Jian; Jiang, Jiaji; Wei, Lai

2018-06-15

Hepatitis C virus (HCV) infection is one of the most common liver infections, with a decrement in HRQoL of HCV patients. This study aims to assess Health-related quality of life (HRQoL) in Chinese patients with chronic HCV infection, and to identify significant predictors of the HRQoL in these patients of China. In this cross-sectional observational study, treatment-naïve Han ethnic adults with chronic HCV infection were enrolled. Adopting European Quality of Life scale (EQ-5D) and EuroQOL visual analogue scale (EQ-VAS) were used to qualify HRQoL. Results were reported in descriptive analyses to describe sociodemographic and clinical characteristics. Multiple linear regression analysis was applied to investigate the associations of these variables with HRQoL. Binary logistic regression analysis was performed to identify associations of these variables with HRQoL by dimensions of EQ-5D. Nine hundred ninety-seven patients were enrolled in the study [median age 46.0 (37.0, 56.0) years; male 54.8%]. Mean EQ-5D index and EQ-VAS score were 0.780 ± 0.083 and 77.2 ± 14.8. Multiple Linear regression analysis showed that income (< 2000 RMB, β = - 0.134; 2000-4999 RMB, β = - 0.085), moderate or severe symptoms of discomfort (more than one symptoms, β = - 0.090), disease profile (cirrhosis, β = - 0.114), hyperlipidemia (β = - 0.065) and depression (β = - 0.065) were independently associated with EQ-5D index. Residence (the west, β = 0.087), income (< 2000 RMB, β = - 0.129; 2000-4999 RMB, β = - 0.052), moderate or severe symptoms of discomfort (more than one symptoms, β = - 0.091), disease profile and depression (β = - 0.316) were the influencing factors on EQ-VAS. Binary logistic regression indicated that disease profile and clinical depression were the major influencing factors on all five dimensions of EQ-5D. In this cross-sectional assessment of HCV patients in China, we indicated HRQoL of Chinese HCV patients. Significant negative associations between HRQoL and sociodemographic and clinical factors such as moderate or severe symptoms of discomfort, disease profile and depression emerged. We have to focus on optimally managing care of HCV patients and improving their HRQoL. ClinicalTrials.gov identifier NCT01293279. Date of registration: February 10, 2011.

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high-spectral resolution radiance measurements

NASA Astrophysics Data System (ADS)

Weisz, Elisabeth; Smith, William L.; Smith, Nadia

2013-06-01

The dual-regression (DR) method retrieves information about the Earth surface and vertical atmospheric conditions from measurements made by any high-spectral resolution infrared sounder in space. The retrieved information includes temperature and atmospheric gases (such as water vapor, ozone, and carbon species) as well as surface and cloud top parameters. The algorithm was designed to produce a high-quality product with low latency and has been demonstrated to yield accurate results in real-time environments. The speed of the retrieval is achieved through linear regression, while accuracy is achieved through a series of classification schemes and decision-making steps. These steps are necessary to account for the nonlinearity of hyperspectral retrievals. In this work, we detail the key steps that have been developed in the DR method to advance accuracy in the retrieval of nonlinear parameters, specifically cloud top pressure. The steps and their impact on retrieval results are discussed in-depth and illustrated through relevant case studies. In addition to discussing and demonstrating advances made in addressing nonlinearity in a linear geophysical retrieval method, advances toward multi-instrument geophysical analysis by applying the DR to three different operational sounders in polar orbit are also noted. For any area on the globe, the DR method achieves consistent accuracy and precision, making it potentially very valuable to both the meteorological and environmental user communities.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Stratospheric Ozone Trends and Variability as Seen by SCIAMACHY from 2002 to 2012

NASA Technical Reports Server (NTRS)

Gebhardt, C.; Rozanov, A.; Hommel, R.; Weber, M.; Bovensmann, H.; Burrows, J. P.; Degenstein, D.; Froidevaux, L.; Thompson, A. M.

2014-01-01

Vertical profiles of the rate of linear change (trend) in the altitude range 15-50 km are determined from decadal O3 time series obtained from SCIAMACHY/ENVISAT measurements in limb-viewing geometry. The trends are calculated by using a multivariate linear regression. Seasonal variations, the quasi-biennial oscillation, signatures of the solar cycle and the El Nino-Southern Oscillation are accounted for in the regression. The time range of trend calculation is August 2002-April 2012. A focus for analysis are the zonal bands of 20 deg N - 20 deg S (tropics), 60 - 50 deg N, and 50 - 60 deg S (midlatitudes). In the tropics, positive trends of up to 5% per decade between 20 and 30 km and negative trends of up to 10% per decade between 30 and 38 km are identified. Positive O3 trends of around 5% per decade are found in the upper stratosphere in the tropics and at midlatitudes. Comparisons between SCIAMACHY and EOS MLS show reasonable agreement both in the tropics and at midlatitudes for most altitudes. In the tropics, measurements from OSIRIS/Odin and SHADOZ are also analysed. These yield rates of linear change of O3 similar to those from SCIAMACHY. However, the trends from SCIAMACHY near 34 km in the tropics are larger than MLS and OSIRIS by a factor of around two.

Technical player profiles related to the physical fitness of young female volleyball players predict team performance.

PubMed

Dávila-Romero, C; Hernández-Mocholí, M A; García-Hermoso, A

2015-03-01

This study is divided into three sequential stages: identification of fitness and game performance profiles (individual player performance), an assessment of the relationship between these profiles, and an assessment of the relationship between individual player profiles and team performance during play (in championship performance). The overall study sample comprised 525 (19 teams) female volleyball players aged 12-16 years and a subsample (N.=43) used to examine study aims one and two was selected from overall sample. Anthropometric, fitness and individual player performance (actual game) data were collected in the subsample. These data were analyzed through clustering methods, ANOVA and independence chi-square test. Then, we investigated whether the proportion of players with the highest individual player performance profile might predict a team's results in the championship. Cluster analysis identified three volleyball fitness profiles (high, medium, and low) and two individual player performance profiles (high and low). The results showed a relationship between both types of profile (fitness and individual player performance). Then, linear regression revealed a moderate relationship between the number of players with a high volleyball fitness profile and a team's results in the championship (R2=0.23). The current study findings may enable coaches and trainers to manage training programs more efficiently in order to obtain tailor-made training, identify volleyball-specific physical fitness training requirements and reach better results during competitions.

Latent profile analysis of regression-based norms demonstrates relationship of compounding MS symptom burden and negative work events.

PubMed

Frndak, Seth E; Smerbeck, Audrey M; Irwin, Lauren N; Drake, Allison S; Kordovski, Victoria M; Kunker, Katrina A; Khan, Anjum L; Benedict, Ralph H B

2016-10-01

We endeavored to clarify how distinct co-occurring symptoms relate to the presence of negative work events in employed multiple sclerosis (MS) patients. Latent profile analysis (LPA) was utilized to elucidate common disability patterns by isolating patient subpopulations. Samples of 272 employed MS patients and 209 healthy controls (HC) were administered neuroperformance tests of ambulation, hand dexterity, processing speed, and memory. Regression-based norms were created from the HC sample. LPA identified latent profiles using the regression-based z-scores. Finally, multinomial logistic regression tested for negative work event differences among the latent profiles. Four profiles were identified via LPA: a common profile (55%) characterized by slightly below average performance in all domains, a broadly low-performing profile (18%), a poor motor abilities profile with average cognition (17%), and a generally high-functioning profile (9%). Multinomial regression analysis revealed that the uniformly low-performing profile demonstrated a higher likelihood of reported negative work events. Employed MS patients with co-occurring motor, memory and processing speed impairments were most likely to report a negative work event, classifying them as uniquely at risk for job loss.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Lipid Profiles in Wheat Cultivars Resistant and Susceptible to Tan Spot and the Effect of Disease on the Profiles

PubMed Central

Kim, Dongwon; Jeannotte, Richard; Welti, Ruth; Bockus, William W.

2013-01-01

Lipid profiles in wheat leaves and the effects of tan spot on the profiles were quantified by mass spectrometry. Inoculation with Pyrenophora tritici-repentis significantly reduced the amount of leaf lipids, including the major plastidic lipids monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), which together accounted for 89% of the mass spectral signal of detected lipids in wheat leaves. Levels of these lipids in susceptible cultivars dropped much more quickly during infection than those in resistant cultivars. Furthermore, cultivars resistant or susceptible to tan spot displayed different lipid profiles; leaves of resistant cultivars had more MGDG and DGDG than susceptible ones, even in non-inoculated plants. Lipid compositional data from leaves of 20 non-inoculated winter wheat cultivars were regressed against an index of disease susceptibility and fitted with a linear model. This analysis demonstrated a significant relationship between resistance and levels of plastidic galactolipids and indicated that cultivars with high resistance to tan spot uniformly had more MGDG and DGDG than cultivars with high susceptibility. These findings suggest that lipid composition of wheat leaves may be a determining factor in the resistance response of cultivars to tan spot. PMID:23035632

The Associations of Gender With Social Participation of Burn Survivors: A Life Impact Burn Recovery Evaluation Profile Study.

PubMed

Levi, Benjamin; Kraft, Casey T; Shapiro, Gabriel D; Trinh, Nhi-Ha T; Dore, Emily C; Jeng, James; Lee, Austin F; Acton, Amy; Marino, Molly; Jette, Alan; Armstrong, Elizabeth A; Schneider, Jeffrey C; Kazis, Lewis E; Ryan, Colleen M

2018-05-04

Burn injury can be debilitating and affect survivors' quality of life in a profound fashion. Burn injury may also lead to serious psychosocial challenges that have not been adequately studied and addressed. Specifically, there has been limited research into the associations of burn injury on community reintegration based on gender. This work analyzed data from 601 burn survivors who completed field testing of a new measure of social participation for burn survivors, the Life Impact Burn Recovery Evaluation (LIBRE) Profile. Differences in item responses between men and women were examined. Scores on the six LIBRE Profile scales were then compared between men and women using analysis of variance and adjusted linear multivariate regression modeling. Overall, men scored significantly better than women on four of the six LIBRE Profile scales: Sexual Relationships, Social Interactions, Work & Employment, and Romantic Relationships. Differences were not substantially reduced after adjustment for demographic characteristics and burn size. Men scored better than women in most of the areas measured by the LIBRE Profile. These gender differences are potentially important for managing burn patients during the post-injury recovery period.

In vitro Cell Viability by CellProfiler® Software as Equivalent to MTT Assay.

PubMed

Gasparini, Luciana S; Macedo, Nayana D; Pimentel, Elisângela F; Fronza, Marcio; Junior, Valdemar L; Borges, Warley S; Cole, Eduardo R; Andrade, Tadeu U; Endringer, Denise C; Lenz, Dominik

2017-07-01

This study evaluated in vitro cell viability by the colorimetric MTT stands for 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay compared to image analysis by CellProfiler ® software. Hepatoma (Hepa-1c1c7) and fibroblast (L929) cells were exposed to isolated substances, camptothecin, lycorine, tazettine, albomaculine, 3-epimacronine, trispheridine, galanthine and Padina gymnospora , Sargassum sp. methanolic extract, and Habranthus itaobinus Ravenna ethyl acetate in different concentrations. After MTT assay, cells were stained with Panotic dye kit. Cell images were obtained with an inverted microscope equipped with a digital camera. The images were analyzed by CellProfiler ® . No cytotoxicity at the highest concentration analyzed for 3-epimacronine, albomaculine, galanthine, trispheridine, P. gymnospora extract and Sargassum sp. extract where detected. Tazettine offered cytotoxicity only against the Hepa1c1c7 cell line. Lycorine, camptothecin, and H. itaobinus extract exhibited cytotoxic effects in both cell lines. The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The error was within the limits of the confidence intervals and these values had a narrow difference. The correlation between the two methods was demonstrated by the linear regression plotted as R 2 . CellProfiler ® image analysis presented similar results to the MTT assay in the identification of viable cells, and image analysis may assist part of biological analysis procedures. The presented methodology is inexpensive and reproducible. In vitro cell viability assessment with MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay may be replaced by image analysis by CellProfiler ® . The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The correlation between the two methods was demonstrated by the linear regression plotted as R2. Abbreviations: HPLC: High pressure liquid chromatography MTT: (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide).

A marketing perspective to "delight" the "patient 2.0": new and challenging expectations for the healthcare provider.

PubMed

Buccoliero, Luca; Bellio, Elena; Mazzola, Maria; Solinas, Elisa

2016-02-09

The study aims at investigating the characteristics and the satisfaction determinants of the emerging patient profile. This profile appears to be more demanding and "empowered" compared to the ones traditionally conceived, asking for unconventional healthcare services and for a closer relationship with providers. Both qualitative (semi-structured interviews and focus groups) and quantitative (survey) analyses were performed on a random sample of 2808 Italian citizens-patients. Analyses entailed descriptive statistics, bivariate analysis and linear regressions. Four relevant dimensions of patient 2.0 experience were identified through a literature review on experiential marketing in healthcare. Beta coefficients exhibited the effect that different healthcare experiential elements have on patient 2.0 satisfaction. Results allow to state that a new marketing approach, based on patient 2.0 characteristics and value drivers, should be adopted in the healthcare sector. Critical satisfaction drivers and new technological healthcare guidelines are identified in order to match the new patient profile needs.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Associations of dietary intake patterns identified using reduced rank regression with markers of arterial stiffness among youth with type 1 diabetes.

PubMed

Lamichhane, A P; Liese, A D; Urbina, E M; Crandell, J L; Jaacks, L M; Dabelea, D; Black, M H; Merchant, A T; Mayer-Davis, E J

2014-12-01

Youth with type 1 diabetes (T1DM) are at substantially increased risk for adverse vascular outcomes, but little is known about the influence of dietary behavior on cardiovascular disease (CVD) risk profile. We aimed to identify dietary intake patterns associated with CVD risk factors and evaluate their impact on arterial stiffness (AS) measures collected thereafter in a cohort of youth with T1DM. Baseline diet data from a food frequency questionnaire and CVD risk factors (triglycerides, low density lipoprotein-cholesterol, systolic blood pressure, hemoglobin A1c, C-reactive protein and waist circumference) were available for 1153 youth aged ⩾10 years with T1DM from the SEARCH for Diabetes in Youth Study. A dietary intake pattern was identified using 33 food groups as predictors and six CVD risk factors as responses in reduced rank regression (RRR) analysis. Associations of this RRR-derived dietary pattern with AS measures (augmentation index (AIx75), n=229; pulse wave velocity, n=237; and brachial distensibility, n=228) were then assessed using linear regression. The RRR-derived pattern was characterized by high intakes of sugar-sweetened beverages (SSB) and diet soda, eggs, potatoes and high-fat meats and low intakes of sweets/desserts and low-fat dairy; major contributors were SSB and diet soda. This pattern captured the largest variability in adverse CVD risk profile and was subsequently associated with AIx75 (β=0.47; P<0.01). The mean difference in AIx75 concentration between the highest and the lowest dietary pattern quartiles was 4.3% in fully adjusted model. Intervention strategies to reduce consumption of unhealthy foods and beverages among youth with T1DM may significantly improve CVD risk profile and ultimately reduce the risk for AS.

An evaluation of the accuracy of some radar wind profiling techniques

NASA Technical Reports Server (NTRS)

Koscielny, A. J.; Doviak, R. J.

1983-01-01

Major advances in Doppler radar measurement in optically clear air have made it feasible to monitor radial velocities in the troposphere and lower stratosphere. For most applications the three dimensional wind vector is monitored rather than the radial velocity. Measurement of the wind vector with a single radar can be made assuming a spatially linear, time invariant wind field. The components and derivatives of the wind are estimated by the parameters of a linear regression of the radial velocities on functions of their spatial locations. The accuracy of the wind measurement thus depends on the locations of the radial velocities. The suitability is evaluated of some of the common retrieval techniques for simultaneous measurement of both the vertical and horizontal wind components. The techniques considered for study are fixed beam, azimuthal scanning (VAD) and elevation scanning (VED).

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies.

PubMed

Buscot, Marie-Jeanne; Wotherspoon, Simon S; Magnussen, Costan G; Juonala, Markus; Sabin, Matthew A; Burgner, David P; Lehtimäki, Terho; Viikari, Jorma S A; Hutri-Kähönen, Nina; Raitakari, Olli T; Thomson, Russell J

2017-06-06

Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health outcome. Indeed, a refined understanding of how deleterious risk factor profiles develop across the life-course may help inform early-life interventions. Previous techniques to determine between-group differences in risk factors at each age may result in biased estimate of the age at divergence. We demonstrate the use of Bayesian hierarchical piecewise regression (BHPR) to generate a point estimate and credible interval for the age at which trajectories diverge between groups for continuous outcome measures that exhibit non-linear within-person response profiles over time. We illustrate our approach by modeling the divergence in childhood-to-adulthood body mass index (BMI) trajectories between two groups of adults with/without type 2 diabetes mellitus (T2DM) in the Cardiovascular Risk in Young Finns Study (YFS). Using the proposed BHPR approach, we estimated the BMI profiles of participants with T2DM diverged from healthy participants at age 16 years for males (95% credible interval (CI):13.5-18 years) and 21 years for females (95% CI: 19.5-23 years). These data suggest that a critical window for weight management intervention in preventing T2DM might exist before the age when BMI growth rate is naturally expected to decrease. Simulation showed that when using pairwise comparison of least-square means from categorical mixed models, smaller sample sizes tended to conclude a later age of divergence. In contrast, the point estimate of the divergence time is not biased by sample size when using the proposed BHPR method. BHPR is a powerful analytic tool to model long-term non-linear longitudinal outcomes, enabling the identification of the age at which risk factor trajectories diverge between groups of participants. The method is suitable for the analysis of unbalanced longitudinal data, with only a limited number of repeated measures per participants and where the time-related outcome is typically marked by transitional changes or by distinct phases of change over time.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Comparison of tropospheric NO2 from in situ aircraft measurements with near-real-time and standard product data from OMI

NASA Astrophysics Data System (ADS)

Bucsela, E. J.; Perring, A. E.; Cohen, R. C.; Boersma, K. F.; Celarier, E. A.; Gleason, J. F.; Wenig, M. O.; Bertram, T. H.; Wooldridge, P. J.; Dirksen, R.; Veefkind, J. P.

2008-08-01

We present an analysis of in situ NO2 measurements from aircraft experiments between summer 2004 and spring 2006. The data are from the INTEX-A, PAVE, and INTEX-B campaigns and constitute the most comprehensive set of tropospheric NO2 profiles to date. Profile shapes from INTEX-A and PAVE are found to be qualitatively similar to annual mean profiles from the GEOS-Chem model. Using profiles from the INTEX-B campaign, we perform error-weighted linear regressions to compare the Ozone Monitoring Instrument (OMI) tropospheric NO2 columns from the near-real-time product (NRT) and standard product (SP) with the integrated in situ columns. Results indicate that the OMI SP algorithm yields NO2 amounts lower than the in situ columns by a factor of 0.86 (±0.2) and that NO2 amounts from the NRT algorithm are higher than the in situ data by a factor of 1.68 (±0.6). The correlation between the satellite and in situ data is good (r = 0.83) for both algorithms. Using averaging kernels, the influence of the algorithm's a priori profiles on the satellite retrieval is explored. Results imply that air mass factors from the a priori profiles are on average slightly larger (˜10%) than those from the measured profiles, but the differences are not significant.

Molecular markers of neuropsychological functioning and Alzheimer's disease.

PubMed

Edwards, Melissa; Balldin, Valerie Hobson; Hall, James; O'Bryant, Sid

2015-03-01

The current project sought to examine molecular markers of neuropsychological functioning among elders with and without Alzheimer's disease (AD) and determine the predictive ability of combined molecular markers and select neuropsychological tests in detecting disease presence. Data were analyzed from 300 participants (n = 150, AD and n = 150, controls) enrolled in the Texas Alzheimer's Research and Care Consortium. Linear regression models were created to examine the link between the top five molecular markers from our AD blood profile and neuropsychological test scores. Logistical regressions were used to predict AD presence using serum biomarkers in combination with select neuropsychological measures. Using the neuropsychological test with the least amount of variance overlap with the molecular markers, the combined neuropsychological test and molecular markers was highly accurate in detecting AD presence. This work provides the foundation for the generation of a point-of-care device that can be used to screen for AD.

Utility of correlation techniques in gravity and magnetic interpretation

NASA Technical Reports Server (NTRS)

Chandler, V. W.; Koski, J. S.; Braice, L. W.; Hinze, W. J.

1977-01-01

Internal correspondence uses Poisson's Theorem in a moving-window linear regression analysis between the anomalous first vertical derivative of gravity and total magnetic field reduced to the pole. The regression parameters provide critical information on source characteristics. The correlation coefficient indicates the strength of the relation between magnetics and gravity. Slope value gives delta j/delta sigma estimates of the anomalous source. The intercept furnishes information on anomaly interference. Cluster analysis consists of the classification of subsets of data into groups of similarity based on correlation of selected characteristics of the anomalies. Model studies are used to illustrate implementation and interpretation procedures of these methods, particularly internal correspondence. Analysis of the results of applying these methods to data from the midcontinent and a transcontinental profile shows they can be useful in identifying crustal provinces, providing information on horizontal and vertical variations of physical properties over province size zones, validating long wavelength anomalies, and isolating geomagnetic field removal problems.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

The Reliability of Individualized Load-Velocity Profiles.

PubMed

Banyard, Harry G; Nosaka, K; Vernon, Alex D; Haff, G Gregory

2017-11-15

This study examined the reliability of peak velocity (PV), mean propulsive velocity (MPV), and mean velocity (MV) in the development of load-velocity profiles (LVP) in the full depth free-weight back squat performed with maximal concentric effort. Eighteen resistance-trained men performed a baseline one-repetition maximum (1RM) back squat trial and three subsequent 1RM trials used for reliability analyses, with 48-hours interval between trials. 1RM trials comprised lifts from six relative loads including 20, 40, 60, 80, 90, and 100% 1RM. Individualized LVPs for PV, MPV, or MV were derived from loads that were highly reliable based on the following criteria: intra-class correlation coefficient (ICC) >0.70, coefficient of variation (CV) ≤10%, and Cohen's d effect size (ES) <0.60. PV was highly reliable at all six loads. Importantly, MPV and MV were highly reliable at 20, 40, 60, 80 and 90% but not 100% 1RM (MPV: ICC=0.66, CV=18.0%, ES=0.10, standard error of the estimate [SEM]=0.04m·s -1 ; MV: ICC=0.55, CV=19.4%, ES=0.08, SEM=0.04m·s -1 ). When considering the reliable ranges, almost perfect correlations were observed for LVPs derived from PV 20-100% (r=0.91-0.93), MPV 20-90% (r=0.92-0.94) and MV 20-90% (r=0.94-0.95). Furthermore, the LVPs were not significantly different (p>0.05) between trials, movement velocities, or between linear regression versus second order polynomial fits. PV 20-100% , MPV 20-90% , and MV 20-90% are reliable and can be utilized to develop LVPs using linear regression. Conceptually, LVPs can be used to monitor changes in movement velocity and employed as a method for adjusting sessional training loads according to daily readiness.

Pesticides Exposure and Cardiovascular Hemodynamic Parameters Among Male Workers Involved in Mosquito Control in East Coast of Malaysia.

PubMed

Samsuddin, Niza; Rampal, Krishna Gopal; Ismail, Noor Hassim; Abdullah, Nor Zamzila; Nasreen, Hashima E

2016-02-01

Research findings have linked exposure to pesticides to an increased risk of cardiovascular (CVS) diseases. Therefore, this study aimed to assess the impact of chronic mix-pesticides exposure on CVS hemodynamic parameters. A total of 198 male Malay pesticide-exposed and 195 male Malay nonexposed workers were examined. Data were collected through exposure-matrix assessment, questionnaire, blood analyses, and CVS assessment. Explanatory variables comprised of lipid profiles, paraoxonase 1 (PON1), and oxidized low-density lipoprotein (ox-LDL). Outcome measures comprised of brachial and aortic diastolic blood pressure (DBP) and systolic BP (SBP), heart rate, and pulse wave velocity (PWV). Linear regressions identified the B coefficient showing how many units of CVS parameters are associated with each unit of covariates. Diazoxonase was significantly lower and ox-LDL was higher among pesticide-exposed workers than the comparison group. The final multivariate linear regression model revealed that age, body mass index (BMI), smoking, and pesticide exposure were independent predictors of brachial and aortic DBP and SBP. Pesticide exposure was also associated with heart rate, but not with PWV. Lipid profiles, PON1 enzymes, and ox-LDL showed no association with any of the CVS parameters. Chronic mix-pesticide exposure among workers involved in mosquito control has possible association with depression of diazoxonase and the increase in ox-LDL, brachial and aortic DBP and SBP, and heart rate. This study raises concerns that those using pesticides may be exposed to hitherto unrecognized CVS risks among others. If this is confirmed by further studies, greater efforts will be needed to protect these workers. © American Journal of Hypertension, Ltd 2015. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Audiometric analyses confirm a cochlear component, disproportional to age, in stapedial otosclerosis.

PubMed

Topsakal, Vedat; Fransen, Erik; Schmerber, Sébastien; Declau, Frank; Yung, Matthew; Gordts, Frans; Van Camp, Guy; Van de Heyning, Paul

2006-09-01

To report the preoperative audiometric profile of surgically confirmed otosclerosis. Retrospective, multicenter study. Four tertiary referral centers. One thousand sixty-four surgically confirmed patients with otosclerosis. Therapeutic ear surgery for hearing improvement. Preoperative audiometric air conduction (AC) and bone conduction (BC) hearing thresholds were obtained retrospectively for 1064 patients with otosclerosis. A cross-sectional multiple linear regression analysis was performed on audiometric data of affected ears. Influences of age and sex were analyzed and age-related typical audiograms were created. Bone conduction thresholds were corrected for Carhart effect and presbyacusis; in addition, we tested to see if separate cochlear otosclerosis component existed. Corrected thresholds were than analyzed separately for progression of cochlear otosclerosis. The study population consisted of 35% men and 65% women (mean age, 44 yr). The mean pure-tone average at 0.5, 1, and 2 kHz was 57 dB hearing level. Multiple linear regression analysis showed significant progression for all measured AC and BC thresholds. The average annual threshold deterioration for AC was 0.45 dB/yr and the annual threshold deterioration for BC was 0.37 dB/yr. The average annual gap expansion was 0.08 dB/year. The corrected BC thresholds for Carhart effect and presbyacusis remained significantly different from zero, but only showed progression at 2 kHz. The preoperative audiological profile of otosclerosis is described. There is a significant sensorineural component in patients with otosclerosis planned for stapedotomy, which is worse than age-related hearing loss by itself. Deterioration rates of AC and BC thresholds have been reported, which can be helpful in clinical practice and might also guide the characterization of allegedly different phenotypes for familial and sporadic otosclerosis.

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

An overview af SAGE I and II ozone measurements

NASA Technical Reports Server (NTRS)

Mccormick, M. P.; Zawodny, J. M.; Veiga, R. E.; Larsen, J. C.; Wang, P. H.

1989-01-01

The stratospheric Aerosol and Gas Experiments (SAGE) I and II measure Mie, Rayleigh, and gaseous extinction profiles using the solar occultation technique. These global measurements yield ozone profiles with a vertical resolution of 1 km which have been routinely obtained for the periods from February 1979 to November 1981 (SAGE I) and October 1984 to the present (SAGE II). The long-term periodic behavior of the measured ozone is presented as well as case studies of the observed short-term spatial and temporal variability. A linear regression shows annual, semiannual, and quasi-biennial oscillation features at various altitudes and latitudes which, in general, agree with past work. Also, ozone, aerosol, and water vapor data are described for the Antarctic springtime, showing large variation relative to the vortex. Cross-sections in latitude and altitude and polar plots at various altitudes clearly delineate the ozone hole vertically and areally.

Spontaneous Imbibition in Low Permeability Medium, SUPRI TR-114

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kovscek, Anthony R.; Schembre, Josephina

1999-08-09

A systematic experimental investigation of capillary pressure characteristics and fluid flow in diatomite was begun. Using an X-ray CT scanner and a specially constructed imbibition cell, we study spontaneous water imbibition processes in diatomite and, for reference, Berea sandstone and chalk. The mass of water imbibed as a function of time is also measured. Imbibition is restricted to concurrent flow. Despite a marked difference in rock properties such as permeability and porosity, we find similar trends in saturation profiles and weight gain versus time functions. Imbibition in diatomote is relatively rapid when initial water saturation is low due to largemore » capillary forces. Using a non-linear regression analysis together with the experimental data, the capillary pressure and water relative permeability curves are determined for the diatomite in the water-air system. The results given for displacement profiles by numerical simulation match the experimental results.« less

Study on the influence of attitude angle on lidar wind measurement results

NASA Astrophysics Data System (ADS)

Han, Xiaochen; Dou, Peilin; Xue, Yangyang

2017-11-01

When carrying on wind profile measurement of offshore wind farm by shipborne Doppler lidar technique, the ship platform often produces motion response under the action of ocean environment load. In order to measure the performance of shipborne lidar, this paper takes two lidar wind measurement results as the research object, simulating the attitude of the ship in the ocean through the three degree of freedom platform, carrying on the synchronous observation test of the wind profile, giving an example of comparing the wind measurement data of two lidars, and carrying out the linear regression statistical analysis for all the experimental correlation data. The results show that the attitude angle will affect the precision of the lidar, The influence of attitude angle on the accuracy of lidar is uncertain. It is of great significance to the application of shipborne Doppler lidar wind measurement technology in the application of wind resources assessment in offshore wind power projects.

Indoor tanning facility density in eighty U.S. cities.

PubMed

Palmer, Richard C; Mayer, Joni A; Woodruff, Susan I; Eckhardt, Laura; Sallis, James F

2002-06-01

The purpose of this study was to examine the number of tanning facilities in select U.S. cities. The twenty most populated cities from each of 4 U.S. regions were selected for the sample. For each city, data on the number of tanning facilities, climate, and general demographic profile were collected. Data for state tanning facility legislation also were collected. A tanning facility density variable was created by dividing the city's number of facilities by its population size. The 80 cities had an average of 50 facilities each. Results of linear regression analysis indicated that higher density was significantly associated with colder climate, lower median income, and higher proportion of Whites. These data indicate that indoor tanning facilities are prevalent in the environments of U.S. urban-dwellers. Cities having the higher density profile may be logical targets for interventions promoting less or safer use of these facilities.

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Relationship between particulate matter exposure and atherogenic profile in "Ground Zero" workers as shown by dynamic contrast enhanced MR imaging.

PubMed

Mani, Venkatesh; Wong, Stephanie K; Sawit, Simonette T; Calcagno, Claudia; Maceda, Cynara; Ramachandran, Sarayu; Fayad, Zahi A; Moline, Jacqueline; McLaughlin, Mary Ann

2013-04-01

In this pilot study, we hypothesize that dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has the potential to evaluate differences in atherosclerosis profiles in patients subjected to high (initial dust cloud) and low (after 13 September 2001) particulate matter (PM) exposure. Exposure to PM may be associated with adverse health effects leading to increased morbidity. Law enforcement workers were exposed to high levels of particulate pollution after working at "Ground Zero" and may exhibit accelerated atherosclerosis. 31 subjects (28 male) with high (n = 19) or low (n = 12) exposure to PM underwent DCE-MRI. Demographics (age, gender, family history, hypertension, diabetes, BMI, and smoking status), biomarkers (lipid profiles, hs-CRP, BP) and ankle-brachial index (ABI) measures (left and right) were obtained from all subjects. Differences between the high and low exposures were compared using independent samples t test. Using linear forward stepwise regression with information criteria model, independent predictors of increased area under curve (AUC) from DCE-MRI were determined using all variables as input. Confidence interval of 95 % was used and variables with p > 0.1 were eliminated. p < 0.05 was considered significant. Subjects with high exposure (HE) had significantly higher DCE-MRI AUC uptake (increased neovascularization) compared to subjects with lower exposure (LE). (AUC: 2.65 ± 0.63 HE vs. 1.88 ± 0.69 LE, p = 0.016). Except for right leg ABI, none of the other parameters were significantly different between the two groups. Regression model indicated that only HE to PM, CRP > 3.0 and total cholesterol were independently associated with increased neovascularization (in decreasing order of importance, all p < 0.026). HE to PM may increase plaque neovascularization, and thereby potentially indicate worsening atherogenic profile of "Ground Zero" workers.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Conference Proceedings on Atmospheric Propagation in the UV, Visible, IR and MM-Wave Region and Related Systems Aspects Held in Copenhagen, Denmark on 9- 13th October 1989

DTIC Science & Technology

1990-03-01

is more likely2 3 . Table 3. Linear Regression Coefficients of Aerosol Concentration and Volumetric Loadings on Wind Speed Size Band Coefficients...vents. S33 UNI L N I m a FE 4 K 26 w " N ~ ~ ~ ~ ~ AN FA CrCS S HI IIq 2-4 C - LA SONDE GRANULOUNTRIQUN. D’autre part, des mesures do granulom ~trie...appel6 NAVY MAR17!- NB’’). Celui-ci rbnulte d’une s~rie de mesures de profils granulom ~tti- ques pris lors de conditions mataorologiques diff6rentes

New evidence and impact of electron transport non-linearities based on new perturbative inter-modulation analysis

NASA Astrophysics Data System (ADS)

van Berkel, M.; Kobayashi, T.; Igami, H.; Vandersteen, G.; Hogeweij, G. M. D.; Tanaka, K.; Tamura, N.; Zwart, H. J.; Kubo, S.; Ito, S.; Tsuchiya, H.; de Baar, M. R.; LHD Experiment Group

2017-12-01

A new methodology to analyze non-linear components in perturbative transport experiments is introduced. The methodology has been experimentally validated in the Large Helical Device for the electron heat transport channel. Electron cyclotron resonance heating with different modulation frequencies by two gyrotrons has been used to directly quantify the amplitude of the non-linear component at the inter-modulation frequencies. The measurements show significant quadratic non-linear contributions and also the absence of cubic and higher order components. The non-linear component is analyzed using the Volterra series, which is the non-linear generalization of transfer functions. This allows us to study the radial distribution of the non-linearity of the plasma and to reconstruct linear profiles where the measurements were not distorted by non-linearities. The reconstructed linear profiles are significantly different from the measured profiles, demonstrating the significant impact that non-linearity can have.

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

A distribution method for analysing the baseline of pulsatile endocrine signals as exemplified by 24-hour growth hormone profiles.

PubMed

Matthews, D R; Hindmarsh, P C; Pringle, P J; Brook, C G

1991-09-01

To develop a method for quantifying the distribution of concentrations present in hormone profiles, which would allow an observer-unbiased estimate of the time concentration attribute and to make an assessment of the baseline. The log-transformed concentrations (regardless of their temporal attribute) are sorted and allocated to class intervals. The number of observations in each interval are then determined and expressed as a percentage of the total number of samples drawn in the study period. The data may be displayed as a frequency distribution or as a cumulative distribution. Cumulative distributions may be plotted as sigmoidal ogives or can be transformed into discrete probabilities (linear probits), which are then linear, and amenable to regression analysis. Probability analysis gives estimates of the mean (the value below which 50% of the observed concentrations lie, which we term 'OC50'). 'Baseline' can be defined in terms of percentage occupancy--the 'Observed Concentration for 5%' (which we term 'OC5') which is the threshold at or below which the hormone concentrations are measured 5% of the time. We report the use of applying this method to 24-hour growth hormone (GH) profiles from 63 children, 26 adults and one giant. We demonstrate that GH effects (growth or gigantism) in these groups are more related to the baseline OC5 concentration than peak concentration (OC5 +/- 95% confidence limits: adults 0.05 +/- 0.04, peak-height-velocity pubertal 0.39 +/- 0.22, giant 8.9 mU/l). Pulsatile hormone profiles can be analysed using this method in order to assess baseline and other concentration domains.

Measuring multi-joint stiffness during single movements: numerical validation of a novel time-frequency approach.

PubMed

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases.

Work, sleep, and cholesterol levels of U.S. long-haul truck drivers

PubMed Central

LEMKE, Michael K.; APOSTOLOPOULOS, Yorghos; HEGE, Adam; WIDEMAN, Laurie; SÖNMEZ, Sevil

2016-01-01

Long-haul truck drivers in the United States experience elevated cardiovascular health risks, possibly due to hypercholesterolemia. The current study has two objectives: 1) to generate a cholesterol profile for U.S. long-haul truck drivers; and 2) to determine the influence of work organization characteristics and sleep quality and duration on cholesterol levels of long-haul truck drivers. Survey and biometric data were collected from 262 long-haul truck drivers. Descriptive analyses were performed for demographic, work organization, sleep, and cholesterol measures. Linear regression and ordinal logistic regression analyses were conducted to examine for possible predictive relationships between demographic, work organization, and sleep variables, and cholesterol outcomes. The majority (66.4%) of drivers had a low HDL (<40 mg/dL), and nearly 42% of drivers had a high-risk total cholesterol to HDL cholesterol ratio. Sleep quality was associated with HDL, LDL, and total cholesterol, and daily work hours were associated with LDL cholesterol. Workday sleep duration was associated with non-HDL cholesterol, and driving experience and sleep quality were associated with cholesterol ratio. Long-haul truck drivers have a high risk cholesterol profile, and sleep quality and work organization factors may induce these cholesterol outcomes. Targeted worksite health promotion programs are needed to curb these atherosclerotic risks. PMID:28049935

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

The Development of a Stochastic Model of the Atmosphere Between 30 and 90 Km to Be Used in Determining the Effect of Atmospheric Variability on Space Shuttle Entry Parameters. Ph.D. Thesis - Virginia Polytechnic Inst. and State Univ.

NASA Technical Reports Server (NTRS)

Campbell, J. W.

1973-01-01

A stochasitc model of the atmosphere between 30 and 90 km was developed for use in Monte Carlo space shuttle entry studies. The model is actually a family of models, one for each latitude-season category as defined in the 1966 U.S. Standard Atmosphere Supplements. Each latitude-season model generates a pseudo-random temperature profile whose mean is the appropriate temperature profile from the Standard Atmosphere Supplements. The standard deviation of temperature at each altitude for a given latitude-season model was estimated from sounding-rocket data. Departures from the mean temperature at each altitude were produced by assuming a linear regression of temperature on the solar heating rate of ozone. A profile of random ozone concentrations was first generated using an auxiliary stochastic ozone model, also developed as part of this study, and then solar heating rates were computed for the random ozone concentrations.

A statistical profile of physical therapists, 1980 and 1990.

PubMed

Chevan, J; Chevan, A

1998-03-01

To plan for future needs, human resource analysts require demographic data. In this research, US census data were used to develop a profile of physical therapists. Data were extracted from the Public Use Microdata Samples of the US censuses of population from 1980 and 1990. Samples of 3,112 physical therapists from 1990 and 1,530 therapists from 1980 were obtained. A profile was generated by use of descriptive statistics to examine geographic distribution, social characteristics, employment characteristics, and income. Linear regression was used to determine factors that influence income. During the 1980s, physical therapy demonstrated remarkable growth, with trends in physical therapist location, gender, age, and place of employment. Even as the profession aged, it stayed an occupation composed predominantly of women, but one less concentrated in hospitals. Geographically, physical therapists remained clustered in the Northeast and along the Pacific Coast. Income generated by physical therapists was predicted by social and geographic characteristics. This study presents a new data source to examine physical therapist characteristics. It provides information necessary for health care planners and analysts to better understand the nature of the profession and those who practice.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques.

PubMed

Douglas, R K; Nawar, S; Alamar, M C; Mouazen, A M; Coulon, F

2018-03-01

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C 10 and C 35 . Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R 2 ) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg -1 , and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R 2 =0.63, RMSEP=107.54mgkg -1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils. Copyright © 2017 Elsevier B.V. All rights reserved.

Immobilization of Cellulase from Bacillus subtilis UniMAP-KB01 on Multi-walled Carbon Nanotubes for Biofuel Production

NASA Astrophysics Data System (ADS)

Naresh, Sandrasekaran; Hoong Shuit, Siew; Kunasundari, Balakrishnan; Hoo Peng, Yong; Qi, Hwa Ng; Teoh, Yi Peng

2018-03-01

Bacillus subtilis UniMAP-KB01, a cellulase producer was isolated from Malaysian mangrove soil. Through morphological identification it was observed that the B. subtilis appears to be in rod shaped and identified as a gram positive bacterium. Growth profile of isolated B. subtilis was established by measuring optical density (OD) at 600 nm for every 1 hour intervals. Polymath software was employed to plot the growth profile and the non-linear plot established gave the precision value of linear regression, R2 of 0.9602, root mean square deviation (RMSD) of 0.0176 and variance of 0.0025. The hydrolysis capacity testing revealed the cellulolytic index of 2.83 ± 0.46 after stained with Gram’s Iodine. The harvested crude enzyme after 24 hours incubation in carboxymethylcellulose (CMC) broth at 45°C and 100 RPM, was tested for enzyme activity. Through Filter Paper Assay (FPA), the cellulase activity was calculated to be 0.05 U/mL. The hydrolysis capacity testing and FPA shown an acceptable value for thermophilic bacterial enzyme activity. Thus, this isolated strain reasoned to be potential for producing thermostable cellulase which will be immobilized onto multi-walled carbon nanotubes and the cellulolytic activity will be characterized for biofuel production.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

Ganglion cell-inner plexiform layer and retinal nerve fiber layer thickness according to myopia and optic disc area: a quantitative and three-dimensional analysis.

PubMed

Seo, Sam; Lee, Chong Eun; Jeong, Jae Hoon; Park, Ki Ho; Kim, Dong Myung; Jeoung, Jin Wook

2017-03-11

To determine the influences of myopia and optic disc size on ganglion cell-inner plexiform layer (GCIPL) and peripapillary retinal nerve fiber layer (RNFL) thickness profiles obtained by spectral domain optical coherence tomography (OCT). One hundred and sixty-eight eyes of 168 young myopic subjects were recruited and assigned to one of three groups according to their spherical equivalent (SE) values and optic disc area. All underwent Cirrus HD-OCT imaging. The influences of myopia and optic disc size on the GCIPL and RNFL thickness profiles were evaluated by multiple comparisons and linear regression analysis. Three-dimensional surface plots of GCIPL and RNFL thickness corresponding to different combinations of myopia and optic disc size were constructed. Each of the quadrant RNFL thicknesses and their overall average were significantly thinner in high myopia compared to low myopia, except for the temporal quadrant (all Ps ≤0.003). The average and all-sectors GCIPL were significantly thinner in high myopia than in moderate- and/or low-myopia (all Ps ≤0.002). The average OCT RNFL thickness was correlated significantly with SE (0.81 μm/diopter, P < 0.001), axial length (-1.44 μm/mm, P < 0.001), and optic disc area (5.35 μm/mm 2 , P < 0.001) by linear regression analysis. As for the OCT GCIPL parameters, average GCIPL thickness showed a significant correlation with SE (0.84 μm/diopter, P < 0.001) and axial length (-1.65 μm/mm, P < 0.001). There was no significant correlation of average GCIPL thickness with optic disc area. Three-dimensional curves showed that larger optic discs were associated with increased average RNFL thickness and that more-myopic eyes were associated with decreased average GCIPL and RNFL thickness. Myopia can significantly affect GCIPL and RNFL thickness profiles, and optic disc size has a significant influence on RNFL thickness. The current OCT maps employed in the evaluation of glaucoma should be analyzed in consideration of refractive status and optic disc size.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

PubMed

Stingone, Jeanette A; Pandey, Om P; Claudio, Luz; Pandey, Gaurav

2017-11-01

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Estimating monotonic rates from biological data using local linear regression.

PubMed

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Predictive value of dysregulation profile trajectories in childhood for symptoms of ADHD, anxiety and depression in late adolescence.

PubMed

Wang, B; Brueni, L G; Isensee, C; Meyer, T; Bock, N; Ravens-Sieberer, U; Klasen, F; Schlack, R; Becker, A; Rothenberger, A

2018-06-01

We examined whether there are certain dysregulation profile trajectories in childhood that may predict an elevated risk for mental disorders in later adolescence. Participants (N = 554) were drawn from a representative community sample of German children, 7-11 years old, who were followed over four measurement points (baseline, 1, 2 and 6 years later). Dysregulation profile, derived from the parent report of the Strengths and Difficulties Questionnaire, was measured at the first three measurement points, while symptoms of attention deficit hyperactivity disorder (ADHD), anxiety and depression were assessed at the fourth measurement point. We used latent class growth analysis to investigate developmental trajectories in the development of the dysregulation profile. The predictive value of dysregulation profile trajectories for later ADHD, anxiety and depression was examined by linear regression. For descriptive comparison, the predictive value of a single measurement (baseline) was calculated. Dysregulation profile was a stable trait during childhood. Boys and girls had similar levels of dysregulation profile over time. Two developmental subgroups were identified, namely the low dysregulation profile and the high dysregulation profile trajectory. The group membership in the high dysregulation profile trajectory (n = 102) was best predictive of later ADHD, regardless of an individual's gender and age. It explained 11% of the behavioural variance. For anxiety this was 8.7% and for depression 5.6%, including some gender effects. The single-point measurement was less predictive. An enduring high dysregulation profile in childhood showed some predictive value for psychological functioning 4 years later. Hence, it might be helpful in the preventive monitoring of children at risk.

Serum amyloid beta peptides in patients with dementia and age-matched non-demented controls as detected by surface-enhanced laser desorption ionisation-time of flight mass spectrometry (SELDI-TOF MS).

PubMed

Frankfort, Suzanne V; van Campen, Jos P C M; Tulner, Linda R; Beijnen, Jos H

2008-09-01

By using surface enhanced laser desorption/ionisation- time of flight mass spectrometry (SELDI-TOF MS) an amyloid beta (Abeta) profile was shown in cerebrospinal fluid (CSF) of patients with dementia. To investigate the Abeta-profile in serum with SELDI-TOF MS, to evaluate if this profile resembles CSF profiles and to investigate the correlation between intensity of Abeta-peptide-peaks in serum and clinical, demographical and genetic variables. Duplicate profiling of Abeta by an SELDI-TOF MS immunocapture assay was performed in 106 patients, suffering from Alzheimer's Disease or Vascular Dementia and age-matched non-demented control patients. Linear regression analyses were performed to investigate the intensities of four selected Abeta peaks as dependent variables in relation to the independent clinical, demographic or genetic variables. Abeta37, Abeta38 and Abeta40 were found among additional unidentified Abeta peptides, with the most pronounced Abeta peak at a molecular mass of 7752. This profile partly resembled the CSF profile. The clinical diagnosis was not a predictive independent variable, however ABCB1 genotypes C1236T, G2677T/A, age and creatinine level showed to be related to Abeta peak intensities in multivariate analyses. We found an Abeta profile in serum that partly resembled the CSF profile in demented patients. Age, creatinine levels, presence of the APOE epsilon4 allele and ABCB1 genotypes (C1236T and G2677T/A) were correlated with the Abeta serum profile. The role of P-gp as an Abeta transporter and the role of ABCB1 genotypes deserves further research. The investigated serum Abeta profile is probably not useful in the diagnosis of dementia.

Circulating leptin and adiponectin are associated with insulin resistance in healthy postmenopausal women with hot flashes.

PubMed

Huang, Wan-Yu; Chang, Chia-Chu; Chen, Dar-Ren; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Hot flashes have been postulated to be linked to the development of metabolic disorders. This study aimed to evaluate the relationship between hot flashes, adipocyte-derived hormones, and insulin resistance in healthy, non-obese postmenopausal women. In this cross-sectional study, a total of 151 women aged 45-60 years were stratified into one of three groups according to hot-flash status over the past three months: never experienced hot flashes (Group N), mild-to-moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, fasting levels of circulating glucose, lipid profiles, plasma insulin, and adipocyte-derived hormones. Multiple linear regression analysis was used to evaluate the associations of hot flashes with adipocyte-derived hormones, and with insulin resistance. The study was performed in a hospital medical center. The mean (standard deviation) of body-mass index was 22.8(2.7) for Group N, 22.6(2.6) for Group M, and 23.5(2.4) for Group S, respectively. Women in Group S displayed statistically significantly higher levels of leptin, fasting glucose, and insulin, and lower levels of adiponectin than those in Groups M and N. Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with higher leptin levels, lower adiponectin levels, and higher leptin-to-adiponectin ratio. Univariate linear regression analysis revealed that hot-flash severity was strongly associated with a higher HOMA-IR index (% difference, 58.03%; 95% confidence interval, 31.00-90.64; p < 0.001). The association between hot flashes and HOMA-IR index was attenuated after adjusting for leptin or adiponectin and was no longer significant after simultaneously adjusting for leptin and adiponectin. The present study provides evidence that hot flashes are associated with insulin resistance in postmenopausal women. It further suggests that hot flash association with insulin resistance is dependent on the combination of leptin and adiponectin variables.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Age estimation standards for a Western Australian population using the coronal pulp cavity index.

PubMed

Karkhanis, Shalmira; Mack, Peter; Franklin, Daniel

2013-09-10

Age estimation is a vital aspect in creating a biological profile and aids investigators by narrowing down potentially matching identities from the available pool. In addition to routine casework, in the present global political scenario, age estimation in living individuals is required in cases of refugees, asylum seekers, human trafficking and to ascertain age of criminal responsibility. Thus robust methods that are simple, non-invasive and ethically viable are required. The aim of the present study is, therefore, to test the reliability and applicability of the coronal pulp cavity index method, for the purpose of developing age estimation standards for an adult Western Australian population. A total of 450 orthopantomograms (220 females and 230 males) of Australian individuals were analyzed. Crown and coronal pulp chamber heights were measured in the mandibular left and right premolars, and the first and second molars. These measurements were then used to calculate the tooth coronal index. Data was analyzed using paired sample t-tests to assess bilateral asymmetry followed by simple linear and multiple regressions to develop age estimation models. The most accurate age estimation based on simple linear regression model was with mandibular right first molar (SEE ±8.271 years). Multiple regression models improved age prediction accuracy considerably and the most accurate model was with bilateral first and second molars (SEE ±6.692 years). This study represents the first investigation of this method in a Western Australian population and our results indicate that the method is suitable for forensic application. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.

PubMed

Berglund, Lars; Garmo, Hans; Lindbäck, Johan; Svärdsudd, Kurt; Zethelius, Björn

2008-09-30

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations. Copyright (c) 2008 John Wiley & Sons, Ltd.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Effect of Malmquist bias on correlation studies with IRAS data base

NASA Technical Reports Server (NTRS)

Verter, Frances

1993-01-01

The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Analyzing Multilevel Data: Comparing Findings from Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2013-01-01

This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Classical Testing in Functional Linear Models.

PubMed

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.

Classical Testing in Functional Linear Models

PubMed Central

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

A Linear Regression and Markov Chain Model for the Arabian Horse Registry

DTIC Science & Technology

1993-04-01

as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

A simplified diagnostic model of orographic rainfall for enhancing satellite-based rainfall estimates in data-poor regions

USGS Publications Warehouse

Funk, Christopher C.; Michaelsen, Joel C.

2004-01-01

An extension of Sinclair's diagnostic model of orographic precipitation (“VDEL”) is developed for use in data-poor regions to enhance rainfall estimates. This extension (VDELB) combines a 2D linearized internal gravity wave calculation with the dot product of the terrain gradient and surface wind to approximate terrain-induced vertical velocity profiles. Slope, wind speed, and stability determine the velocity profile, with either sinusoidal or vertically decaying (evanescent) solutions possible. These velocity profiles replace the parameterized functions in the original VDEL, creating VDELB, a diagnostic accounting for buoyancy effects. A further extension (VDELB*) uses an on/off constraint derived from reanalysis precipitation fields. A validation study over 365 days in the Pacific Northwest suggests that VDELB* can best capture seasonal and geographic variations. A new statistical data-fusion technique is presented and is used to combine VDELB*, reanalysis, and satellite rainfall estimates in southern Africa. The technique, matched filter regression (MFR), sets the variance of the predictors equal to their squared correlation with observed gauge data and predicts rainfall based on the first principal component of the combined data. In the test presented here, mean absolute errors from the MFR technique were 35% lower than the satellite estimates alone. VDELB assumes a linear solution to the wave equations and a Boussinesq atmosphere, and it may give unrealistic responses under extreme conditions. Nonetheless, the results presented here suggest that diagnostic models, driven by reanalysis data, can be used to improve satellite rainfall estimates in data-sparse regions.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-07-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach was justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatland sites in Finland and a tundra site in Siberia. The flux measurements were performed using transparent chambers on vegetated surfaces and opaque chambers on bare peat surfaces. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes and even lower for longer closure times. The degree of underestimation increased with increasing CO2 flux strength and is dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

[Alteration of profile of treatment of the public psychiatric hospitals of Belo Horizonte, Brazil, in the context of mental health care reform].

PubMed

Coelho, Vívian Andrade Araújo; Volpe, Fernando Madalena; Diniz, Sabrina Stephanie Lana; Silva, Eliane Mussel da; Cunha, Cristiane de Freitas

2014-08-01

This article seeks to describe the profile of treatment and internment in public psychiatric hospitals in Belo Horizonte, Brazil, from 2002 to 2011. The changes in the characteristics of treatment and the profiles of the patients treated are analyzed in the context of health care reform. It is a study of temporal series with trend analysis by means of linear regression. There was a reduction in the total of patients treated in the period under scrutiny. Inversely, there was an increase in internments with a reduction in length of stay, though no change in readmission rates. Patients from Belo Horizonte prevailed, however a relative increase in demand from the surrounding area was observed. There was a reversal in the prevalence of morbidity switching from psychotic disorders to disorders resulting from the use of alcohol and/or other drugs. The alteration observed in the profile of treatment in public psychiatric hospitals in Belo Horizonte was concomitant with the progressive implementation of community mental health services, which have probably met the demand that was formerly directed to these hospitals. Currently the psychiatric hospital is not the first, much less the only venue for treatment in the mental health network in Minas Gerais.

Collisional redistribution of radiation. III - The equation of motion for the correlation function and the scattered spectrum

NASA Technical Reports Server (NTRS)

Burnett, K.; Cooper, J.

1980-01-01

Computations were made of the scattering of monochromatic radiation by a degenerate atom in the binary-collision approximation for field strengths whose products of the Rabi frequency for atomic transition and the duration of a strong collision are much less than 1. An expression of motion for the correlation function is derived which does not exclude the region where thermal correlations may be neglected; the equation is valid outside the quantum-regression regime, and has a straightforward solution for practical cases. Solutions for the weak-field linear response regime are presented in terms of generalized absorption and emission profiles which depend on the indices of the atomic multipoles.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-11-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

PubMed

Henneghan, Ashley M; Palesh, Oxana; Harrison, Michelle; Kesler, Shelli R

2018-07-15

The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric approach. Cross sectional data collection included completion of a survey, cognitive testing, and non-fasting blood from 66 participants. Data were analyzed using random forest regression to identify the most significant predictors for each of the cognitive test scores. A different cytokine profile predicted each cognitive test. Adjusted R 2 for each model ranged from 0.71-0.77 (p's < 9.50 -10 ). The relationships between all the cytokine predictors and cognitive test scores were non-linear. Our findings are unique to the field of CRCI and suggest non-linear cytokine specificity to neural networks underlying cognitive functions assessed in this study. Copyright © 2018 Elsevier B.V. All rights reserved.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Private traits and attributes are predictable from digital records of human behavior.

PubMed

Kosinski, Michal; Stillwell, David; Graepel, Thore

2013-04-09

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

GIS Tools to Estimate Average Annual Daily Traffic

DOT National Transportation Integrated Search

2012-06-01

This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

PubMed Central

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R.

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases. PMID:22448233

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Multivariate analysis of DSC-XRD simultaneous measurement data: a study of multistage crystalline structure changes in a linear poly(ethylene imine) thin film.

PubMed

Kakuda, Hiroyuki; Okada, Tetsuo; Otsuka, Makoto; Katsumoto, Yukiteru; Hasegawa, Takeshi

2009-01-01

A multivariate analytical technique has been applied to the analysis of simultaneous measurement data from differential scanning calorimetry (DSC) and X-ray diffraction (XRD) in order to study thermal changes in crystalline structure of a linear poly(ethylene imine) (LPEI) film. A large number of XRD patterns generated from the simultaneous measurements were subjected to an augmented alternative least-squares (ALS) regression analysis, and the XRD patterns were readily decomposed into chemically independent XRD patterns and their thermal profiles were also obtained at the same time. The decomposed XRD patterns and the profiles were useful in discussing the minute peaks in the DSC. The analytical results revealed the following changes of polymorphisms in detail: An LPEI film prepared by casting an aqueous solution was composed of sesquihydrate and hemihydrate crystals. The sesquihydrate one was lost at an early stage of heating, and the film changed into an amorphous state. Once the sesquihydrate was lost by heating, it was not recovered even when it was cooled back to room temperature. When the sample was heated again, structural changes were found between the hemihydrate and the amorphous components. In this manner, the simultaneous DSC-XRD measurements combined with ALS analysis proved to be powerful for obtaining a better understanding of the thermally induced changes of the crystalline structure in a polymer film.

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.

PubMed

Malegori, Cristina; Nascimento Marques, Emanuel José; de Freitas, Sergio Tonetto; Pimentel, Maria Fernanda; Pasquini, Celio; Casiraghi, Ernestina

2017-04-01

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits. Copyright © 2016 Elsevier B.V. All rights reserved.

The association of very-low-density lipoprotein with ankle-brachial index in peritoneal dialysis patients with controlled serum low-density lipoprotein cholesterol level

PubMed Central

2013-01-01

Background Peripheral artery disease (PAD) represents atherosclerotic disease and is a risk factor for death in peritoneal dialysis (PD) patients, who tend to show an atherogenic lipid profile. In this study, we investigated the relationship between lipid profile and ankle-brachial index (ABI) as an index of atherosclerosis in PD patients with controlled serum low-density lipoprotein (LDL) cholesterol level. Methods Thirty-five PD patients, whose serum LDL cholesterol level was controlled at less than 120mg/dl, were enrolled in this cross-sectional study in Japan. The proportions of cholesterol level to total cholesterol level (cholesterol proportion) in 20 lipoprotein fractions and the mean size of lipoprotein particles were measured using an improved method, namely, high-performance gel permeation chromatography. Multivariate linear regression analysis was adjusted for diabetes mellitus and cardiovascular and/or cerebrovascular diseases. Results The mean (standard deviation) age was 61.6 (10.5) years; PD vintage, 38.5 (28.1) months; ABI, 1.07 (0.22). A low ABI (0.9 or lower) was observed in 7 patients (low-ABI group). The low-ABI group showed significantly higher cholesterol proportions in the chylomicron fraction and large very-low-density lipoproteins (VLDLs) (Fractions 3–5) than the high-ABI group (ABI>0.9). Adjusted multivariate linear regression analysis showed that ABI was negatively associated with serum VLDL cholesterol level (parameter estimate=-0.00566, p=0.0074); the cholesterol proportions in large VLDLs (Fraction 4, parameter estimate=-3.82, p=0.038; Fraction 5, parameter estimate=-3.62, p=0.0039) and medium VLDL (Fraction 6, parameter estimate=-3.25, p=0.014); and the size of VLDL particles (parameter estimate=-0.0352, p=0.032). Conclusions This study showed that the characteristics of VLDL particles were associated with ABI among PD patients. Lowering serum VLDL level may be an effective therapy against atherosclerosis in PD patients after the control of serum LDL cholesterol level. PMID:24093487

Evaluation of accuracy of ambulatory glucose profile in an outpatient setting in children with type 1 diabetes.

PubMed

Hulse, Anjana; Rai, Suahma; Prasanna Kumar, K M

2016-01-01

In children with type 1 diabetes, intensive diabetes management has been demonstrated to reduce long-term microvascular complications. At present, self-monitoring of blood glucose (SMBG) by patients at home and glycated hemoglobin estimation every 3 months are used to monitor glycemic control in children. Recently, ambulatory glucose profile (AGP) is increasingly being used to study the glycemic patterns in adults. However, accuracy and reliability of AGP in children have not been evaluated yet. To assess the accuracy of AGP data in children with type 1 diabetes mellitus when compared with laboratory random blood sugar (RBS) levels, capillary blood glucose (CBG) measured by glucometer in the hospital, and SMBG monitored at home. Paired RBS, CBG, and AGP data were analyzed for 51 patients who wore AGP sensors for 2 weeks. Simultaneous venous and CBG samples were collected on day 1 and day 14. SMBG at home was checked and recorded by the patients for optimizing insulin doses. Accuracy measures (mean absolute deviation, mean absolute relative difference (MARD), and coefficient of linear regression of AGP on RBS, CBG, and home-monitored SMBG were calculated. Seventy paired RBS, CBG, and AGP data and 362 paired home-monitored SMBG and AGP data were available. The MARD was 9.56% for AGP over RBS and 15.07% for AGP over CBG. The linear regression coefficient of AGP over RBS was 0.93 and that of AGP over CBG was 0.89 ( P < 0.001). The accuracy of AGP over SMBG was evaluated over four ranges: <75, 76-140, 141-200, and >200 mg/dl. In this study, AGP data significantly correlate with RBS and CBG data in children with type 1 diabetes. However, a large number of samples in a research setting would help to document reproducibility of our results.

Development of advanced diagnostics for characterization of burning droplets in microgravity

NASA Technical Reports Server (NTRS)

Sankar, Subramanian; Buermann, Dale H.; Bachalo, William D.

1995-01-01

Diagnostic techniques currently used for microgravity research are generally not as advanced as those used in earth based gravity experiments. Diagnostic techniques for measuring the instantaneous radial temperature profile (or temperature gradients) within the burning droplet do not exist. Over the past few years, Aerometrics has been researching and developing a rainbow thermometric technique for measuring the droplet temperatures of burning droplets. This technique has recently been integrated with the phase Doppler interferometric technique to yield a diagnostic instrument that can be used to simultaneously measure the size, velocity, and temperature of burning droplets in complex spray flames. Also, the rainbow thermometric technique has been recently integrated with a point-diffraction interferometric technique for measuring the instantaneous gas phase temperature field surrounding a burning droplet. These research programs, apart from being very successful, have also helped us identify other innovative techniques for the characterization of burning droplets. For example, new techniques have been identified for measuring the instantaneous regression rate of burning droplets. Also, there is the possibility of extracting the instantaneous radial temperature distribution or the temperature gradients within a droplet during transient heating. What is important is that these diagnostic techniques have the potential for making use of inexpensive, light-weight, and rugged devices such as diode lasers and linear CCD arrays. As a result, they can be easily packaged for incorporation into microgravity drop-test and flight-test facilities. Furthermore, with the use of linear CCD arrays, data rates as high as 10-100 kHz can be easily achieved. This data rate is orders of magnitude higher than what is currently achievable. In this research and development program, a compact and rugged diagnostic system will be developed that can be used to measure instantaneous fuel droplet diameter, droplet regression rate, and the droplet internal temperature profiles or gradients at very high data rates in microgravity experiments.

Seminal Plasma HIV-1 RNA Concentration Is Strongly Associated with Altered Levels of Seminal Plasma Interferon-γ, Interleukin-17, and Interleukin-5

PubMed Central

Hoffman, Jennifer C.; Anton, Peter A.; Baldwin, Gayle Cocita; Elliott, Julie; Anisman-Posner, Deborah; Tanner, Karen; Grogan, Tristan; Elashoff, David; Sugar, Catherine; Yang, Otto O.

2014-01-01

Abstract Seminal plasma HIV-1 RNA level is an important determinant of the risk of HIV-1 sexual transmission. We investigated potential associations between seminal plasma cytokine levels and viral concentration in the seminal plasma of HIV-1-infected men. This was a prospective, observational study of paired blood and semen samples from 18 HIV-1 chronically infected men off antiretroviral therapy. HIV-1 RNA levels and cytokine levels in seminal plasma and blood plasma were measured and analyzed using simple linear regressions to screen for associations between cytokines and seminal plasma HIV-1 levels. Forward stepwise regression was performed to construct the final multivariate model. The median HIV-1 RNA concentrations were 4.42 log10 copies/ml (IQR 2.98, 4.70) and 2.96 log10 copies/ml (IQR 2, 4.18) in blood and seminal plasma, respectively. In stepwise multivariate linear regression analysis, blood HIV-1 RNA level (p<0.0001) was most strongly associated with seminal plasma HIV-1 RNA level. After controlling for blood HIV-1 RNA level, seminal plasma HIV-1 RNA level was positively associated with interferon (IFN)-γ (p=0.03) and interleukin (IL)-17 (p=0.03) and negatively associated with IL-5 (p=0.0007) in seminal plasma. In addition to blood HIV-1 RNA level, cytokine profiles in the male genital tract are associated with HIV-1 RNA levels in semen. The Th1 and Th17 cytokines IFN-γ and IL-17 are associated with increased seminal plasma HIV-1 RNA, while the Th2 cytokine IL-5 is associated with decreased seminal plasma HIV-1 RNA. These results support the importance of genital tract immunomodulation in HIV-1 transmission. PMID:25209674

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

A simplified competition data analysis for radioligand specific activity determination.

PubMed

Venturino, A; Rivera, E S; Bergoc, R M; Caro, R A

1990-01-01

Non-linear regression and two-step linear fit methods were developed to determine the actual specific activity of 125I-ovine prolactin by radioreceptor self-displacement analysis. The experimental results obtained by the different methods are superposable. The non-linear regression method is considered to be the most adequate procedure to calculate the specific activity, but if its software is not available, the other described methods are also suitable.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

The influence of temperament and character profiles on specialty choice and well-being in medical residents

PubMed Central

Sievert, Martin; Zwir, Igor; Cloninger, Kevin M.; Lester, Nigel; Rozsa, Sandor

2016-01-01

Background Multiple factors influence the decision to enter a career in medicine and choose a specialty. Previous studies have looked at personality differences in medicine but often were unable to describe the heterogeneity that exists within each specialty. Our study used a person-centered approach to characterize the complex relations between the personality profiles of resident physicians and their choice of specialty. Methods 169 resident physicians at a large Midwestern US training hospital completed the Temperament and Character Inventory (TCI) and the Satisfaction with Life Scale (SWLS). Clusters of personality profiles were identified without regard to medical specialty, and then the personality clusters were tested for association with their choice of specialty by co-clustering analysis. Life satisfaction was tested for association with personality traits and medical specialty by linear regression and analysis of variance. Results We identified five clusters of people with distinct personality profiles, and found that these were associated with particular medical specialties Physicians with an “investigative” personality profile often chose pathology or internal medicine, those with a “commanding” personality often chose general surgery, “rescuers” often chose emergency medicine, the “dependable” often chose pediatrics, and the “compassionate” often chose psychiatry. Life satisfaction scores were not enhanced by personality-specialty congruence, but were related strongly to self-directedness regardless of specialty. Conclusions The personality profiles of physicians were strongly associated with their medical specialty choices. Nevertheless, the relationships were complex: physicians with each personality profile went into a variety of medical specialties, and physicians in each medical specialty had variable personality profiles. The plasticity and resilience of physicians were more important for their life satisfaction than was matching personality to the prototype of a particular specialty. PMID:27651982

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Effect of seafood mediated PCB exposure on desaturase activity and PUFA profile in Faroese septuagenarians.

PubMed

Tøttenborg, Sandra Søgaard; Choi, Anna L; Bjerve, Kristian S; Weihe, Pal; Grandjean, Philippe

2015-07-01

Polychlorinated biphenyl (PCB) exposure may affect serum concentrations of polyunsaturated fatty acids (PUFAs) by inhibiting desaturases ∆5 and ∆6 that drive their synthesis from precursor fatty acids. Such changes in the composition of fatty acids may affect cardiovascular disease risk, which is thought to increase at elevated PCB exposures. This population-based cross-sectional study examined 712 Faroese men and women aged 70-74 years. The serum phospholipid fraction of fasting blood samples was used to determine the PUFA profile, including linoleic acid, dihomo-γ-linolenic acid, arachidonic acid, eicosatrienoic acid, and other relevant fatty acids. Ratios between precursor and metabolite fatty acids were used as proxies for ∆5 and ∆6 desaturase activity. Tertiles of serum-PCB concentrations were used in multiple regression analyses to determine the association between the exposure and desaturase activity. In multiple regression models, PCB exposure was inversely related to the estimated Δ6 desaturase activity resulting in accumulation of precursor fatty acids and decrease in the corresponding product PUFAs. A positive association between PCB and Δ5 desaturation was also found. A relative increase in EA was also observed, though only in the third tertile of PCB exposure. Non-linear relationships between the exposure and the desaturase activity were not found. Consuming fish and seafood may not be translated into beneficial fatty acid profiles if the diet simultaneously causes exposure to PCBs. Although the desaturase estimates were likely influenced by dietary intakes of product PUFAs, the association between PCB exposure and ∆6 desaturase activity is plausible and may affect cardiovascular disease risk. Copyright © 2015 Elsevier Inc. All rights reserved.

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

NASA Astrophysics Data System (ADS)

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2015-01-01

The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Relationship between particulate matter exposure and atherogenic profile in “Ground Zero” workers as shown by dynamic contrast enhanced MR imaging

PubMed Central

Wong, Stephanie K.; Sawit, Simonette T.; Calcagno, Claudia; Maceda, Cynara; Ramachandran, Sarayu; Fayad, Zahi A.; Moline, Jacqueline; McLaughlin, Mary Ann

2013-01-01

In this pilot study, we hypothesize that dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has the potential to evaluate differences in atherosclerosis profiles in patients subjected to high (initial dust cloud) and low (after 13 September 2001) particulate matter (PM) exposure. Exposure to PM may be associated with adverse health effects leading to increased morbidity. Law enforcement workers were exposed to high levels of particulate pollution after working at “Ground Zero” and may exhibit accelerated atherosclerosis. 31 subjects (28 male) with high (n = 19) or low (n = 12) exposure to PM underwent DCE-MRI. Demographics (age, gender, family history, hypertension, diabetes, BMI, and smoking status), biomarkers (lipid profiles, hs-CRP, BP) and ankle-brachial index (ABI) measures (left and right) were obtained from all subjects. Differences between the high and low exposures were compared using independent samples t test. Using linear forward stepwise regression with information criteria model, independent predictors of increased area under curve (AUC) from DCE-MRI were determined using all variables as input. Confidence interval of 95 % was used and variables with p > 0.1 were eliminated. p < 0.05 was considered significant. Subjects with high exposure (HE) had significantly higher DCE-MRI AUC uptake (increased neovascularization) compared to subjects with lower exposure (LE). (AUC: 2.65 ± 0.63 HE vs. 1.88 ± 0.69 LE, p = 0.016). Except for right leg ABI, none of the other parameters were significantly different between the two groups. Regression model indicated that only HE to PM, CRP > 3.0 and total cholesterol were independently associated with increased neovascularization (in decreasing order of importance, all p < 0.026). HE to PM may increase plaque neovascularization, and thereby potentially indicate worsening atherogenic profile of “Ground Zero” workers. PMID:23179748

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

An integrated structural and geochemical study of fracture aperture growth in the Campito Formation of eastern California

NASA Astrophysics Data System (ADS)

Doungkaew, N.; Eichhubl, P.

2015-12-01

Processes of fracture formation control flow of fluid in the subsurface and the mechanical properties of the brittle crust. Understanding of fundamental fracture growth mechanisms is essential for understanding fracture formation and cementation in chemically reactive systems with implications for seismic and aseismic fault and fracture processes, migration of hydrocarbons, long-term CO2 storage, and geothermal energy production. A recent study on crack-seal veins in deeply buried sandstone of east Texas provided evidence for non-linear fracture growth, which is indicated by non-elliptical kinematic fracture aperture profiles. We hypothesize that similar non-linear fracture growth also occurs in other geologic settings, including under higher temperature where solution-precipitation reactions are kinetically favored. To test this hypothesis, we investigate processes of fracture growth in quartzitic sandstone of the Campito Formation, eastern California, by combining field structural observations, thin section petrography, and fluid inclusion microthermometry. Fracture aperture profile measurements of cemented opening-mode fractures show both elliptical and non-elliptical kinematic aperture profiles. In general, fractures that contain fibrous crack-seal cement have elliptical aperture profiles. Fractures filled with blocky cement have linear aperture profiles. Elliptical fracture aperture profiles are consistent with linear-elastic or plastic fracture mechanics. Linear aperture profiles may reflect aperture growth controlled by solution-precipitation creep, with the aperture distribution controlled by solution-precipitation kinetics. We hypothesize that synkinematic crack-seal cement preserves the elliptical aperture profiles of elastic fracture opening increments. Blocky cement, on the other hand, may form postkinematically relative to fracture opening, with fracture opening accommodated by continuous solution-precipitation creep.

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Coronary fluorine-18-sodium fluoride uptake is increased in healthy adults with an unfavorable cardiovascular risk profile: results from the CAMONA study.

PubMed

Blomberg, Björn A; Thomassen, Anders; de Jong, Pim A; Lam, Marnix G E; Diederichsen, Axel C P; Olsen, Michael H; Mickley, Hans; Mali, Willem P T M; Alavi, Abass; Høilund-Carlsen, Poul F

2017-11-01

Coronary artery fluorine-18-sodium fluoride (F-NaF) uptake reflects coronary artery calcification metabolism and is considered to be an early prognostic marker of coronary heart disease. This study evaluated the relationship between coronary artery F-NaF uptake and cardiovascular risk in healthy adults at low cardiovascular risk. Study participants underwent blood pressure measurements, blood analyses, and coronary artery F-NaF PET/CT imaging. In addition, the 10-year risk for the development of cardiovascular disease, on the basis of the Framingham Risk Score, was estimated. Multivariable linear regression evaluated the dependence of coronary artery F-NaF uptake on cardiovascular risk factors. We recruited 89 (47 men, 42 women) healthy adults aged 21-75 years. Female sex (0.34 kBq/ml; P=0.009), age (0.16 kBq/ml per SD; P=0.002), and BMI (0.42 kBq/ml per SD; P<0.001) were independent determinants of increased coronary artery F-NaF uptake (adjusted R=0.21; P<0.001). Coronary artery F-NaF uptake increased linearly according to the number of cardiovascular risk factors present (P<0.001 for a linear trend). The estimated 10-year risk for the development of cardiovascular disease was on average 2.4 times higher in adults with coronary artery F-NaF uptake in the highest quartile compared with those in the lowest quartile of the distribution (8.0 vs. 3.3%, P<0.001). Our findings indicate that coronary artery F-NaF PET/CT imaging is feasible in healthy adults at low cardiovascular risk and that an unfavorable cardiovascular risk profile is associated with a marked increase in coronary artery F-NaF uptake.

Slip accumulation and lateral propagation of active normal faults in Afar

NASA Astrophysics Data System (ADS)

Manighetti, I.; King, G. C. P.; Gaudemer, Y.; Scholz, C. H.; Doubre, C.

2001-01-01

We investigate fault growth in Afar, where normal fault systems are known to be currently growing fast and most are propagating to the northwest. Using digital elevation models, we have examined the cumulative slip distribution along 255 faults with lengths ranging from 0.3 to 60 km. Faults exhibiting the elliptical or "bell-shaped" slip profiles predicted by simple linear elastic fracture mechanics or elastic-plastic theories are rare. Most slip profiles are roughly linear for more than half of their length, with overall slopes always <0.035. For the dominant population of NW striking faults and fault systems longer than 2 km, the slip profiles are asymmetric, with slip being maximum near the eastern ends of the profiles where it drops abruptly to zero, whereas slip decreases roughly linearly and tapers in the direction of overall Aden rift propagation. At a more detailed level, most faults appear to be composed of distinct, shorter subfaults or segments, whose slip profiles, while different from one to the next, combine to produce the roughly linear overall slip decrease along the entire fault. On a larger scale, faults cluster into kinematically coupled systems, along which the slip on any scale individual fault or fault system complements that of its neighbors, so that the total slip of the whole system is roughly linearly related to its length, with an average slope again <0.035. We discuss the origin of these quasilinear, asymmetric profiles in terms of "initiation points" where slip starts, and "barriers" where fault propagation is arrested. In the absence of a barrier, slip apparently extends with a roughly linear profile, tapered in the direction of fault propagation.

Nutrient profiling can help identify foods of good nutritional quality for their price: a validation study with linear programming.

PubMed

Maillot, Matthieu; Ferguson, Elaine L; Drewnowski, Adam; Darmon, Nicole

2008-06-01

Nutrient profiling ranks foods based on their nutrient content. They may help identify foods with a good nutritional quality for their price. This hypothesis was tested using diet modeling with linear programming. Analyses were undertaken using food intake data from the nationally representative French INCA (enquête Individuelle et Nationale sur les Consommations Alimentaires) survey and its associated food composition and price database. For each food, a nutrient profile score was defined as the ratio between the previously published nutrient density score (NDS) and the limited nutrient score (LIM); a nutritional quality for price indicator was developed and calculated from the relationship between its NDS:LIM and energy cost (in euro/100 kcal). We developed linear programming models to design diets that fulfilled increasing levels of nutritional constraints at a minimal cost. The median NDS:LIM values of foods selected in modeled diets increased as the levels of nutritional constraints increased (P = 0.005). In addition, the proportion of foods with a good nutritional quality for price indicator was higher (P < 0.0001) among foods selected (81%) than among foods not selected (39%) in modeled diets. This agreement between the linear programming and the nutrient profiling approaches indicates that nutrient profiling can help identify foods of good nutritional quality for their price. Linear programming is a useful tool for testing nutrient profiling systems and validating the concept of nutrient profiling.

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

PubMed

Taylor, Terence E; Lacalle Muls, Helena; Costello, Richard W; Reilly, Richard B

2018-01-01

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence.

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence

PubMed Central

Lacalle Muls, Helena; Costello, Richard W.; Reilly, Richard B.

2018-01-01

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence. PMID:29346430

The dark cube: dark and light character profiles.

PubMed

Garcia, Danilo; Rosenberg, Patricia

2016-01-01

Background. Research addressing distinctions and similarities between people's malevolent character traits (i.e., the Dark Triad: Machiavellianism, narcissism, and psychopathy) has detected inconsistent linear associations to temperament traits. Additionally, these dark traits seem to have a common core expressed as uncooperativeness. Hence, some researchers suggest that the dark traits are best represented as one global construct (i.e., the unification argument) rather than as ternary construct (i.e., the uniqueness argument). We put forward the dark cube (cf. Cloninger's character cube) comprising eight dark profiles that can be used to compare individuals who differ in one dark character trait while holding the other two constant. Our aim was to investigate in which circumstances individuals who are high in each one of the dark character traits differ in Cloninger's "light" character traits: self-directedness, cooperativeness, and self-transcendence. We also investigated if people's dark character profiles were associated to their light character profiles. Method. A total of 997 participants recruited from Amazon's Mechanical Turk (MTurk) responded to the Short Dark Triad and the Short Character Inventory. Participants were allocated to eight different dark profiles and eight light profiles based on their scores in each of the traits and any possible combination of high and low scores. We used three-way interaction regression analyses and t-tests to investigate differences in light character traits between individuals with different dark profiles. As a second step, we compared the individuals' dark profile with her/his character profile using an exact cell-wise analysis conducted in the ROPstat software (http://www.ropstat.com). Results. Individuals who expressed high levels of Machiavellianism and those who expressed high levels of psychopathy also expressed low self-directedness and low cooperativeness. Individuals with high levels of narcissism, in contrast, scored high in self-directedness. Moreover, individuals with a profile low in the dark traits were more likely to end up with a profile high in cooperativeness. The opposite was true for those individuals with a profile high in the dark traits. The rest of the cross-comparisons revealed some of the characteristics of human personality as a non-linear complex dynamic system. Conclusions. Our study suggests that individuals who are high in Machiavellianism and psychopathy share a unified non-agentic and uncooperative character (i.e., irresponsible, low in self-control, unempathetic, unhelpful, untolerant), while individuals high in narcissism have a more unique character configuration expressed as high agency and, when the other dark traits are high, highly spiritual but uncooperative. In other words, based on differences in their associations to the light side of character, the Dark Triad seems to be a dyad rather than a triad.

The dark cube: dark and light character profiles

PubMed Central

2016-01-01

Background. Research addressing distinctions and similarities between people’s malevolent character traits (i.e., the Dark Triad: Machiavellianism, narcissism, and psychopathy) has detected inconsistent linear associations to temperament traits. Additionally, these dark traits seem to have a common core expressed as uncooperativeness. Hence, some researchers suggest that the dark traits are best represented as one global construct (i.e., the unification argument) rather than as ternary construct (i.e., the uniqueness argument). We put forward the dark cube (cf. Cloninger’s character cube) comprising eight dark profiles that can be used to compare individuals who differ in one dark character trait while holding the other two constant. Our aim was to investigate in which circumstances individuals who are high in each one of the dark character traits differ in Cloninger’s “light” character traits: self-directedness, cooperativeness, and self-transcendence. We also investigated if people’s dark character profiles were associated to their light character profiles. Method. A total of 997 participants recruited from Amazon’s Mechanical Turk (MTurk) responded to the Short Dark Triad and the Short Character Inventory. Participants were allocated to eight different dark profiles and eight light profiles based on their scores in each of the traits and any possible combination of high and low scores. We used three-way interaction regression analyses and t-tests to investigate differences in light character traits between individuals with different dark profiles. As a second step, we compared the individuals’ dark profile with her/his character profile using an exact cell-wise analysis conducted in the ROPstat software (http://www.ropstat.com). Results. Individuals who expressed high levels of Machiavellianism and those who expressed high levels of psychopathy also expressed low self-directedness and low cooperativeness. Individuals with high levels of narcissism, in contrast, scored high in self-directedness. Moreover, individuals with a profile low in the dark traits were more likely to end up with a profile high in cooperativeness. The opposite was true for those individuals with a profile high in the dark traits. The rest of the cross-comparisons revealed some of the characteristics of human personality as a non-linear complex dynamic system. Conclusions. Our study suggests that individuals who are high in Machiavellianism and psychopathy share a unified non-agentic and uncooperative character (i.e., irresponsible, low in self-control, unempathetic, unhelpful, untolerant), while individuals high in narcissism have a more unique character configuration expressed as high agency and, when the other dark traits are high, highly spiritual but uncooperative. In other words, based on differences in their associations to the light side of character, the Dark Triad seems to be a dyad rather than a triad. PMID:26966650

Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment.

PubMed

Nirouei, Mahyar; Ghasemi, Ghasem; Abdolmaleki, Parviz; Tavakoli, Abdolreza; Shariati, Shahab

2012-06-01

The antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the target cells are already in different phases of clinical trials. They prevent viral entry and have a highly specific mechanism of action with a low toxicity profile. Few QSAR studies have been performed on this group of inhibitors. This study was performed to develop a quantitative structure-activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4 receptors. Forty different indole glyoxamide derivatives were selected as a sample set and geometrically optimized using Gaussian 98W. Different combinations of multiple linear regression (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear log (1/EC50) prediction. The results that were obtained using GA-ANN were compared with MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR, MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99, 0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were highly effective in designing QSAR models when compared to statistical method.

Specialization Agreements in the Council for Mutual Economic Assistance

DTIC Science & Technology

1988-02-01

proportions to stabilize variance (S. Weisberg, Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134). If the dependent...27, 1986, p. 3. Weisberg, S., Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134. Wiles, P. J., Communist International

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Data Transformations for Inference with Linear Regression: Clarifications and Recommendations

ERIC Educational Resources Information Center

Pek, Jolynn; Wong, Octavia; Wong, C. M.

2017-01-01

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES

EPA Science Inventory

The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Molecular profiling of the developing avian telencephalon: regional timing and brain subdivision continuities.

PubMed

Chen, Chun-Chun; Winkler, Candace M; Pfenning, Andreas R; Jarvis, Erich D

2013-11-01

In our companion study (Jarvis et al. [2013] J Comp Neurol. doi: 10.1002/cne.23404) we used quantitative brain molecular profiling to discover that distinct subdivisions in the avian pallium above and below the ventricle and the associated mesopallium lamina have similar molecular profiles, leading to a hypothesis that they may form as continuous subdivisions around the lateral ventricle. To explore this hypothesis, here we profiled the expression of 16 genes at eight developmental stages. The genes included those that define brain subdivisions in the adult and some that are also involved in brain development. We found that phyletic hierarchical cluster and linear regression network analyses of gene expression profiles implicated single and mixed ancestry of these brain regions at early embryonic stages. Most gene expression-defined pallial subdivisions began as one ventral or dorsal domain that later formed specific folds around the lateral ventricle. Subsequently a clear ventricle boundary formed, partitioning them into dorsal and ventral pallial subdivisions surrounding the mesopallium lamina. These subdivisions each included two parts of the mesopallium, the nidopallium and hyperpallium, and the arcopallium and hippocampus, respectively. Each subdivision expression profile had a different temporal order of appearance, similar in timing to the order of analogous cell types of the mammalian cortex. Furthermore, like the mammalian pallium, expression in the ventral pallial subdivisions became distinct during prehatch development, whereas the dorsal portions did so during posthatch development. These findings support the continuum hypothesis of avian brain subdivision development around the ventricle and influence hypotheses on homologies of the avian pallium with other vertebrates. Copyright © 2013 Wiley Periodicals, Inc.

Influence of a ketogenic diet, fish-oil, and calorie restriction on plasma metabolites and lipids in C57BL/6J mice

PubMed Central

2014-01-01

Background Diet therapies including calorie restriction, ketogenic diets, and fish-oil supplementation have been used to improve health and to treat a variety of neurological and non-neurological diseases. Methods We investigated the effects of three diets on circulating plasma metabolites (glucose and β-hydroxybutyrate), hormones (insulin and adiponectin), and lipids over a 32-day period in C57BL/6J mice. The diets evaluated included a standard rodent diet (SD), a ketogenic diet (KD), and a standard rodent diet supplemented with fish-oil (FO). Each diet was administered in either unrestricted (UR) or restricted (R) amounts to reduce body weight by 20%. Results The KD-UR increased body weight and glucose levels and promoted a hyperlipidemic profile, whereas the FO-UR decreased body weight and glucose levels and promoted a normolipidemic profile, compared to the SD-UR. When administered in restricted amounts, all three diets produced a similar plasma metabolite profile, which included decreased glucose levels and a normolipidemic profile. Linear regression analysis showed that circulating glucose most strongly predicted body weight and triglyceride levels, whereas calorie intake moderately predicted glucose levels and strongly predicted ketone body levels. Conclusions These results suggest that biomarkers of health can be improved when diets are consumed in restricted amounts, regardless of macronutrient composition. PMID:24910707

Coconut oil predicts a beneficial lipid profile in pre-menopausal women in the Philippines

PubMed Central

Feranil, Alan B.; Duazo, Paulita L.; Kuzawa, Christopher W.; Adair, Linda S.

2011-01-01

Coconut oil is a common edible oil in many countries, and there is mixed evidence for its effects on lipid profiles and cardiovascular disease risk. Here we examine the association between coconut oil consumption and lipid profiles in a cohort of 1,839 Filipino women (age 35–69 years) participating in the Cebu Longitudinal Health and Nutrition Survey, a community based study in Metropolitan Cebu City. Coconut oil intake was measured as individual coconut oil intake calculated using two 24-hour dietary recalls (9.54 ± 8.92 grams). Cholesterol profiles were measured in plasma samples collected after an overnight fast. Mean lipid values in this sample were total cholesterol (TC) (186.52 ± 38.86 mg/dL), high density lipoprotein cholesterol (HDL-c) (40.85 ± 10.30 mg/dL), low density lipoprotein cholesterol (LDL-c) (119.42 ± 33.21 mg/dL), triglycerides (130.75 ± 85.29 mg/dL) and the TC/HDL ratio (4.80 ± 1.41). Linear regression models were used to estimate the association between coconut oil intake and each plasma lipid outcome after adjusting for total energy intake, age, body mass index (BMI), number of pregnancies, education, menopausal status, household assets and urban residency. Dietary coconut oil intake was positively associated with HDL-c levels. PMID:21669587

Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population

PubMed Central

Yamakado, Minoru; Nagao, Kenji; Imaizumi, Akira; Tani, Mizuki; Toda, Akiko; Tanaka, Takayuki; Jinzu, Hiroko; Miyano, Hiroshi; Yamamoto, Hiroshi; Daimon, Takashi; Horimoto, Katsuhisa; Ishizaka, Yuko

2015-01-01

Plasma free amino acid (PFAA) profile is highlighted in its association with visceral obesity and hyperinsulinemia, and future diabetes. Indeed PFAA profiling potentially can evaluate individuals’ future risks of developing lifestyle-related diseases, in addition to diabetes. However, few studies have been performed especially in Asian populations, about the optimal combination of PFAAs for evaluating health risks. We quantified PFAA levels in 3,701 Japanese subjects, and determined visceral fat area (VFA) and two-hour post-challenge insulin (Ins120 min) values in 865 and 1,160 subjects, respectively. Then, models between PFAA levels and the VFA or Ins120 min values were constructed by multiple linear regression analysis with variable selection. Finally, a cohort study of 2,984 subjects to examine capabilities of the obtained models for predicting four-year risk of developing new-onset lifestyle-related diseases was conducted. The correlation coefficients of the obtained PFAA models against VFA or Ins120 min were higher than single PFAA level. Our models work well for future risk prediction. Even after adjusting for commonly accepted multiple risk factors, these models can predict future development of diabetes, metabolic syndrome, and dyslipidemia. PFAA profiles confer independent and differing contributions to increasing the lifestyle-related disease risks in addition to the currently known factors in a general Japanese population. PMID:26156880

Relationship between serum lipid concentrations and posttraumatic stress symptoms in the bereaved after the Sewol ferry disaster: A prospective cohort study.

PubMed

Tae, Hyejin; Huh, Hyu Jung; Hwang, Jihyun; Chae, Jeong-Ho

2018-05-16

The objective of this study was to investigate the relationship between serum lipid concentrations and PTSD symptoms in the bereaved after a traumatic familial loss. Eighteen months after the Sewol ferry disaster, 107 subjects who experienced traumatic losses as a result of the accident completed a mental and medical survey as well as laboratory tests for lipid profiles. At 30 months after the trauma, a total of 64 individuals completed a follow-up psychometric survey and biochemical measurements. We performed multiple linear regression analyses, examining the association between PTSD symptoms and lipid profiles. Other potential influences on lipid profiles such as metabolic risk factors, demographic risk factors, and underlying medical history were accounted for. Participants reporting clinically significant PTSD symptoms exhibited lower serum HDL-C levels than those without PTSD symptoms. In addition, we found that the severity of PTSD symptoms and sex could explain the changes in lipid profiles independently of other possible risk factors of changes. The results of this study suggest that PTSD symptoms may contribute to an increased risk for developing metabolic syndrome via detrimental changes in lipid concentrations. Routine screening and multidisciplinary management to prevent metabolic syndrome in individuals who experience traumatic losses would therefore be valuable. Copyright © 2018 Elsevier B.V. All rights reserved.

Version 8 SBUV Ozone Profile Trends Compared with Trends from a Zonally Averaged Chemical Model

NASA Technical Reports Server (NTRS)

Rosenfield, Joan E.; Frith, Stacey; Stolarski, Richard

2004-01-01

Linear regression trends for the years 1979-2003 were computed using the new Version 8 merged Solar Backscatter Ultraviolet (SBUV) data set of ozone profiles. These trends were compared to trends computed using ozone profiles from the Goddard Space Flight Center (GSFC) zonally averaged coupled model. Observed and modeled annual trends between 50 N and 50 S were a maximum in the higher latitudes of the upper stratosphere, with southern hemisphere (SH) trends greater than northern hemisphere (NH) trends. The observed upper stratospheric maximum annual trend is -5.5 +/- 0.9 % per decade (1 sigma) at 47.5 S and -3.8 +/- 0.5 % per decade at 47.5 N, to be compared with the modeled trends of -4.5 +/- 0.3 % per decade in the SH and -4.0 +/- 0.2% per decade in the NH. Both observed and modeled trends are most negative in winter and least negative in summer, although the modeled seasonal difference is less than observed. Model trends are shown to be greatest in winter due to a repartitioning of chlorine species and the increasing abundance of chlorine with time. The model results show that trend differences can occur depending on whether ozone profiles are in mixing ratio or number density coordinates, and on whether they are recorded on pressure or altitude levels.

The reliability of isoinertial force-velocity-power profiling and maximal strength assessment in youth.

PubMed

Meylan, César M P; Cronin, John B; Oliver, Jon L; Hughes, Michael M G; Jidovtseff, Boris; Pinder, Shane

2015-03-01

The purpose of this study was to quantify the inter-session reliability of force-velocity-power profiling and estimated maximal strength in youth. Thirty-six males (11-15 years old) performed a ballistic supine leg press test at five randomized loads (80%, 100%, 120%, 140%, and 160% body mass) on three separate occasions. Peak and mean force, power, velocity, and peak displacement were collected with a linear position transducer attached to the weight stack. Mean values at each load were used to calculate different regression lines and estimate maximal strength, force, velocity, and power. All variables were found reliable (change in the mean [CIM] = - 1 to 14%; coefficient of variation [CV] = 3-18%; intraclass correlation coefficient [ICC] = 0.74-0.99), but were likely to benefit from a familiarization, apart from the unreliable maximal force/velocity ratio (CIM = 0-3%; CV = 23-25%; ICC = 0.35-0.54) and load at maximal power (CIM = - 1 to 2%; CV = 10-13%; ICC = 0.26-0.61). Isoinertial force-velocity-power profiling and maximal strength in youth can be assessed after a familiarization session. Such profiling may provide valuable insight into neuromuscular capabilities during growth and maturation and may be used to monitor specific training adaptations.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Circulating leptin and adiponectin are associated with insulin resistance in healthy postmenopausal women with hot flashes

PubMed Central

Huang, Wan-Yu; Chang, Chia-Chu; Chen, Dar-Ren; Kor, Chew-Teng; Chen, Ting-Yu; Wu, Hung-Ming

2017-01-01

Introduction Hot flashes have been postulated to be linked to the development of metabolic disorders. This study aimed to evaluate the relationship between hot flashes, adipocyte-derived hormones, and insulin resistance in healthy, non-obese postmenopausal women. Participants and design In this cross-sectional study, a total of 151 women aged 45–60 years were stratified into one of three groups according to hot-flash status over the past three months: never experienced hot flashes (Group N), mild-to-moderate hot flashes (Group M), and severe hot flashes (Group S). Variables measured in this study included clinical parameters, hot flash experience, fasting levels of circulating glucose, lipid profiles, plasma insulin, and adipocyte-derived hormones. Multiple linear regression analysis was used to evaluate the associations of hot flashes with adipocyte-derived hormones, and with insulin resistance. Settings The study was performed in a hospital medical center. Results The mean (standard deviation) of body-mass index was 22.8(2.7) for Group N, 22.6(2.6) for Group M, and 23.5(2.4) for Group S, respectively. Women in Group S displayed statistically significantly higher levels of leptin, fasting glucose, and insulin, and lower levels of adiponectin than those in Groups M and N. Multivariate linear regression analysis revealed that hot-flash severity was significantly associated with higher leptin levels, lower adiponectin levels, and higher leptin-to-adiponectin ratio. Univariate linear regression analysis revealed that hot-flash severity was strongly associated with a higher HOMA-IR index (% difference, 58.03%; 95% confidence interval, 31.00–90.64; p < 0.001). The association between hot flashes and HOMA-IR index was attenuated after adjusting for leptin or adiponectin and was no longer significant after simultaneously adjusting for leptin and adiponectin. Conclusion The present study provides evidence that hot flashes are associated with insulin resistance in postmenopausal women. It further suggests that hot flash association with insulin resistance is dependent on the combination of leptin and adiponectin variables. PMID:28448547

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Leveraging cues from person-generated health data for peer matching in online communities

PubMed Central

Hartzler, Andrea L; Taylor, Megan N; Park, Albert; Griffiths, Troy; Backonja, Uba; McDonald, David W; Wahbeh, Sam; Brown, Cory; Pratt, Wanda

2016-01-01

Objective Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor. Materials and Methods Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants’ ratings with linear regression and qualitatively analyzed participants’ feedback for emerging themes about choosing mentors and improving profile design. Results Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor. Conclusion Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities. PMID:26911825

The association between the activity profile and cardiovascular risk.

PubMed

Maddison, Ralph; Jiang, Yannan; Foley, Louise; Scragg, Robert; Direito, Artur; Olds, Timothy

2016-08-01

This study sought to better understand the interrelationships between physical activity and sedentary behaviour and the relationship to risk of cardiovascular disease (CVDR) in adults aged 30-75 years. Cross-sectional. Data from two-year waves (2003-2004 and 2005-2006) of the National Health and Nutritional Examination survey were analysed in 2014. Accelerometer-derived time and proportion of time spent sedentary and on moderate-to-vigorous physical activity (MVPA) were calculated to generate four activity profiles based on cut-points to define low and high levels for the respective behaviours. Using health outcome data, CVDR was calculated for each person. Weighted multiple linear regression models were used to evaluate the predicted effects of sedentary and physical activity behaviours on the CVDR score, adjusting for participants' sex, age group, race, annual household income, and accelerometer wear time. The lowest CVDR was observed among Busy Exercisers (high MVPA and low sedentary; 8.5%), whereas Couch Potatoes (low MVPA and high sedentary) had the highest (18.6%). Compared with the reference group (Busy Exercisers), the activity profile associated with the highest CVDR was Couch Potatoes (adjusted mean difference 3.6, SE 0.38, p<0.0001). A smoothed three-dimensional response surface "risk landscape" was developed to better visualise the conjoint associations of MVPA and sedentary behaviour on CVDR for each activity profile. The association between MVPA was greater than that of sedentary behaviour; however, for people with low MVPA, shifts in sedentary behaviour may have the greatest impact on CVDR. Activity profiles that consider the interrelationships between physical activity and sedentary behaviour differ in terms of CVDR. Future interventions may need to be tailored to specific profiles and be dynamic enough to reflect change in the profile over time. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Offset-electrode profile acquisition strategy for electrical resistivity tomography

NASA Astrophysics Data System (ADS)

Robbins, Austin R.; Plattner, Alain

2018-04-01

We present an electrode layout strategy that allows electrical resistivity profiles to image the third dimension close to the profile plane. This "offset-electrode profile" approach involves laterally displacing electrodes away from the profile line in an alternating fashion and then inverting the resulting data using three-dimensional electrical resistivity tomography software. In our synthetic and field surveys, the offset-electrode method succeeds in revealing three-dimensional structures in the vicinity of the profile plane, which we could not achieve using three-dimensional inversions of linear profiles. We confirm and explain the limits of linear electrode profiles through a discussion of the three-dimensional sensitivity patterns: For a homogeneous starting model together with a linear electrode layout, all sensitivities remain symmetric with respect to the profile plane through each inversion step. This limitation can be overcome with offset-electrode layouts by breaking the symmetry pattern among the sensitivities. Thanks to freely available powerful three-dimensional resistivity tomography software and cheap modern computing power, the requirement for full three-dimensional calculations does not create a significant burden and renders the offset-electrode approach a cost-effective method. By offsetting the electrodes in an alternating pattern, as opposed to laying the profile out in a U-shape, we minimize shortening the profile length.

Using Parametric Cost Models to Estimate Engineering and Installation Costs of Selected Electronic Communications Systems

DTIC Science & Technology

1994-09-01

Institute of Technology, Wright- Patterson AFB OH, January 1994. 4. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 5...Technology, Wright-Patterson AFB OH 5 April 1994. 29. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 30. Office of

An Evaluation of the Automated Cost Estimating Integrated Tools (ACEIT) System

DTIC Science & Technology

1989-09-01

residual and it is described as the residual divided by its standard deviation (13:App A,17). Neter, Wasserman, and Kutner, in Applied Linear Regression Models...others. Applied Linear Regression Models. Homewood IL: Irwin, 1983. 19. Raduchel, William J. "A Professional’s Perspective on User-Friendliness," Byte

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

Fitting program for linear regressions according to Mahon (1996)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trappitsch, Reto G.

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

ERIC Educational Resources Information Center

Thompson, Russel L.

Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

On the null distribution of Bayes factors in linear regression

USDA-ARS?s Scientific Manuscript database

We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR.

PubMed

Merchak, Noelle; Silvestre, Virginie; Loquet, Denis; Rizk, Toufic; Akoka, Serge; Bejjani, Joseph

2017-01-01

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13 C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13 C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13 C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13 C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13 C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information. Graphical abstract A strategy based on the multivariate linear regression of variables obtained by a rapid 13 C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

NASA Astrophysics Data System (ADS)

Wu, Cheng; Zhen Yu, Jian

2018-03-01

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Is adult gait less susceptible than paediatric gait to hip joint centre regression equation error?

PubMed

Kiernan, D; Hosking, J; O'Brien, T

2016-03-01

Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

Use of chemical indicators of beer aging for ex-post checking of storage conditions and prediction of the sensory stability of beer.

PubMed

Cejka, Pavel; Culík, Jiří; Horák, Tomáš; Jurková, Marie; Olšovská, Jana

2013-12-26

The rate of beer aging is affected by storage conditions including largely time and temperature. Although bottled beer is commonly stored for up to 1 year, sensorial damage of it is quite frequent. Therefore, a method for retrospective determination of temperature of stored beer was developed. The method is based on the determination of selected carbonyl compounds called as "aging indicators", which are formed during beer aging. The aging indicators were determined using GC-MS after precolumn derivatization with O-(2,3,4,5,6-pentaflourobenzyl)hydroxylamine hydrochloride, and their profile was correlated with the development of old flavor evolving under defined conditions (temperature, time) using both a mathematical and statistical apparatus. Three approaches, including calculation from regression graph, multiple linear regression, and neural networks, were employed. The ultimate uncertainty of the method ranged from 3.0 to 11.0 °C depending on the approach used. Furthermore, the assay was extended to include prediction of beer tendency to sensory aging from freshly bottled beer.

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

PubMed

Fitzsimmons, Eric J; Kvam, Vanessa; Souleyrette, Reginald R; Nambisan, Shashi S; Bonett, Douglas G

2013-01-01

Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

Brain responses to facial attractiveness induced by facial proportions: evidence from an fMRI study

PubMed Central

Shen, Hui; Chau, Desmond K. P.; Su, Jianpo; Zeng, Ling-Li; Jiang, Weixiong; He, Jufang; Fan, Jintu; Hu, Dewen

2016-01-01

Brain responses to facial attractiveness induced by facial proportions are investigated by using functional magnetic resonance imaging (fMRI), in 41 young adults (22 males and 19 females). The subjects underwent fMRI while they were presented with computer-generated, yet realistic face images, which had varying facial proportions, but the same neutral facial expression, baldhead and skin tone, as stimuli. Statistical parametric mapping with parametric modulation was used to explore the brain regions with the response modulated by facial attractiveness ratings (ARs). The results showed significant linear effects of the ARs in the caudate nucleus and the orbitofrontal cortex for all of the subjects, and a non-linear response profile in the right amygdala for only the male subjects. Furthermore, canonical correlation analysis was used to learn the most relevant facial ratios that were best correlated with facial attractiveness. A regression model on the fMRI-derived facial ratio components demonstrated a strong linear relationship between the visually assessed mean ARs and the predictive ARs. Overall, this study provided, for the first time, direct neurophysiologic evidence of the effects of facial ratios on facial attractiveness and suggested that there are notable gender differences in perceiving facial attractiveness as induced by facial proportions. PMID:27779211

Brain responses to facial attractiveness induced by facial proportions: evidence from an fMRI study.

PubMed

Shen, Hui; Chau, Desmond K P; Su, Jianpo; Zeng, Ling-Li; Jiang, Weixiong; He, Jufang; Fan, Jintu; Hu, Dewen

2016-10-25

Brain responses to facial attractiveness induced by facial proportions are investigated by using functional magnetic resonance imaging (fMRI), in 41 young adults (22 males and 19 females). The subjects underwent fMRI while they were presented with computer-generated, yet realistic face images, which had varying facial proportions, but the same neutral facial expression, baldhead and skin tone, as stimuli. Statistical parametric mapping with parametric modulation was used to explore the brain regions with the response modulated by facial attractiveness ratings (ARs). The results showed significant linear effects of the ARs in the caudate nucleus and the orbitofrontal cortex for all of the subjects, and a non-linear response profile in the right amygdala for only the male subjects. Furthermore, canonical correlation analysis was used to learn the most relevant facial ratios that were best correlated with facial attractiveness. A regression model on the fMRI-derived facial ratio components demonstrated a strong linear relationship between the visually assessed mean ARs and the predictive ARs. Overall, this study provided, for the first time, direct neurophysiologic evidence of the effects of facial ratios on facial attractiveness and suggested that there are notable gender differences in perceiving facial attractiveness as induced by facial proportions.

Determination and analysis of non-linear index profiles in electron-beam-deposited MgOAl2O3ZrO2 ternary composite thin-film optical coatings

NASA Astrophysics Data System (ADS)

Sahoo, N. K.; Thakur, S.; Senthilkumar, M.; Das, N. C.

2005-02-01

Thickness-dependent index non-linearity in thin films has been a thought provoking as well as intriguing topic in the field of optical coatings. The characterization and analysis of such inhomogeneous index profiles pose several degrees of challenges to thin-film researchers depending upon the availability of relevant experimental and process-monitoring-related information. In the present work, a variety of novel experimental non-linear index profiles have been observed in thin films of MgOAl2O3ZrO2 ternary composites in solid solution under various electron-beam deposition parameters. Analysis and derivation of these non-linear spectral index profiles have been carried out by an inverse-synthesis approach using a real-time optical monitoring signal and post-deposition transmittance and reflection spectra. Most of the non-linear index functions are observed to fit polynomial equations of order seven or eight very well. In this paper, the application of such a non-linear index function has also been demonstrated in designing electric-field-optimized high-damage-threshold multilayer coatings such as normal- and oblique-incidence edge filters and a broadband beam splitter for p-polarized light. Such designs can also advantageously maintain the microstructural stability of the multilayer structure due to the low stress factor of the non-linear ternary composite layers.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Leg intramuscular pressures during locomotion in humans

NASA Technical Reports Server (NTRS)

Ballard, R. E.; Watenpaugh, D. E.; Breit, G. A.; Murthy, G.; Holley, D. C.; Hargens, A. R.

1998-01-01

To assess the usefulness of intramuscular pressure (IMP) measurement for studying muscle function during gait, IMP was recorded in the soleus and tibialis anterior muscles of 10 volunteers during treadmill walking and running by using transducer-tipped catheters. Soleus IMP exhibited single peaks during late-stance phase of walking [181 +/- 69 (SE) mmHg] and running (269 +/- 95 mmHg). Tibialis anterior IMP showed a biphasic response, with the largest peak (90 +/- 15 mmHg during walking and 151 +/- 25 mmHg during running) occurring shortly after heel strike. IMP magnitude increased with gait speed in both muscles. Linear regression of soleus IMP against ankle joint torque obtained by a dynamometer produced linear relationships (n = 2, r = 0.97 for both). Application of these relationships to IMP data yielded estimated peak soleus moment contributions of 0.95-1.65 N . m/kg during walking, and 1.43-2.70 N . m/kg during running. Phasic elevations of IMP during exercise are probably generated by local muscle tissue deformations due to muscle force development. Thus profiles of IMP provide a direct, reproducible index of muscle function during locomotion in humans.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

PubMed

Torija, Antonio J; Ruiz, Diego P

2012-10-01

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%. Copyright © 2012 Elsevier B.V. All rights reserved.

Structure-function relationships using spectral-domain optical coherence tomography: comparison with scanning laser polarimetry.

PubMed

Aptel, Florent; Sayous, Romain; Fortoul, Vincent; Beccat, Sylvain; Denis, Philippe

2010-12-01

To evaluate and compare the regional relationships between visual field sensitivity and retinal nerve fiber layer (RNFL) thickness as measured by spectral-domain optical coherence tomography (OCT) and scanning laser polarimetry. Prospective cross-sectional study. One hundred and twenty eyes of 120 patients (40 with healthy eyes, 40 with suspected glaucoma, and 40 with glaucoma) were tested on Cirrus-OCT, GDx VCC, and standard automated perimetry. Raw data on RNFL thickness were extracted for 256 peripapillary sectors of 1.40625 degrees each for the OCT measurement ellipse and 64 peripapillary sectors of 5.625 degrees each for the GDx VCC measurement ellipse. Correlations between peripapillary RNFL thickness in 6 sectors and visual field sensitivity in the 6 corresponding areas were evaluated using linear and logarithmic regression analysis. Receiver operating curve areas were calculated for each instrument. With spectral-domain OCT, the correlations (r(2)) between RNFL thickness and visual field sensitivity ranged from 0.082 (nasal RNFL and corresponding visual field area, linear regression) to 0.726 (supratemporal RNFL and corresponding visual field area, logarithmic regression). By comparison, with GDx-VCC, the correlations ranged from 0.062 (temporal RNFL and corresponding visual field area, linear regression) to 0.362 (supratemporal RNFL and corresponding visual field area, logarithmic regression). In pairwise comparisons, these structure-function correlations were generally stronger with spectral-domain OCT than with GDx VCC and with logarithmic regression than with linear regression. The largest areas under the receiver operating curve were seen for OCT superior thickness (0.963 ± 0.022; P < .001) in eyes with glaucoma and for OCT average thickness (0.888 ± 0.072; P < .001) in eyes with suspected glaucoma. The structure-function relationship was significantly stronger with spectral-domain OCT than with scanning laser polarimetry, and was better expressed logarithmically than linearly. Measurements with these 2 instruments should not be considered to be interchangeable. Copyright © 2010 Elsevier Inc. All rights reserved.

A Simulation-Based Comparison of Several Stochastic Linear Regression Methods in the Presence of Outliers.

ERIC Educational Resources Information Center

Rule, David L.

Several regression methods were examined within the framework of weighted structural regression (WSR), comparing their regression weight stability and score estimation accuracy in the presence of outlier contamination. The methods compared are: (1) ordinary least squares; (2) WSR ridge regression; (3) minimum risk regression; (4) minimum risk 2;…

Hyperspectral scattering profiles for prediction of the microbial spoilage of beef

NASA Astrophysics Data System (ADS)

Peng, Yankun; Zhang, Jing; Wu, Jianhu; Hang, Hui

2009-05-01

Spoilage in beef is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. There is still no technology for the rapid, accurate and non-destructive detection of bacterially spoiled or contaminated beef. In this study, hyperspectral imaging technique was exploited to measure biochemical changes within the fresh beef. Fresh beef rump steaks were purchased from a commercial plant, and left to spoil in refrigerator at 8°C. Every 12 hours, hyperspectral scattering profiles over the spectral region between 400 nm and 1100 nm were collected directly from the sample surface in reflection pattern in order to develop an optimal model for prediction of the beef spoilage, in parallel the total viable count (TVC) per gram of beef were obtained by classical microbiological plating methods. The spectral scattering profiles at individual wavelengths were fitted accurately by a two-parameter Lorentzian distribution function. TVC prediction models were developed, using multi-linear regression, on relating individual Lorentzian parameters and their combinations at different wavelengths to log10(TVC) value. The best predictions were obtained with r2= 0.96 and SEP = 0.23 for log10(TVC). The research demonstrated that hyperspectral imaging technique is a valid tool for real-time and non-destructive detection of bacterial spoilage in beef.

In vitro dissolution method fitted to in vivo absorption profile of rivaroxaban immediate-release tablets applying in silico data.

PubMed

Wingert, Nathalie R; Dos Santos, Natália O; Campanharo, Sarah C; Simon, Elisa S; Volpato, Nadia M; Steppe, Martin

2018-05-01

This study aimed to develop and validate an in vitro dissolution method based on in silico-in vivo data to determine whether an in vitro-in vivo relationship could be established for rivaroxaban in immediate-release tablets. Oral drugs with high permeability but poorly soluble in aqueous media, such as the anticoagulant rivaroxaban, have a major potential to reach a high level of in vitro-in vivo relationship. Currently, there is no study on scientific literature approaching the development of RIV dissolution profile based on its in vivo performance. Drug plasma concentration values were modeled using computer simulation with adjustment of pharmacokinetic properties. Those values were converted into drug fractions absorbed by the Wagner-Nelson deconvolution approach. Gradual and continuous dissolution of RIV tablets was obtained with a 30 rpm basket on 50 mM sodium acetate +0.2% SDS, pH 6.5 medium. Dissolution was conducted for up to 180 min. The fraction absorbed was plotted against the drug fraction dissolved, and a linear point-to-point regression (R 2  = 0.9961) obtained. The in vitro dissolution method designed promoted a more convenient dissolution profile of RIV tablets, whereas it suggests a better relationship with in vivo performance.

Fatty Acid Diversity is Not Associated with Neutral Genetic Diversity in Native Populations of the Biodiesel Plant Jatropha curcas L.

PubMed

Martínez-Díaz, Yesenia; González-Rodríguez, Antonio; Rico-Ponce, Héctor Rómulo; Rocha-Ramírez, Víctor; Ovando-Medina, Isidro; Espinosa-García, Francisco J

2017-01-01

Jatropha curcas L. (Euphorbiaceae) is a shrub native to Mexico and Central America, which produces seeds with a high oil content that can be converted to biodiesel. The genetic diversity of this plant has been widely studied, but it is not known whether the diversity of the seed oil chemical composition correlates with neutral genetic diversity. The total seed oil content, the diversity of profiles of fatty acids and phorbol esters were quantified, also, the genetic diversity obtained from simple sequence repeats was analyzed in native populations of J. curcas in Mexico. Using the fatty acids profiles, a discriminant analysis recognized three groups of individuals according to geographical origin. Bayesian assignment analysis revealed two genetic groups, while the genetic structure of the populations could not be explained by isolation-by-distance. Genetic and fatty acid profile data were not correlated based on Mantel test. Also, phorbol ester content and genetic diversity were not associated. Multiple linear regression analysis showed that total oil content was associated with altitude and seasonality of temperature. The content of unsaturated fatty acids was associated with altitude. Therefore, the cultivation planning of J. curcas should take into account chemical variation related to environmental factors. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Unit Cohesion and the Surface Navy: Does Cohesion Affect Performance

DTIC Science & Technology

1989-12-01

v. 68, 1968. Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. Rand Corporation R-2607...Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. SAS User’s Guide: Basics, Version 5 ed

Comparison of Selection Procedures and Validation of Criterion Used in Selection of Significant Control Variates of a Simulation Model

DTIC Science & Technology

1990-03-01

and M.H. Knuter. Applied Linear Regression Models. Homewood IL: Richard D. Erwin Inc., 1983. Pritsker, A. Alan B. Introduction to Simulation and SLAM...Control Variates in Simulation," European Journal of Operational Research, 42: (1989). Neter, J., W. Wasserman, and M.H. Xnuter. Applied Linear Regression Models

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course

ERIC Educational Resources Information Center

Enders, Felicity B.; Jenkins, Sarah; Hoverman, Verna

2010-01-01

Biostatistics is traditionally a difficult subject for students to learn. While the mathematical aspects are challenging, it can also be demanding for students to learn the exact language to use to correctly interpret statistical results. In particular, correctly interpreting the parameters from linear regression is both a vital tool and a…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

Using Simple Linear Regression to Assess the Success of the Montreal Protocol in Reducing Atmospheric Chlorofluorocarbons

ERIC Educational Resources Information Center

Nelson, Dean

2009-01-01

Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can…

Quantum State Tomography via Linear Regression Estimation

PubMed Central

Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

2013-01-01

A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Skin microrelief profiles as a cutaneous aging index.

PubMed

Kim, Dai Hyun; Rhyu, Yeon Seung; Ahn, Hyo Hyun; Hwang, Eenjun; Uhm, Chang Sub

2016-10-01

An objective measurement of cutaneous topographical information is important for quantifying the degree of skin aging. Our aim was to improve methods for measuring microrelief patterns using a three-dimensional analysis based on silicone replicas and scanning electron microscope (SEM). Another objective was to compare the results with those obtained using a two-dimensional analysis method based on dermoscopy. Silicone replicas were obtained from forearms, dorsum of the hands and fingers of 51 volunteers. Cutaneous profiles obtained by SEM with silicone replicas showed more consistent correlations with age than data obtained by dermoscopy. This indicates the advantage of three-dimensional topography analysis using silicone replicas and SEM over the widely used dermoscopic assessment. The cutaneous age was calculated using stepwise linear regression, and the result was 57.40-9.47 × (number of furrows on dorsum of the hand) × (width of furrows on dorsum of the hand). © The Author 2016. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Separation mechanism of nortriptyline and amytriptyline in RPLC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gritti, Fabrice; Guiochon, Georges A

2005-08-01

The single and the competitive equilibrium isotherms of nortriptyline and amytriptyline were acquired by frontal analysis (FA) on the C{sub 18}-bonded discovery column, using a 28/72 (v/v) mixture of acetonitrile and water buffered with phosphate (20 mM, pH 2.70). The adsorption energy distributions (AED) of each compound were calculated from the raw adsorption data. Both the fitting of the adsorption data using multi-linear regression analysis and the AEDs are consistent with a trimodal isotherm model. The single-component isotherm data fit well to the tri-Langmuir isotherm model. The extension to a competitive two-component tri-Langmuir isotherm model based on the best parametersmore » of the single-component isotherms does not account well for the breakthrough curves nor for the overloaded band profiles measured for mixtures of nortriptyline and amytriptyline. However, it was possible to derive adjusted parameters of a competitive tri-Langmuir model based on the fitting of the adsorption data obtained for these mixtures. A very good agreement was then found between the calculated and the experimental overloaded band profiles of all the mixtures injected.« less

Influence of operator experience on canal preparation time when using the rotary Ni-Ti ProFile system in simulated curved canals.

PubMed

Mesgouez, C; Rilliard, F; Matossian, L; Nassiri, K; Mandel, E

2003-03-01

The aim of this study was to determine the influence of operator experience on the time needed for canal preparation when using a rotary nickel-titanium (Ni-Ti) system. A total of 100 simulated curved canals in resin blocks were used. Four operators prepared a total of 25 canals each. The operators included practitioners with prior experience of the preparation technique, and practitioners with no experience. The working length for each instrument was precisely predetermined. All canals were instrumented with rotary Ni-Ti ProFile Variable Taper Series 29 engine-driven instruments using a high-torque handpiece (Maillefer, Ballaigues, Switzerland). The time taken to prepare each canal was recorded. Significant differences between the operators were analysed using the Student's t-test and the Kruskall-Wallis and Dunn nonparametric tests. Comparison of canal preparation times demonstrated a statistically significant difference between the four operators (P < 0.001). In the inexperienced group, a significant linear regression between canal number and preparation time occurred. Time required for canal preparation was inversely related to operator experience.

New Observations of Molecular Nitrogen by the Imaging Ultraviolet Spectrograph on MAVEN

NASA Astrophysics Data System (ADS)

Stevens, Michael H.; Evans, J. S.; Schneider, Nicholas M.; Stewart, A. I. F.; Deighan, Justin; Jain, Sonal K.; Crismani, Matteo M. J.; Stiepen, Arnaud; Chaffin, Michael S.; McClintock, William E.; Holsclaw, Greg M.; Lefevre, Franck; Montmessin, Franck; Lo, Daniel Y.; Clarke, John T.; Bougher, Stephen W.; Jakosky, Bruce M.

2015-11-01

The Martian ultraviolet dayglow provides information on the basic state of the Martian upper atmosphere. The Imaging Ultraviolet Spectrograph (IUVS) on NASA’s Mars Atmosphere and Volatile Evolution (MAVEN) mission has observed Mars at mid and far-UV wavelengths since its arrival in September 2014. In this work, we describe a linear regression method used to extract components of UV spectra from IUVS limb observations and focus in particular on molecular nitrogen (N2) photoelectron excited emissions. We identify N2 Lyman-Birge-Hopfield (LBH) emissions for the first time at Mars and we also confirm the tentative identification of N2 Vegard-Kaplan (VK) emissions. We compare observed VK and LBH limb radiance profiles to model results between 90 and 210 km. Finally, we compare retrieved N2 density profiles to general circulation (GCM) model results. Contrary to earlier analyses using other satellite data that indicated N2 densities were a factor of three less than predictions, we find that N2 abundances exceed GCM results by about a factor of two at 130 km but are in agreement at 150 km.

Private traits and attributes are predictable from digital records of human behavior

PubMed Central

Kosinski, Michal; Stillwell, David; Graepel, Thore

2013-01-01

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy. PMID:23479631

Atmospheric, Cloud, and Surface Parameters Retrieved from Satellite Ultra-spectral Infrared Sounder Measurements

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Liu, Xu; Larar, Allen M.; Smith, William L.; Yang, Ping; Schluessel, Peter; Strow, Larrabee

2007-01-01

An advanced retrieval algorithm with a fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. This physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. This retrieval algorithm is applied to the MetOp satellite Infrared Atmospheric Sounding Interferometer (IASI) launched on October 19, 2006. IASI possesses an ultra-spectral resolution of 0.25 cm(exp -1) and a spectral coverage from 645 to 2760 cm(exp -1). Preliminary retrievals of atmospheric soundings, surface properties, and cloud optical/microphysical properties with the IASI measurements are obtained and presented.

Exploring Learners’ Mental Health Profile: A study in Universiti Tun Hussein Onn Malaysia

NASA Astrophysics Data System (ADS)

Lee, M. F.; Lai, C. S.

2017-08-01

Mental health issue was a serious matter that was often neglected by people. This article will describe a study of the mental health profile among the learners of Malaysia Technical University (MTU) that focus on Universiti Tun Hussein Onn Malaysia (UTHM). A survey using DASS-21 inventory and self-developed questionnaire was used for this study to investigate learners’ mental health level in three elements and factors contribute towards mental health. A total number of 450 students from seven faculties in UTHM was strata randomly selected as sampel for this study. The relationships between factors of mental health and the elements of mental health was identified. Collected data was analysed using percentage, mean score, standard deviation and multiple linear regression. Findings showed that majority of students possess normal level but the percentage of severe and extremely severe level was increasing. The main factor highly significantly correlate to all the mental health elements was self-evaluation. Hence, it is highly recommended that mental health issue needs great attention and remedial action from higher learning institution, non-governmental organizations, parents, students themselves and other concerned bodies.

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Regression of non-linear coupling of noise in LIGO detectors

NASA Astrophysics Data System (ADS)

Da Silva Costa, C. F.; Billman, C.; Effler, A.; Klimenko, S.; Cheng, H.-P.

2018-03-01

In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener–Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

The associations between religion, bereavement and depression among Hong Kong nurses.

PubMed

Cheung, Teris; Lee, Paul H; Yip, Paul S F

2017-07-04

This paper is to examine the associations between religion, bereavement and depression among nursing professionals using a cross-sectional survey design. There is little empirical evidence in Asia suggesting that religion may either increase or lower the likelihood of nursing professionals being depressed. We analyzed the results of a Mental Health Survey soliciting data from 850 Hong Kong nurses (aged 21-59, 178 males) regarding their mental well-being and associated factors, including participants' socio-economic profile and recent life-events. Multiple linear regression analyses examined associations between religion, bereavement and depression. Religious faith is weakly associated with lower self-reported depression in bereavement. Our findings confirm those studies suggesting that religion positively affects mental health and yet healthcare providers have yet to assimilate this insight.

Determinants of health-promoting lifestyle behaviors among Arab immigrants from the region of the Levant.

PubMed

Aqtash, Salah; Van Servellen, Gwen

2013-10-01

Arab immigrants in the United States are at risk for heart disease, stroke, and diabetes. We explored health-promoting lifestyle behaviors among Arab immigrants to the United States from the Middle Eastern region of the Levant. In 218 male and female Arab adults surveyed with the revised Health-Promoting Lifestyle Profile (HPLP-II), the mean for the HPLP-II was 2.73 (range 1-4), with spiritual growth and interpersonal relations the most frequently reported practices and physical activity the least frequently practiced dimension of health-promoting behaviors. Multiple linear regression analysis highlighted four determinants of health-promoting lifestyle behaviors: health insurance, acculturation, self-efficacy, and social support. Health promotion programs serving Arab immigrants should take these determinants into consideration. © 2013 Wiley Periodicals, Inc.

Correlation between the transdermal characteristics of pseudoephedrine and amygdalin in majiepingchuan in vitro.

PubMed

Kong, Hui; Qu, Huihua; Qu, Baoping; Zeng, Wenhao; Zhao, Yan; Wang, Xueqian; Wang, Qingguo

2016-04-01

To analyze the transdermal profile of pseudoephedrine and amygdalin in the Traditional Chinese Medicine majiepingchuan in rat skin and to reveal their interaction. A Franz diffusion cell was used in vitro to evaluate the transdermal parameters of cumulative transdermal flux (Q(tot)), cumulative transmission (T(tot)), and mean penetration rate (Kp) of pseudoephedrine and amygdalin in majiepingchuan. Linear regression analyses of Q(tot) over time of pseudoephedrine vs amygdalin and their ratios was adopted for correlation evaluation. At 1, 2, 4, 6, and 8 h, the Q(tot), T(tot) and Kp of pseudoephedrine showed a good correlation with that of amygdalin. There was a small difference in the ratios of Q(tot), T(tot) and Kp between pseudoephedrine and amygdalin, and a correlation between them.

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Estimating Volume, Biomass, and Carbon in Hedmark County, Norway Using a Profiling LiDAR

NASA Technical Reports Server (NTRS)

Nelson, Ross; Naesset, Erik; Gobakken, T.; Gregoire, T.; Stahl, G.

2009-01-01

A profiling airborne LiDAR is used to estimate the forest resources of Hedmark County, Norway, a 27390 square kilometer area in southeastern Norway on the Swedish border. One hundred five profiling flight lines totaling 9166 km were flown over the entire county; east-west. The lines, spaced 3 km apart north-south, duplicate the systematic pattern of the Norwegian Forest Inventory (NFI) ground plot arrangement, enabling the profiler to transit 1290 circular, 250 square meter fixed-area NFI ground plots while collecting the systematic LiDAR sample. Seven hundred sixty-three plots of the 1290 plots were overflown within 17.8 m of plot center. Laser measurements of canopy height and crown density are extracted along fixed-length, 17.8 m segments closest to the center of the ground plot and related to basal area, timber volume and above- and belowground dry biomass. Linear, nonstratified equations that estimate ground-measured total aboveground dry biomass report an R(sup 2) = 0.63, with an regression RMSE = 35.2 t/ha. Nonstratified model results for the other biomass components, volume, and basal area are similar, with R(sup 2) values for all models ranging from 0.58 (belowground biomass, RMSE = 8.6 t/ha) to 0.63. Consistently, the most useful single profiling LiDAR variable is quadratic mean canopy height, h (sup bar)(sub qa). Two-variable models typically include h (sup bar)(sub qa) or mean canopy height, h(sup bar)(sub a), with a canopy density or a canopy height standard deviation measure. Stratification by productivity class did not improve the nonstratified models, nor did stratification by pine/spruce/hardwood. County-wide profiling LiDAR estimates are reported, by land cover type, and compared to NFI estimates.

Gene expression inference with deep learning.

PubMed

Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-06-15

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Gene expression inference with deep learning

PubMed Central

Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-01-01

Motivation: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. Results: We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. Availability and implementation: D-GEX is available at https://github.com/uci-cbcl/D-GEX. Contact: xhx@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26873929

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Adding a Parameter Increases the Variance of an Estimated Regression Function

ERIC Educational Resources Information Center

Withers, Christopher S.; Nadarajah, Saralees

2011-01-01

The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression…

Using nonlinear quantile regression to estimate the self-thinning boundary curve

Treesearch

Quang V. Cao; Thomas J. Dean

2015-01-01

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed...

Simultaneous spectrophotometric determination of salbutamol and bromhexine in tablets.

PubMed

Habib, I H I; Hassouna, M E M; Zaki, G A

2005-03-01

Typical anti-mucolytic drugs called salbutamol hydrochloride and bromhexine sulfate encountered in tablets were determined simultaneously either by using linear regression at zero-crossing wavelengths of the first derivation of UV-spectra or by application of multiple linear partial least squares regression method. The results obtained by the two proposed mathematical methods were compared with those obtained by the HPLC technique.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Image interpolation via regularized local linear regression.

PubMed

Liu, Xianming; Zhao, Debin; Xiong, Ruiqin; Ma, Siwei; Gao, Wen; Sun, Huifang

2011-12-01

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l(2)-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation. © 2011 IEEE

Effect of Coannular Flow on Linearized Euler Equation Predictions of Jet Noise

NASA Technical Reports Server (NTRS)

Hixon, R.; Shih, S.-H.; Mankbadi, Reda R.

1997-01-01

An improved version of a previously validated linearized Euler equation solver is used to compute the noise generated by coannular supersonic jets. Results for a single supersonic jet are compared to the results from both a normal velocity profile and an inverted velocity profile supersonic jet.

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

PubMed Central

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

PubMed

Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Profiles of internalizing and externalizing symptoms associated with bullying victimization.

PubMed

Eastman, Meridith; Foshee, Vangie; Ennett, Susan; Sotres-Alvarez, Daniela; Reyes, H Luz McNaughton; Faris, Robert; North, Kari

2018-06-01

This study identified profiles of internalizing (anxiety and depression) and externalizing (delinquency and violence against peers) symptoms among bullying victims and examined associations between bullying victimization characteristics and profile membership. The sample consisted of 1196 bullying victims in grades 8-10 (M age  = 14.4, SD = 1.01) who participated in The Context Study in three North Carolina counties in Fall 2003. Five profiles were identified using latent profile analysis: an asymptomatic profile and four profiles capturing combinations of internalizing and externalizing symptoms. Associations between bullying characteristics and membership in symptom profiles were tested using multinomial logistic regression. More frequent victimization increased odds of membership in the two high internalizing profiles compared to the asymptomatic profile. Across all multinomial logistic regression models, when the high internalizing, high externalizing profile was the reference category, adolescents who received any type of bullying (direct, indirect, or dual) were more likely to be in this category than any others. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Solving a mixture of many random linear equations by tensor decomposition and alternating minimization.

DOT National Transportation Integrated Search

2016-09-01

We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample...

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

Variation in sensitivity, absorption and density of the central rod distribution with eccentricity.

PubMed

Tornow, R P; Stilling, R

1998-01-01

To assess the human rod photopigment distribution and sensitivity with high spatial resolution within the central +/-15 degrees and to compare the results of pigment absorption, sensitivity and rod density distribution (number of rods per square degree). Rod photopigment density distribution was measured with imaging densitometry using a modified Rodenstock scanning laser ophthalmoscope. Dark-adapted sensitivity profiles were measured with green stimuli (17' arc diameter, 1 degrees spacing) using a T ubingen manual perimeter. Sensitivity profiles were plotted on a linear scale and rod photopigment optical density distribution profiles were converted to absorption profiles of the rod photopigment layer. Both the absorption profile of the rod photopigment and the linear sensitivity profile for green stimuli show a minimum at the foveal center and increase steeply with eccentricity. The variation with eccentricity corresponds to the rod density distribution. Rod photopigment absorption profiles, retinal sensitivity profiles, and the rod density distribution are linearly related within the central +/-15 degrees. This is in agreement with theoretical considerations. Both methods, imaging retinal densitometry using a scanning laser ophthalmoscope and dark-adapted perimetry with small green stimuli, are useful for assessing the central rod distribution and sensitivity. However, at present, both methods have limitations. Suggestions for improving the reliability of both methods are given.

A Note on the Relationship between Temperature and Water Vapor in Quasi-Equilibrium and Climate States

NASA Technical Reports Server (NTRS)

Shie, C.-L.; Shie, C.-L.; Tao, W.-K.; Simpson, J.; Sui, C.-H.

2005-01-01

An ideal and simple formulation is successfully derived that well represents a quasi-linear relationship found between the domain-averaged water vapor, q (mm), and temperature, T (K), fields obtained from a series of quasi-equilibrium (long-term) simulations for the Tropics using the two-dimensional Goddard Cumulus Ensemble (GCE) model. Earlier model work showed that the forced maintenance of two different wind profiles in the Tropics leads to two different equilibrium states. Investigating this finding required investigation of the slope of the moisture-temperature relations, which turns out to be linear in the Tropics. The extra-tropical climate equilibriums become more complex, but insight on modeling sensitivity can be obtained by linear stepwise regression of the integrated temperature and humidity. A globally curvilinear moisture-temperature distribution, similar to the famous Clausius-Clapeyron curve (i.e., saturated water vapor pressure versus temperature), is then found in this study. Such a genuine finding clarifies that the dynamics are crucial to the climate (shown in the earlier work) but the thermodynamics adjust. The range of validity of this result is further examined herein. The GCE-modeled tropical domain-averaged q and T fields form a linearly-regressed "q-T" slope that genuinely resides within an ideal range of slopes obtained from the aforementioned formulation. A quantity (denoted as dC2/dC1) representing the derivative between the static energy densities due to temperature (C2) and water vapor (C1) for various quasi-equilibrium states can also be obtained. A dC2/dC1 value near unity obtained for the GCE-modeled tropical simulations implies that the static energy densities due to moisture and temperature only differ by a pure constant for various equilibrium states. An overall q-T relation also including extra-tropical regions is, however, found to have a curvilinear relationship. Accordingly, warm/moist regions favor change in water vapor faster than temperature, while cold/dry regions favor an increase in temperature quicker than water vapor.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure.

PubMed

Senn, Stephen; Graf, Erika; Caputo, Angelika

2007-12-30

Stratifying and matching by the propensity score are increasingly popular approaches to deal with confounding in medical studies investigating effects of a treatment or exposure. A more traditional alternative technique is the direct adjustment for confounding in regression models. This paper discusses fundamental differences between the two approaches, with a focus on linear regression and propensity score stratification, and identifies points to be considered for an adequate comparison. The treatment estimators are examined for unbiasedness and efficiency. This is illustrated in an application to real data and supplemented by an investigation on properties of the estimators for a range of underlying linear models. We demonstrate that in specific circumstances the propensity score estimator is identical to the effect estimated from a full linear model, even if it is built on coarser covariate strata than the linear model. As a consequence the coarsening property of the propensity score-adjustment for a one-dimensional confounder instead of a high-dimensional covariate-may be viewed as a way to implement a pre-specified, richly parametrized linear model. We conclude that the propensity score estimator inherits the potential for overfitting and that care should be taken to restrict covariates to those relevant for outcome. Copyright (c) 2007 John Wiley & Sons, Ltd.

On the Relation between the Linear Factor Model and the Latent Profile Model

ERIC Educational Resources Information Center

Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

2011-01-01

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.

PubMed

Trninić, Marko; Jeličić, Mario; Papić, Vladan

2015-07-01

In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

PubMed

O'Leary, Neil; Chauhan, Balwantray C; Artes, Paul H

2012-10-01

To establish a method for estimating the overall statistical significance of visual field deterioration from an individual patient's data, and to compare its performance to pointwise linear regression. The Truncated Product Method was used to calculate a statistic S that combines evidence of deterioration from individual test locations in the visual field. The overall statistical significance (P value) of visual field deterioration was inferred by comparing S with its permutation distribution, derived from repeated reordering of the visual field series. Permutation of pointwise linear regression (PoPLR) and pointwise linear regression were evaluated in data from patients with glaucoma (944 eyes, median mean deviation -2.9 dB, interquartile range: -6.3, -1.2 dB) followed for more than 4 years (median 10 examinations over 8 years). False-positive rates were estimated from randomly reordered series of this dataset, and hit rates (proportion of eyes with significant deterioration) were estimated from the original series. The false-positive rates of PoPLR were indistinguishable from the corresponding nominal significance levels and were independent of baseline visual field damage and length of follow-up. At P < 0.05, the hit rates of PoPLR were 12, 29, and 42%, at the fifth, eighth, and final examinations, respectively, and at matching specificities they were consistently higher than those of pointwise linear regression. In contrast to population-based progression analyses, PoPLR provides a continuous estimate of statistical significance for visual field deterioration individualized to a particular patient's data. This allows close control over specificity, essential for monitoring patients in clinical practice and in clinical trials.

Factors associated to quality of life in active elderly.

PubMed

Alexandre, Tiago da Silva; Cordeiro, Renata Cereda; Ramos, Luiz Roberto

2009-08-01

To analyze whether quality of life in active, healthy elderly individuals is influenced by functional status and sociodemographic characteristics, as well as psychological parameters. Study conducted in a sample of 120 active elderly subjects recruited from two open universities of the third age in the cities of São Paulo and São José dos Campos (Southeastern Brazil) between May 2005 and April 2006. Quality of life was measured using the abbreviated Brazilian version of the World Health Organization Quality of Live (WHOQOL-bref) questionnaire. Sociodemographic, clinical and functional variables were measured through crossculturally validated assessments by the Mini Mental State Examination, Geriatric Depression Scale, Functional Reach, One-Leg Balance Test, Timed Up and Go Test, Six-Minute Walk Test, Human Activity Profile and a complementary questionnaire. Simple descriptive analyses, Pearson's correlation coefficient, Student's t-test for non-related samples, analyses of variance, linear regression analyses and variance inflation factor were performed. The significance level for all statistical tests was set at 0.05. Linear regression analysis showed an independent correlation without colinearity between depressive symptoms measured by the Geriatric Depression Scale and four domains of the WHOQOL-bref. Not having a conjugal life implied greater perception in the social domain; developing leisure activities and having an income over five minimum wages implied greater perception in the environment domain. Functional status had no influence on the Quality of Life variable in the analysis models in active elderly. In contrast, psychological factors, as assessed by the Geriatric Depression Scale, and sociodemographic characteristics, such as marital status, income and leisure activities, had an impact on quality of life.

Uremic Pruritus, Dialysis Adequacy, and Metabolic Profiles in Hemodialysis Patients: A Prospective 5-Year Cohort Study

PubMed Central

Chen, Hung-Yuan; Chiu, Yen-Ling; Hsu, Shih-Ping; Pai, Mei-Fen; Ju-YehYang; Lai, Chun-Fu; Lu, Hui-Min; Huang, Shu-Chen; Yang, Shao-Yu; Wen, Su-Yin; Chiu, Hsien-Ching; Hu, Fu-Chang; Peng, Yu-Sen; Jee, Shiou-Hwa

2013-01-01

Background Uremic pruritus is a common and intractable symptom in patients on chronic hemodialysis, but factors associated with the severity of pruritus remain unclear. This study aimed to explore the associations of metabolic factors and dialysis adequacy with the aggravation of pruritus. Methods We conducted a 5-year prospective cohort study on patients with maintenance hemodialysis. A visual analogue scale (VAS) was used to assess the intensity of pruritus. Patient demographic and clinical characteristics, laboratory parameters, dialysis adequacy (assessed by Kt/V), and pruritus intensity were recorded at baseline and follow-up. Change score analysis of the difference score of VAS between baseline and follow-up was performed using multiple linear regression models. The optimal threshold of Kt/V, which is associated with the aggravation of uremic pruritus, was determined by generalized additive models and receiver operating characteristic analysis. Results A total of 111 patients completed the study. Linear regression analysis showed that lower Kt/V and use of low-flux dialyzer were significantly associated with the aggravation of pruritus after adjusting for the baseline pruritus intensity and a variety of confounding factors. The optimal threshold value of Kt/V for pruritus was 1.5 suggested by both generalized additive models and receiver operating characteristic analysis. Conclusions Hemodialysis with the target of Kt/V ≥1.5 and use of high-flux dialyzer may reduce the intensity of pruritus in patients on chronic hemodialysis. Further clinical trials are required to determine the optimal dialysis dose and regimen for uremic pruritus. PMID:23940749

Health Related Quality of Life and its Predictors among Bengali Thalassemic Children Admitted to a Tertiary Care Hospital.

PubMed

Saha, Rajib; Misra, Raghunath; Saha, Indranil

2015-10-01

To assess the quality of life among thalassemic children and to find out association of quality of life (QOL) with the socio-demographic factors, and clinico-therapeutic profile. This cross sectional descriptive epidemiological study was conducted from July 2011 through June 2012 on 365 admitted thalassemic patients of 5 to 12 y of age in the Burdwan Medical College and Hospital. Parents of the children were interviewed using Paediatric Quality of Life Inventory 4.0 Generic Core Scale. Statistically significant variables in bivariate analysis were considered for correlation matrix where independent variables were found inter related. So, partial correlation was done and statistically significant variables in partial correlation were considered for linear regression. The mean age of 365 thalassemic children was 8.3 ± 2.4 y. Multiple linear regressions predicted that only 70.5 % variation of total summary score depended on duration since splenectomy (31.2 % variation), last pre transfusion Hb level (20.7 %), family history of thalassemia (17.3 %) and frequency of blood transfusions (1.3 %). After splenectomy, thalassemic children could lead a better quality of life upto 5 y only. The betterment of the quality of life needs maintaining pre transfusion Hb level above 7 g/dl. Previous experience of the disease among the family members enriches the awareness among them and helps them to take correct decisions timely about the child and that leads to better QOL. More awareness regarding the maintenance of pre transfusion Hb level should be built up among parents and families where such disease has occurred for the first time.

Estimation of stature from sternum - Exploring the quadratic models.

PubMed

Saraf, Ashish; Kanchan, Tanuj; Krishan, Kewal; Ateriya, Navneet; Setia, Puneet

2018-04-14

Identification of the dead is significant in examination of unknown, decomposed and mutilated human remains. Establishing the biological profile is the central issue in such a scenario, and stature estimation remains one of the important criteria in this regard. The present study was undertaken to estimate stature from different parts of the sternum. A sample of 100 sterna was obtained from individuals during the medicolegal autopsies. Length of the deceased and various measurements of the sternum were measured. Student's t-test was performed to find the sex differences in stature and sternal measurements included in the study. Correlation between stature and sternal measurements were analysed using Karl Pearson's correlation, and linear and quadratic regression models were derived. All the measurements were found to be significantly larger in males than females. Stature correlated best with the combined length of sternum, among males (R = 0.894), females (R = 0.859), and for the total sample (R = 0.891). The study showed that the models derived for stature estimation from combined length of sternum are likely to give the most accurate estimates of stature in forensic case work when compared to manubrium and mesosternum. Accuracy of stature estimation further increased with quadratic models derived for the mesosternum among males and combined length of sternum among males and females when compared to linear regression models. Future studies in different geographical locations and a larger sample size are proposed to confirm the study observations. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Influence of specific obsessive-compulsive symptom dimensions on strategic planning in patients with obsessive-compulsive disorder.

PubMed

Pinto, Paula Sanders Pereira; Iego, Sandro; Nunes, Samantha; Menezes, Hemanny; Mastrorosa, Rosana Sávio; Oliveira, Irismar Reis de; Rosário, Maria Conceição do

2011-03-01

This study investigates obsessive-compulsive disorder patients in terms of strategic planning and its association with specific obsessive-compulsive symptom dimensions. We evaluated 32 obsessive-compulsive disorder patients. Strategic planning was assessed by the Rey-Osterrieth Complex Figure Test, and the obsessive-compulsive dimensions were assessed by the Dimensional Yale-Brown Obsessive-Compulsive Scale. In the statistical analyses, the level of significance was set at 5%. We employed linear regression, including age, intelligence quotient, number of comorbidities, the Yale-Brown Obsessive-Compulsive Scale score, and the Dimensional Yale-Brown Obsessive-Compulsive Scale. The Dimensional Yale-Brown Obsessive-Compulsive Scale "worst-ever" score correlated significantly with the planning score on the copy portion of the Rey-Osterrieth Complex Figure Test (r = 0.4, p = 0.04) and was the only variable to show a significant association after linear regression (β = 0.55, t = 2.1, p = 0.04). Compulsive hoarding correlated positively with strategic planning (r = 0.44, p = 0.03). None of the remaining symptom dimensions presented any significant correlations with strategic planning. We found the severity of obsessive-compulsive symptoms to be associated with strategic planning. In addition, there was a significant positive association between the planning score on the copy portion of the Rey-Osterrieth Complex Figure Test copy score and the hoarding dimension score on the Dimensional Yale-Brown Obsessive-Compulsive Scale. Our results underscore the idea that obsessive-compulsive disorder is a heterogeneous disorder and suggest that the hoarding dimension has a specific neuropsychological profile. Therefore, it is important to assess the peculiarities of each obsessive-compulsive symptom dimension.

A Model Comparison for Count Data with a Positively Skewed Distribution with an Application to the Number of University Mathematics Courses Completed

ERIC Educational Resources Information Center

Liou, Pey-Yan

2009-01-01

The current study examines three regression models: OLS (ordinary least square) linear regression, Poisson regression, and negative binomial regression for analyzing count data. Simulation results show that the OLS regression model performed better than the others, since it did not produce more false statistically significant relationships than…

Determinants of medication adherence in older people with dementia from the caregivers' perspective.

PubMed

El-Saifi, Najwan; Moyle, Wendy; Jones, Cindy; Alston-Knox, Clair

2018-05-11

ABSTRACTBackground:Adherence to treatment is a primary determinant of treatment success. Caregiver support can influence medication adherence in people with cognitive impairment. This study sought to characterize medication adherence in older people with dementia from the caregivers' perspective, and to identify influencing factors. Caregivers caring for a person with dementia and living in the community were eligible to complete the survey. Bayesian profile regression was applied to identify determinants of medication adherence measured using the Adherence to Refills and Medication Scale. Out of the 320 caregivers who participated in the survey, Bayesian profile regression on 221 participants identified two groups: Profile 1 (55 caregivers) with a mean adherence rate of 0.69 (80% Credible Interval (CrI): 0.61-0.77), and Profile 2 (166 caregivers) with a mean adherence rate of 0.80 (80% CrI: 0.77-0.84). Caregivers in Profile 1 were characterized with below data average scores for the following: cognitive functioning, commitment or intention, self-efficacy, and health knowledge, which were all above the data average in Profile 2, except for health knowledge. Caregivers in Profile 1 had a greater proportion of care recipients taking more than five medications and with late-stage dementia. Trade, technical, or vocational training was more common among the caregivers in Profile 1. Profile 2 caregivers had a better patient-provider relationship and less medical problems. Bayesian profile regression was useful in understanding caregiver factors that influence medication adherence. Tailored interventions to the determinants of medication adherence can guide the development of evidence-based interventions.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

NASA Astrophysics Data System (ADS)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Serum osteocalcin is significantly related to indices of obesity and lipid profile in Malaysian men.

PubMed

Chin, Kok-Yong; Ima-Nirwana, Soelaiman; Mohamed, Isa Naina; Ahmad, Fairus; Ramli, Elvy Suhana Mohd; Aminuddin, Amilia; Ngah, Wan Zurinah Wan

2014-01-01

Recent studies revealed a possible reciprocal relationship between the skeletal system and obesity and lipid metabolism, mediated by osteocalcin, an osteoblast-specific protein. This study aimed to validate the relationship between serum osteocalcin and indices of obesity and lipid parameters in a group of Malaysian men. A total of 373 men from the Malaysian Aging Male Study were included in the analysis. Data on subjects' demography, body mass index (BMI), body fat (BF) mass, waist circumference (WC), serum osteocalcin and fasting lipid levels were collected. Bioelectrical impendence (BIA) method was used to estimate BF. Multiple linear and binary logistic regression analyses were performed to analyze the association between serum osteocalcin and the aforementioned variables, with adjustment for age, ethnicity and BMI. Multiple regression results indicated that weight, BMI, BF mass, BF %, WC were significantly and negatively associated with serum osteocalcin (p < 0.001). There was a significant positive association between serum osteocalcin and high density lipoprotein (HDL) cholesterol (p = 0.032). Binary logistic results indicated that subjects with low serum osteocalcin level were more likely to be associated with high BMI (obese and overweight), high BF%, high WC and low HDL cholesterol (p < 0.05). Subjects with high osteocalcin level also demonstrated high total cholesterol level (p < 0.05) but this association was probably driven by high HDL level. These variables were not associated with serum C-terminal of telopeptide crosslinks in the subjects (p > 0.05). Serum osteocalcin is associated with indices of obesity and HDL level in men. These relationships should be validated by a longitudinal study, with comprehensive hormone profile testing.

On the aliasing of the solar cycle in the lower stratospheric tropical temperature

NASA Astrophysics Data System (ADS)

Kuchar, Ales; Ball, William T.; Rozanov, Eugene V.; Stenke, Andrea; Revell, Laura; Miksovsky, Jiri; Pisoft, Petr; Peter, Thomas

2017-09-01

The double-peaked response of the tropical stratospheric temperature profile to the 11 year solar cycle (SC) has been well documented. However, there are concerns about the origin of the lower peak due to potential aliasing with volcanic eruptions or the El Niño-Southern Oscillation (ENSO) detected using multiple linear regression analysis. We confirm the aliasing using the results of the chemistry-climate model (CCM) SOCOLv3 obtained in the framework of the International Global Atmospheric Chemisty/Stratosphere-troposphere Processes And their Role in Climate Chemistry-Climate Model Initiative phase 1. We further show that even without major volcanic eruptions included in transient simulations, the lower stratospheric response exhibits a residual peak when historical sea surface temperatures (SSTs)/sea ice coverage (SIC) are used. Only the use of climatological SSTs/SICs in addition to background stratospheric aerosols removes volcanic and ENSO signals and results in an almost complete disappearance of the modeled solar signal in the lower stratospheric temperature. We demonstrate that the choice of temporal subperiod considered for the regression analysis has a large impact on the estimated profile signal in the lower stratosphere: at least 45 consecutive years are needed to avoid the large aliasing effect of SC maxima with volcanic eruptions in 1982 and 1991 in historical simulations, reanalyses, and observations. The application of volcanic forcing compiled for phase 6 of the Coupled Model Intercomparison Project (CMIP6) in the CCM SOCOLv3 reduces the warming overestimation in the tropical lower stratosphere and the volcanic aliasing of the temperature response to the SC, although it does not eliminate it completely.

Orthogonal Projection in Teaching Regression and Financial Mathematics

ERIC Educational Resources Information Center

Kachapova, Farida; Kachapov, Ilias

2010-01-01

Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

New adaptive method to optimize the secondary reflector of linear Fresnel collectors

DOE PAGES

Zhu, Guangdong

2017-01-16

Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form,more » but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. Here, the proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.« less

New adaptive method to optimize the secondary reflector of linear Fresnel collectors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Guangdong

Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form,more » but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. Here, the proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.« less

Trends in lipid profiles and descriptive characteristics of U.S. adults with and without diabetes and cholesterol-lowering medication use-National Health and Nutrition Examination Survey, 2003-2012, United States.

PubMed

Mercado, Carla I; Gregg, Edward; Gillespie, Cathleen; Loustalot, Fleetwood

2018-01-01

With a cholesterol-lowering focus for diabetic adults and in the age of polypharmacy, it is important to understand how lipid profile levels differ among those with and without diabetes. Investigate the means, differences, and trends in lipid profile measures [TC, total cholesterol; LDL-c, low-density lipoprotein; HDL-c, high-density lipoprotein; and TG, triglycerides] among US adults by diabetes status and cholesterol-lowering medication. Population number and proportion of adults aged ≥21 years with diabetes and taking cholesterol-lowering medication were estimated using data on 10,384 participants from NHANES 2003-2012. Age-standardized means, trends, and differences in lipid profile measures were estimated by diabetes status and cholesterol medication use. For trends and differences, linear regression analysis were used adjusted for age, gender, and race/ethnicity. Among diabetic adults, 52% were taking cholesterol-lowering medication compared to the 14% taking cholesterol-lowering medication without diabetes. Although diabetic adults had significantly lower TC and LDL-c levels than non-diabetic adults [% difference (95% confidence interval): TC = -5.2% (-6.8 --3.5), LDL-c = -8.0% (-10.4 --5.5)], the percent difference was greater among adults taking cholesterol medication [TC = -8.0% (-10.3 --5.7); LDL-c = -13.7% (-17.1 --10.2)] than adults not taking cholesterol medication [TC = -3.5% (-5.2 --1.6); LDL-c = -4.3% (-7.1 --1.5)] (interaction p-value: TC = <0.001; LDL-c = <0.001). From 2003-2012, mean TC and HDL-c significantly decreased among diabetic adults taking cholesterol medication [% difference per survey cycle (p-value for linear trend): TC = -2.3% (0.003) and HDL-c = -2.3% (0.033)]. Mean TC, HDL-c, and LDL-c levels did not significantly change from 2003 to 2012 in non-diabetic adults taking cholesterol medication or for adults not taking cholesterol medications. Diabetic adults were more likely to have lower lipid levels, except for triglyceride levels, than non-diabetic adults with profound differences when considering cholesterol medication use, possibly due to the positive effects from clinical diabetes management.

Special Judo Fitness Test Level and Anthropometric Profile of Elite Spanish Judo Athletes.

PubMed

Casals, Cristina; Huertas, Jesús R; Franchini, Emerson; Sterkowicz-Przybycień, Katarzyna; Sterkowicz, Stanislaw; Gutiérrez-García, Carlos; Escobar-Molina, Raquel

2017-05-01

Casals, C, Huertas, JR, Franchini, E, Sterkowicz-Przybycień, K, Sterkowicz, S, Gutiérrez-García, C, and Escobar-Molina, R. Special judo fitness test level and anthropometric profile of elite spanish judo athletes. J Strength Cond Res 31(5): 1229-1235, 2017-The aim of this study was to determine the anthropometric variables that best predict Special Judo Fitness Test (SJFT) performance. In addition, anthropometric profiles of elite Spanish judo athletes were compared by sex and age category (seniors and juniors). In this cross-sectional study, a total of 51 (29 females) athletes from the Spanish National Judo Team were evaluated during a competitive period. All athletes performed the SJFT and underwent an anthropometric assessment through skinfold thickness measurements. Mann-Whitney comparisons by sex and age category showed that males had significantly higher muscle mass and lower fat mass than females (p < 0.001), whereas juniors and seniors exhibited few differences in body composition. Linear regression analyses (stepwise method) were performed to explore the relationships between anthropometric characteristics and SJFT variables. Model 1 included sex, age category, and body mass as predictors. Body mass and sex significantly predicted the SJFT index (R = 0.27, p < 0.001); thus, both criteria should be considered before interpreting the test. The predictors of model 2 were quick-assessment variables, including skinfolds, breadths, girths, and height. This regression model showed that the biceps skinfold significantly predicted the SJFT index in elite athletes (R = 0.31, p < 0.001). Model 3 included body compositions and somatotypes as predictors. Higher muscle and bone masses and lower ectomorphy were associated with better SJFT performance (R = 0.44, p < 0.001). Hence, training programs should attempt to increase the muscle mass percentage and reduce the upper arm fat, whereas the bone percentage could be considered in the selection of talented athletes in conjunction with other factors.

Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models.

PubMed

Seeker, Luise A; Ilska, Joanna J; Psifidi, Androniki; Wilbourn, Rachael V; Underwood, Sarah L; Fairlie, Jennifer; Holland, Rebecca; Froy, Hannah; Bagnall, Ainsley; Whitelaw, Bruce; Coffey, Mike; Nussey, Daniel H; Banos, Georgios

2018-01-01

Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL) to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1) characterize the change in bovine relative leukocyte TL (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05-0.08) and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954).

Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models

PubMed Central

Ilska, Joanna J.; Psifidi, Androniki; Wilbourn, Rachael V.; Underwood, Sarah L.; Fairlie, Jennifer; Holland, Rebecca; Froy, Hannah; Bagnall, Ainsley; Whitelaw, Bruce; Coffey, Mike; Nussey, Daniel H.; Banos, Georgios

2018-01-01

Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL) to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1) characterize the change in bovine relative leukocyte TL (RLTL) across the lifetime in Holstein Friesian dairy cattle, 2) estimate genetic parameters of RLTL over time and 3) investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05–0.08) and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954). PMID:29438415

Analysis of Learning Curve Fitting Techniques.

DTIC Science & Technology

1987-09-01

1986. 15. Neter, John and others. Applied Linear Regression Models. Homewood IL: Irwin, 19-33. 16. SAS User’s Guide: Basics, Version 5 Edition. SAS... Linear Regression Techniques (15:23-52). Random errors are assumed to be normally distributed when using -# ordinary least-squares, according to Johnston...lot estimated by the improvement curve formula. For a more detailed explanation of the ordinary least-squares technique, see Neter, et. al., Applied

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Photometric analysis of esthetically pleasant and unpleasant facial profile

PubMed Central

Fortes, Helena Nunes da Rocha; Guimarães, Thamirys Correia; Belo, Ivana Mara Lira; da Matta, Edgard Norões Rodrigues

2014-01-01

Objective To identify which linear, angular and proportionality measures could influence a profile to be considered esthetically pleasant or unpleasant, and to assess sexual dimorphism. Methods 150 standardized facial profile photographs of dental students of both sexes were obtained and printed on photographic paper. Ten plastic surgeons, ten orthodontists and ten layperson answered a questionnaire characterizing each profile as pleasant, acceptable or unpleasant. With the use of a score system, the 15 most pleasant and unpleasant profiles of each sex were selected. The photographs were scanned into AutoCAD computer software. Linear, angular and proportion measurements were obtained using the software tools. The average values between groups were compared by the Student's t-test and the Mann-Whitney test at 5%. Results The linear measures LL-S, LL-H, LL-E, LL-B and Pn-H showed statistically significant differences (p < 0.05). Statistical differences were also found in the angular measures G'.Pn.Pg', G'.Sn.Pg' and Sn.Me'.C and in the proportions G'-Sn:Sn-Me' and Sn-Gn':Gn'-C (p < 0.05). Differences between sexes were found for the linear measure Ala-Pn, angles G'-Pg'.N-Pn, Sn.Me'.C, and proportions Gn'-Sn:Sn-Me' and Ala-Pn:N'-Sn. (p < 0.05). Conclusion The anteroposterior position of the lower lip, the amount of nose that influences the profile, facial convexity, total vertical proportion and lip-chin proportion appear to influence pleasantness of facial profile. Sexual dimorphism was identified in nasal length, nasofacial and lower third of the face angles, total vertical and nasal height/length proportions. PMID:24945516

Photometric analysis of esthetically pleasant and unpleasant facial profile.

PubMed

Fortes, Helena Nunes da Rocha; Guimarães, Thamirys Correia; Belo, Ivana Mara Lira; da Matta, Edgard Norões Rodrigues

2014-01-01

To identify which linear, angular and proportionality measures could influence a profile to be considered esthetically pleasant or unpleasant, and to assess sexual dimorphism. 150 standardized facial profile photographs of dental students of both sexes were obtained and printed on photographic paper. Ten plastic surgeons, ten orthodontists and ten layperson answered a questionnaire characterizing each profile as pleasant, acceptable or unpleasant. With the use of a score system, the 15 most pleasant and unpleasant profiles of each sex were selected. The photographs were scanned into AutoCAD computer software. Linear, angular and proportion measurements were obtained using the software tools. The average values between groups were compared by the Student's t-test and the Mann-Whitney test at a significance level of 5%. The linear measures LL-S, LL-H, LL-E, LL-B and Pn-H showed statistically significant differences (p < 0.05). Statistical differences were also found in the angular measures G'.Pn.Pg', G'.Sn.Pg' and Sn.Me'.C and in the proportions G'-Sn:Sn-Me' and Sn-Gn':Gn'-C (p < 0.05). Differences between sexes were found for the linear measure Ala-Pn, angles G'-Pg'.N-Pn, Sn.Me'.C, and proportions Gn'-Sn:Sn-Me' and Ala-Pn:N'-Sn. (p < 0.05). The anteroposterior position of the lower lip, the amount of nose that influences the profile, facial convexity, total vertical proportion and lip-chin proportion appear to influence pleasantness of facial profile. Sexual dimorphism was identified in nasal length, nasofacial and lower third of the face angles, total vertical and nasal height/length proportions.

Nonlinear isochrones in murine left ventricular pressure-volume loops: how well does the time-varying elastance concept hold?

PubMed

Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P

2006-04-01

The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) < 0.75) during isovolumic relaxation and early filling; and 4) logarithmic regression is superior in estimating the fixed volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

PubMed

Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M

2017-06-01

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Application of General Regression Neural Network to the Prediction of LOD Change

NASA Astrophysics Data System (ADS)

Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

2012-01-01

Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Factors associated with mortality and length of stay in the Oporto burn unit (2006-2009).

PubMed

Bartosch, Isabel; Bartosch, Carla; Egipto, Paula; Silva, Alvaro

2013-05-01

Retrospective studies are essential to evaluate and improve the efficiency of care of burned patients. This study analyses the work done in the burn unit of Hospital de S. João in the north of Portugal. A retrospective review was performed in patients admitted from 2006 to 2009. The study population was characterised regarding patient demographics, admissions profile, burn aetiology, burn site, extension and treatment. Multiple linear and logistic regression models were done in order to elucidate which of these factors influenced the mortality and length of stay. The characteristics before and after the creation of the burn unit, as well as the similarities and differences with the published data of other national and international burn units, are analysed. Copyright © 2012 Elsevier Ltd and ISBI. All rights reserved.

Stress associated with hospitalization in patients with COPD: the role of social support and health related quality of life

PubMed Central

2012-01-01

Background The objective of this study was to determine stress levels during hospitalization in patients with Chronic Obstructive Pulmonary Disease (COPD). We wanted to relate stress to previous level of quality of life and patients’ Social Support. Methods 80 patients (70.43; SD = 8.13 years old) with COPD were assessed by means of: Hospital Stress Rating Scale, Nottingham Health Profile, St. George’s Respiratory Questionnaire and Social Support Scale. Results COPD patients’ stress levels are lower than expected independently from the severity or number of previous hospitalizations. Linear regression analysis shows the predictive value of Quality of Life and Social Support on stress level during hospitalization (p < 0.0001). Conclusion HRQOL and social support can be associated with stress during hospitalization. PMID:23227860

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

40 CFR 1066.220 - Linearity verification for chassis dynamometer systems.

Code of Federal Regulations, 2014 CFR

2014-07-01

... dynamometer speed and torque at least as frequently as indicated in Table 1 of § 1066.215. The intent of... linear regression and the linearity criteria specified in Table 1 of this section. (b) Performance requirements. If a measurement system does not meet the applicable linearity criteria in Table 1 of this...

A Learning Progression Should Address Regression: Insights from Developing Non-Linear Reasoning in Ecology

ERIC Educational Resources Information Center

Hovardas, Tasos

2016-01-01

Although ecological systems at varying scales involve non-linear interactions, learners insist thinking in a linear fashion when they deal with ecological phenomena. The overall objective of the present contribution was to propose a hypothetical learning progression for developing non-linear reasoning in prey-predator systems and to provide…

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Estimating normative limits of Heidelberg Retina Tomograph optic disc rim area with quantile regression.

PubMed

Artes, Paul H; Crabb, David P

2010-01-01

To investigate why the specificity of the Moorfields Regression Analysis (MRA) of the Heidelberg Retina Tomograph (HRT) varies with disc size, and to derive accurate normative limits for neuroretinal rim area to address this problem. Two datasets from healthy subjects (Manchester, UK, n = 88; Halifax, Nova Scotia, Canada, n = 75) were used to investigate the physiological relationship between the optic disc and neuroretinal rim area. Normative limits for rim area were derived by quantile regression (QR) and compared with those of the MRA (derived by linear regression). Logistic regression analyses were performed to quantify the association between disc size and positive classifications with the MRA, as well as with the QR-derived normative limits. In both datasets, the specificity of the MRA depended on optic disc size. The odds of observing a borderline or outside-normal-limits classification increased by approximately 10% for each 0.1 mm(2) increase in disc area (P < 0.1). The lower specificity of the MRA with large optic discs could be explained by the failure of linear regression to model the extremes of the rim area distribution (observations far from the mean). In comparison, the normative limits predicted by QR were larger for smaller discs (less specific, more sensitive), and smaller for larger discs, such that false-positive rates became independent of optic disc size. Normative limits derived by quantile regression appear to remove the size-dependence of specificity with the MRA. Because quantile regression does not rely on the restrictive assumptions of standard linear regression, it may be a more appropriate method for establishing normative limits in other clinical applications where the underlying distributions are nonnormal or have nonconstant variance.

Factors Impacting Online Ratings for Otolaryngologists.

PubMed

Calixto, Nathaniel E; Chiao, Whitney; Durr, Megan L; Jiang, Nancy

2018-06-01

To identify factors associated with online patient ratings and comments for a nationwide sample of otolaryngologists. Ratings, demographic information, and written comments were obtained for a random sample of otolaryngologists from HealthGrades.com and Vitals.com . Online Presence Score (OPS) was based on 10 criteria, including professional website and social media profiles. Regression analyses identified factors associated with increased rating. We evaluated for correlations between OPS and other attributes with star rating and used chi-square tests to evaluate content differences between positive and negative comments. On linear regression, increased OPS was associated with higher ratings on HealthGrades and Vitals; higher ratings were also associated with younger age on Vitals and less experience on HealthGrades. However, detailed correlation studies showed weak correlation between OPS and rating; age and graduation year also showed low correlation with ratings. Negative comments more likely focused on surgeon-independent factors or poor bedside manner. Though younger otolaryngologists with greater online presence tend to have higher ratings, weak correlations suggest that age and online presence have only a small impact on the content found on ratings websites. While most written comments are positive, deficiencies in bedside manner or other physician-independent factors tend to elicit negative comments.

Comparison of Metabolic and Hormonal Profiles of Women With and Without Premenstrual Syndrome: A Community Based Cross-Sectional Study.

PubMed

Hashemi, Somayeh; Ramezani Tehrani, Fahimeh; Mohammadi, Nader; Rostami Dovom, Marzieh; Torkestani, Farahnaz; Simbar, Masumeh; Azizi, Fereidoun

2016-04-01

Premenstrual syndrome (PMS) is reported by up to 85% of women of reproductive age. Although several studies have focused on the hormone and lipid profiles of females with PMS, the results are controversial. This study was designed to investigate the association of hormonal and metabolic factors with PMS among Iranian women of reproductive age. This study was a community based cross-sectional study. Anthropometric measurements, biochemical parameters, and metabolic disorders were compared between 354 women with PMS and 302 healthy controls selected from among 1126 women of reproductive age who participated in the Iranian PCOS prevalence study. P values < 0.05 were considered significant. Prolactin (PRL) and triglycerides (TG) were significantly elevated in women with PMS, whereas their testosterone (TES), high density lipoprotein (HDL) and 17-hydroxyprogesterone (17-OHP) levels were significantly less than they were in women without the syndrome (P < 0.05). After adjusting for age and body mass index (BMI), linear regression analysis demonstrated that for every one unit increase in PMS score there was 12% rise in the probability of having metabolic syndrome (P = 0.033). There was a significant association between PMS scores and the prevalence of metabolic syndrome. Further studies are needed to confirm and validate the relationships between lipid profile abnormalities and metabolic disorders with PMS.

Preparation and characterization of poly(lactic acid) nanoparticles for sustained release of pyridostigmine bromide.

PubMed

Tan, Q Y; Xu, M L; Wu, J Y; Yin, H F; Zhang, J Q

2012-04-01

A novel pyridostigmine bromide poly (lactic acid) nanoparticles (PBPNPs) was prepared to obtain sustained release characteristics of PB. A central composite design approach was employed for process optimization. The in vitro release studies were carried out by dialysis method and conducted using four different dissolution media. Similar factor method was investigated for dissolution profile comparison. Multiple linear regression analysis for process optimization revealed that the optimal PBPNPs were obtained where the values of the amount of PB (X1, mg), PLA concentration (X2, % w:v), and PVA concentration (X3, % w:v) were 49.20 mg, 3.31% and 3.41%, respectively. The average particle size and zeta potential of PBPNPs with the optimized formulation were 722.9 +/- 4.3 nm, and -25.12 +/- 1.2 mV, respectively. PBPNPs provided an initial burst of drug release followed by a very slow release over an extended period of time (72 h). Compared with free PB, PBPNPs had a significantly lower release rate of PB in vitro. The in vitro release profile of the PBPNPs could be described by Weibull models, regardless of type of dissolution medium. Statistical significance of similarity between every two dissolution profiles of PBPNPs in different dissolution media was found, and the difference between the curves of PBPNPs and pure PB was statistically significant.

Drivers of high-involvement consumers' intention to buy PDO wines: Valpolicella PDO case study.

PubMed

Capitello, Roberta; Agnoli, Lara; Begalli, Diego

2016-08-01

This study investigates whether different sensory profiles of wines belonging to the same Protected Designation of Origin (PDO) are perceived as different products by consumers. It identifies the drivers of consumers' intention to buy preferred wines. Descriptive sensory analysis, consumer tests and consumer interviews were conducted to reach research aims. To perform the consumer tests and interviews, 443 consumers participated in the survey. The tasted wines comprised five samples representative of Valpolicella PDO wine. Analysis of variance tests, principal component analysis and linear and logit regressions were employed to verify the research hypotheses. The results demonstrated: (1) different sensory profiles exist within the Valpolicella PDO wine; (2) these sensory profiles result in consumers having the perception of diversified products; (3) the perception of differences was less marked for consumers than for trained assessors due to the different weight attributed to visual, aroma and the taste/mouthfeel hedonic dimensions; and (4) consumers' liking, as well as general perceptions, attitudes, preferences, wine knowledge and experience, contribute to consumers' intentions to buy more than the socio-demographic characteristics of consumers. The analysis of the drivers of consumers' intention to buy certain PDO wines provides new marketing insights into the roles of intrinsic quality, preferences and consumers' subjective characteristics in market segmentation. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Aphid aerial density profiles are consistent with turbulent advection amplifying flight behaviours: abandoning the epithet ‘passive’

PubMed Central

Reynolds, Andy M; Reynolds, Don R

2008-01-01

Seminal field studies led by C. G. Johnson in the 1940s and 1950s showed that aphid aerial density diminishes with height above the ground such that the linear regression coefficient, b, of log density on log height provides a single-parameter characterization of the vertical density profile. This coefficient decreases with increasing atmospheric stability, ranging from −0.27 for a fully convective boundary layer to −2.01 for a stable boundary layer. We combined a well-established Lagrangian stochastic model of atmospheric dispersal with simple models of aphid behaviour in order to account for the range of aerial density profiles. We show that these density distributions are consistent with the aphids producing just enough lift to become neutrally buoyant when they are in updraughts and ceasing to produce lift when they are in downdraughts. This active flight behaviour in a weak flier is thus distinctly different from the aerial dispersal of seeds and wingless arthropods, which is passive once these organisms have launched into the air. The novel findings from the model indicate that the epithet ‘passive’ often applied to the windborne migration of small winged insects is misleading and should be abandoned. The implications for the distances traversed by migrating aphids under various boundary-layer conditions are outlined. PMID:18782743

Nutritional Epidemiology of Antenatal Smoking Cessation Among Japanese Women.

PubMed

Mak, Kwok-Kei; Watanabe, Hiroko; Nomachi, Shinobu; Suganuma, Nobuhiko

2016-01-01

This study compared the nutritional status before pregnancy, as well as dietary profiles and biomarkers during first trimester, between never-smokers and antenatal quitters among Japanese women. One hundred fifty pregnant women (79 never-smokers and 71 antenatal quitters) from two obstetrics and gynecology clinics were recruited in Japan. Subjects' prepregnancy nutritional status was indicated by their body mass index (BMI). In the first trimester, their dietary profiles were assessed by the Brief Diet-History Questionnaire (BDHQ) and pregnancy outcomes were screened by biomarker tests. Generalized linear regression was used to examine the differences of energy-adjusted dietary intakes and biomarker results between the two smoking groups, with adjustment of maternal age, BMI, gestation week, and parity. The results showed that antenatal quitters were more likely to have a prepregnancy underweight status than never-smokers. During the first trimester, antenatal quitters had significantly higher intakes of unsaturated fatty acids and antioxidants (vegetable lipids and isoflavone), and lower intakes of total cholesterol than never-smokers. Moreover, antenatal quitters had a significantly higher level of serum homocysteine (6.36 nmol/mL vs 4.88 nmol/mL) than never-smokers. In conclusion, antenatal quitters are more likely to have a poor nutritional status before pregnancy than never-smokers. Quitting smoking before pregnancy and having a good nutritional profile during the trimester may not sufficiently reverse the adverse effects of former smoking behaviors on pregnancy outcomes.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Blood lipid levels in a rural male population.

PubMed

Thelin, A; Stiernström, E L; Holmberg, S

2001-06-01

Farmers have a low risk for cardiovascular disease, which may be related to a favourable blood lipid profile. In order to study the blood lipid levels and evaluate the effect of other cardiovascular risk factors on the blood lipid profile, this cross-sectional study was made. A total of 1013 farmers and 769 non-farming rural men in nine different Swedish counties were examined, interviewed, and replied to questionnaires. The inter-relationships between different risk factors were analysed using a multivariate linear regression model. The farmers had a significantly more favourable blood lipid profile than the non-farmers although the total cholesterol levels were almost the same for the two groups. In the total study population there were significant positive relationships between total cholesterol level and body mass index (BMI), diastolic blood pressure and smoking. The high-density lipoprotein (HDL) level was positively related to physical workload and alcohol consumption, and negatively related to BMI, waist/hip ratio and smoking. Triglyceride levels showed a positive relationship to BMI, waist/hip ratio and blood pressure. Differences between farmers and other rural males were seen, especially with respect to the effect of physical activity and psychosocial factors. Among the farmers, a negative correlation between the Karasek-Theorell authority over work index and total cholesterol, the low-density lipoprotein (LDL)/HDL ratio and triglyceride levels was observed. This study indicated that diet is of minor significance for the blood lipid profile, whereas factors such as physical activity, body weight and the waist/hip ratio, smoking, alcohol consumption, and perhaps psychosocial working conditions are major independent factors affecting the blood lipid profile most prominently among farmers, but also among non-farming rural men.

City-level variations in NOx emissions derived from hourly monitoring data in Chicago

NASA Astrophysics Data System (ADS)

de Foy, Benjamin

2018-03-01

Control on emissions of nitrogen oxides (NOx) in the United States of America have led to reductions in concentrations in urban areas by up to a factor of two in the last decade. The Air Quality System monitoring network provides surface measurements of concentrations at hourly resolution over multiple years, revealing variations at the annual, seasonal, day of week and diurnal time scales. A multiple linear regression model was used to estimate the temporal profiles in the NOx concentrations as well as the impact of meteorology, ozone concentrations, and boundary layer heights. The model is applied to data from 2005 to 2016 available at 6 sites in Chicago, Illinois. Results confirm the 50% decrease in NOx over the length of the time series. The weekend effect is found to be stronger in more commercial areas, with 32% reductions on Saturdays and 45% on Sundays and holidays; and weaker in more residential areas with 20% reductions on Saturdays and 30% reductions on Sundays. Weekday diurnal profiles follow a double hump with emission peaks during the morning and afternoon rush hours, but only a shallow drop during the middle day. Difference in profiles from the 6 sites suggest that there are different emission profiles within the urban area. Diurnal profiles on Saturdays have less variation throughout the day and more emissions in the evening. Sundays are very different from both weekdays and Saturdays with a gradual increase until the early evening. The results suggest that in addition to vehicle type and vehicle miles traveled, vehicle speed and congestion must be taken into account to correctly quantify morning rush hour emissions and the weekend effect.

Leveraging cues from person-generated health data for peer matching in online communities.

PubMed

Hartzler, Andrea L; Taylor, Megan N; Park, Albert; Griffiths, Troy; Backonja, Uba; McDonald, David W; Wahbeh, Sam; Brown, Cory; Pratt, Wanda

2016-05-01

Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor. Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants' ratings with linear regression and qualitatively analyzed participants' feedback for emerging themes about choosing mentors and improving profile design. Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor. Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mental chronometry with simple linear regression.

PubMed

Chen, J Y

1997-10-01

Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Distance correction system for localization based on linear regression and smoothing in ambient intelligence display.

PubMed

Kim, Dae-Hee; Choi, Jae-Hun; Lim, Myung-Eun; Park, Soo-Jun

2008-01-01

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression and smoothing method, by which distance information of a user who approaches to the display can he accurately output even in an unanticipated condition using a passive infrared VIR) sensor and an ultrasonic device. The developed system consists of an ambient intelligence display and an ultrasonic transmitter, and a sensor gateway. Each module communicates with each other through RF (Radio frequency) communication. The ambient intelligence display includes an ultrasonic receiver and a PIR sensor for motion detection. In particular, this system selects and processes algorithms such as smoothing or linear regression for current input data processing dynamically through judgment process that is determined using the previous reliable data stored in a queue. In addition, we implemented GUI software with JAVA for real time location tracking and an ambient intelligence display.

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER’S DISEASE*

PubMed Central

Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

2015-01-01

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM. PMID:26900412

Liquid electrolyte informatics using an exhaustive search with linear regression.

PubMed

Sodeyama, Keitaro; Igarashi, Yasuhiko; Nakayama, Tomofumi; Tateyama, Yoshitaka; Okada, Masato

2018-06-14

Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the "prediction accuracy" and "calculation cost" of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the "accuracy" and "cost" when the search of a huge amount of new materials was carried out.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

PubMed

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso

PubMed Central

Kong, Shengchun; Nan, Bin

2013-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328

Observations of Rotation Reversal and Fluctuation Hysteresis in Alcator C-Mod L-Mode Plasmas

NASA Astrophysics Data System (ADS)

Cao, N. M.; Rice, J. E.; White, A. E.; Baek, S. G.; Creely, A. J.; Ennever, P. C.; Hubbard, A. E.; Hughes, J. W.; Irby, J.; Rodriguez-Fernandez, P.; Chilenski, M. A.; Diamond, P. H.; Reinke, M. L.; Alcator C-Mod Team

2017-10-01

Intrinsic core toroidal rotation in Alcator C-Mod L-mode plasmas has been observed to spontaneously reverse direction when the minimum value of the normalized collisionality ν*, crosses around 0.4. In Ohmic plasmas, the rotation is co-current in the low density linear Ohmic confinement (LOC) regime and counter-current in the higher density saturated Ohmic confinement (SOC) regime. The reversal manifests a hysteresis loop in ν*, where the critical collisionalities for the forward and reverse transitions differ by 10-15%. Temperature and density profiles of the two rotation states are observed to be indistinguishable to within experimental error estimated with Gaussian process regression. However, qualitative differences between the two rotation states are observed in fluctuation spectra, including the broadening of reflectometry spectra and, under certain conditions, the appearance of high-k features in phase contrast imaging (PCI) spectra (kθρs up to 1). These results suggest that the turbulent state can decouple from local profiles, and that turbulent self-regulation may play a role in the LOC/SOC transition. This work is supported by the US DOE under Grant DE-FC02-99ER54512 (C-Mod).

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

TU-H-BRA-06: Characterization of a Linear Accelerator Operating in a Compact MRIGuided Radiation Therapy System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, O; Mutic, S; Li, H

2016-06-15

Purpose: To describe the performance of a linear accelerator operating in a compact MRI-guided radiation therapy system. Methods: A commercial linear accelerator was placed in an MRI unit that is employed in a commercial MR-based image guided radiation therapy (IGRT) system. The linear accelerator components were placed within magnetic field-reducing hardware that provided magnetic fields of less than 40 G for the magnetron, gun driver, and port circulator, with 1 G for the linear accelerator. The system did not employ a flattening filter. The test linear accelerator was an industrial 4 MV model that was employed to test the abilitymore » to run an accelerator in the MR environment. An MR-compatible diode detector array was used to measure the beam profiles with the accelerator outside and inside the MR field and with the gradient coils on and off to examine if there was any effect on the delivered dose distribution. The beam profiles and time characteristics of the beam were measured. Results: The beam profiles exhibited characteristic unflattened Bremsstrahlung features with less than ±1.5% differences in the profile magnitude when the system was outside and inside the magnet and less than 1% differences with the gradient coils on and off. The central axis dose rate fluctuated by less than 1% over a 30 second period when outside and inside the MRI. Conclusion: A linaccompatible MR design has been shown to be effective in not perturbing the operation of a commercial linear accelerator. While the accelerator used in the tests was 4MV, there is nothing fundamentally different with the operation of a 6MV unit, implying that the design will enable operation of the proposed clinical unit. Research funding provided by ViewRay, Inc.« less

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

Treesearch

Kenneth L. Cormier; Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1992-01-01

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the...

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data.

PubMed

Kim, Jongrae; Bates, Declan G; Postlethwaite, Ian; Heslop-Harrison, Pat; Cho, Kwang-Hyun

2008-05-15

Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. The software used in this article is available from http://sbie.kaist.ac.kr/software

Generation and Characterization of Electron Bunches with Ramped Current Profiles in a Dual-Frequency Superconducting Linear Accelerator

DOE PAGES

Piot, P.; Behrens, C.; Gerth, C.; ...

2011-09-07

We report on the successful experimental generation of electron bunches with ramped current profiles. The technique relies on impressing nonlinear correlations in the longitudinal phase space using a superconducing radiofrequency linear accelerator operating at two frequencies and a current-enhancing dispersive section. The produced {approx} 700-MeV bunches have peak currents of the order of a kilo-Ampere. Data taken for various accelerator settings demonstrate the versatility of the method and in particular its ability to produce current profiles that have a quasi-linear dependency on the longitudinal (temporal) coordinate. The measured bunch parameters are shown, via numerical simulations, to produce gigavolt-per-meter peak acceleratingmore » electric fields with transformer ratios larger than 2 in dielectric-lined waveguides.« less

Improving in the fasting, but not the postprandial, glucose level is associated with reduction of plasma d-ROMs level in patients with type 2 diabetes.

PubMed

Ohara, Makoto; Watanabe, Kentaro; Suzuki, Tatsuya; Sekimizu, Ken-ichi; Motoyama, Masayuki; Ishii, Kazuhito; Sawai, Keisuke; Nakano, Hiroshi; Oba, Kenzo; Mizuno, Kyoichi

2013-01-01

This study aimed to evaluate the relationship between improvement of glucose metabolism and plasma levels of diacron-reactive oxygen metabolites (d-ROMs) in patients with type 2 diabetes. As the first daily profile, the plasma levels of glucose and d-ROMs were determined on admission. Then, after treatment to lower plasma glucose levels, the second daily profile of these levels was evaluated. Fasting plasma glucose (FPG), the total area under the curve (AUC) of the daily plasma glucose profile (AUCDP), the AUC of the postprandial plasma glucose levels (AUCPP), the AUC of the daily plasma d-ROMs profile (AUCd-ROMs), the coefficient of variation (CV) of plasma glucose (CVPG), and the mean amplitude of glycemic excursions (MAGE) were calculated. The relationship between the improvement of glucose metabolism and that of oxidative stress in patients with type 2 diabetes was evaluated. The second determinations of FPG, AUCDP, AUCPP, MAGE, and AUCd-ROMs were significantly lower than those of the first determinations, but no significant difference was observed in CVPG. Linear regression analysis demonstrated significant associations between the changes in AUCd-ROMs and the changes in both FPG and AUCDP, whereas no significant association was observed between the change in AUCd-ROMs and the change in AUCPP, CVPG, or MAGE. This study has demonstrated that improvement of the FPG level, but not of the postprandial glucose level, is associated with a reduction of the plasma level of d-ROMs in patients with type 2 diabetes.

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Light impurity transport in JET ILW L-mode plasmas

NASA Astrophysics Data System (ADS)

Bonanomi, N.; Mantica, P.; Giroud, C.; Angioni, C.; Manas, P.; Menmuir, S.; Contributors, JET

2018-03-01

A series of experimental observations of light impurity profiles was carried out in JET (Joint European Torus) ITER-like wall (ILW) L-mode plasmas in order to investigate their transport mechanisms. These discharges feature the presence of 3He, Be, C, N, Ne, whose profiles measured by active Charge Exchange diagnostics are compared with quasi-linear and non-linear gyro-kinetic simulations. The peaking of 3He density follows the electron density peaking, Be and Ne are also peaked, while the density profiles of C and N are flat in the mid plasma region. Gyro-kinetic simulations predict peaked density profiles for all the light impurities studied and at all the radial positions considered, and fail predicting the flat or hollow profiles observed for C and N at mid radius in our cases.

Linear regression models for solvent accessibility prediction in proteins.

PubMed

Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Using Linear Regression To Determine the Number of Factors To Retain in Factor Analysis and the Number of Issues To Retain in Delphi Studies and Other Surveys.

ERIC Educational Resources Information Center

Jurs, Stephen; And Others

The scree test and its linear regression technique are reviewed, and results of its use in factor analysis and Delphi data sets are described. The scree test was originally a visual approach for making judgments about eigenvalues, which considered the relationships of the eigenvalues to one another as well as their actual values. The graph that is…

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

PubMed

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Time of flight dependent linearity in diffuse imaging: how effective is it to evaluate the spatial resolution by measuring the edge response function?

PubMed

Ortiz-Rascón, E; Bruce, N C; Rodríguez-Rosales, A A; Garduño-Mejía, J

2016-03-01

We describe the behavior of linearity in diffuse imaging by evaluating the differences between time-resolved images produced by photons arriving at the detector at different times. Two approaches are considered: Monte Carlo simulations and experimental results. The images of two complete opaque bars embedded in a transparent or in a turbid medium with a slab geometry are analyzed; the optical properties of the turbid medium sample are close to those of breast tissue. A simple linearity test was designed involving a direct comparison between the intensity profile produced by two bars scanned at the same time and the intensity profile obtained by adding two profiles of each bar scanned one at a time. It is shown that the linearity improves substantially when short time of flight photons are used in the imaging process, but even then the nonlinear behavior prevails. As the edge response function (ERF) has been used widely for testing the spatial resolution in imaging systems, the main implication of a time dependent linearity is the weakness of the linearity assumption when evaluating the spatial resolution through the ERF in diffuse imaging systems, and the need to evaluate the spatial resolution by other methods.

Intercomparison of methods for the estimation of displacement height and roughness length from single-level eddy covariance data

NASA Astrophysics Data System (ADS)

Graf, Alexander; van de Boer, Anneke; Schüttemeyer, Dirk; Moene, Arnold; Vereecken, Harry

2013-04-01

The displacement height d and roughness length z0 are parameters of the logarithmic wind profile and as such these are characteristics of the surface, that are required in a multitude of meteorological modeling applications. Classically, both parameters are estimated from multi-level measurements of wind speed over a terrain sufficiently homogeneous to avoid footprint-induced differences between the levels. As a rule-of thumb, d of a dense, uniform crop or forest canopy is 2/3 to 3/4 of the canopy height h, and z0 about 10% of canopy height in absence of any d. However, the uncertainty of this rule-of-thumb becomes larger if the surface of interest is not "dense and uniform", in which case a site-specific determination is required again. By means of the eddy covariance method, alternative possibilities to determine z0 and d have become available. Various authors report robust results if either several levels of sonic anemometer measurements, or one such level combined with a classic wind profile is used to introduce direct knowledge on the friction velocity into the estimation procedure. At the same time, however, the eddy covariance method to measure various fluxes has superseded the profile method, leaving many current stations without a wind speed profile with enough levels sufficiently far above the canopy to enable the classic estimation of z0 and d. From single-level eddy covariance measurements at one point in time, only one parameter can be estimated, usually z0 while d is assumed to be known. Even so, results tend to scatter considerably. However, it has been pointed out, that the use of multiple points in time providing different stability conditions can enable the estimation of both parameters, if they are assumed constant over the time period regarded. These methods either rely on flux-variance similarity (Weaver 1990 and others following), or on the integrated universal function for momentum (Martano 2000 and others following). In both cases, iterations over the range of possible d values are necessary. We extended this set of methods by a non-iterative, regression based approach. Only a stability range of data is used in which the universal function is known to be approximately linear. Then, various types of multiple linear regression can be used to relate the terms of the logarithmic wind profile equation to each other, and derive z0 and d from the regression parameters. Two examples each of the two existing iterative approaches, and the new non-iterative one are compared to each other and to plausibility limits in three different agricultural crops. The study contains periods of growth as well as of constant crop height, also allowing for an examination of the relations between z0, d, and canopy height. Results indicate that estimated z0 values, even in absence of prescribed d values, are fairly robust, plausible and consistent across all methods. The largest deviations are produced by the two flux-variance similarity based methods. Estimates of d, in contrast, can be subject to implausible deviations with all methods, even after quality-filtering of input data. Again, the largest deviations occur with flux-variance similarity based methods. Ensemble averaging between all methods can reduce this problem, offering a potentially useful way of estimating d at more complex sites where the rule-of-thumb cannot be applied easily. Martano P (2000): Estimation of surface roughness length and displacement height from single-level sonic anemometer data. Journal of Applied Meteorology 39:708-715. Weaver HL (1990): Temperature and Humidity flux-variance relations determined by one-dimensional eddy correlation. Boundary-Layer Meteorology 53:77-91.

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Different behavioral effect dose–response profiles in mice exposed to two-carbon chlorinated hydrocarbons: Influence of structural and physical properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Umezu, Toyoshi, E-mail: umechan2@nies.go.jp; Shibata, Yasuyuki, E-mail: yshibata@nies.go.jp

2014-09-01

The present study aimed to clarify whether dose–response profiles of acute behavioral effects of 1,2-dichloroethane (DCE), 1,1,1-trichloroethane (TCE), trichloroethylene (TRIC), and tetrachloroethylene (PERC) differ. A test battery involving 6 behavioral endpoints was applied to evaluate the effects of DCE, TCE, TRIC, and PERC in male ICR strain mice under the same experimental conditions. The behavioral effect dose–response profiles of these compounds differed. Regression analysis was used to evaluate the relationship between the dose–response profiles and structural and physical properties of the compounds. Dose–response profile differences correlated significantly with differences in specific structural and physical properties. These results suggest that differencesmore » in specific structural and physical properties of DCE, TCE, TRIC, and PERC are responsible for differences in behavioral effects that lead to a variety of dose–response profiles. - Highlights: • We examine effects of 4 chlorinated hydrocarbons on 6 behavioral endpoints in mice. • The behavioral effect dose–response profiles for the 4 compounds are different. • We utilize regression analysis to clarify probable causes of the different profiles. • The compound's physicochemical properties probably produce the different profiles.« less

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

Diagnosis of Enzyme Inhibition Using Excel Solver: A Combined Dry and Wet Laboratory Exercise

ERIC Educational Resources Information Center

Dias, Albino A.; Pinto, Paula A.; Fraga, Irene; Bezerra, Rui M. F.

2014-01-01

In enzyme kinetic studies, linear transformations of the Michaelis-Menten equation, such as the Lineweaver-Burk double-reciprocal transformation, present some constraints. The linear transformation distorts the experimental error and the relationship between "x" and "y" axes; consequently, linear regression of transformed data…

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

Geomorphological and structural characterization of the southern Weihe Graben, central China: Implications for fault segmentation

NASA Astrophysics Data System (ADS)

Cheng, Yali; He, Chuanqi; Rao, Gang; Yan, Bing; Lin, Aiming; Hu, Jianmin; Yu, Yangli; Yao, Qi

2018-01-01

The Cenozoic graben systems around the tectonically stable Ordos Block, central China, have been considered as ideal places for investigating active deformation within continental rifts, such as the Weihe Graben at the southern margin with high historical seismicity (e.g., 1556 M 8.5 Huaxian great earthquake). However, previous investigations have mostly focused on the active structures in the eastern and northern parts of this graben. By contrast, in the southwest, tectonic activity along the northern margin of the Qinling Mountains has not been systematically investigated yet. In this study, based on digital elevation models (DEMs), we carried out geomorphological analysis to evaluate the relative tectonic activity along the whole South Border Fault (SBF). On the basis of field observations, high resolution DEMs acquired by small unmanned aerial vehicles (sUVA) using structure-for-motion techniques, radiocarbon (14C) age dating, we demonstrate that: 1) Tectonic activity along the SBF changes along strike, being higher in the eastern sector. 2) Seven major segment boundaries have been assigned, where the fault changes its strike and has lower tectonic activity. 3) The fault segment between the cities of Huaxian and Huayin characterized by almost pure normal slip has been active during the Holocene. We suggest that these findings would provide a basis for further investigating on the seismic risk in densely-populated Weihe Graben. Table S2. The values and classification of geomorphic indices obtained in this study. Fig. S1. Morphological features of the stream long profiles (Nos. 1-75) and corresponding SLK values. Fig. S2. Comparison of geomorphological parameters acquired from different DEMs (90-m SRTM and 30-m ASTER GDEM): (a) HI values; (b) HI linear regression; (c) mean slope of drainage basin; (d) mean slope linear regression.

A practical data processing workflow for multi-OMICS projects.

PubMed

Kohl, Michael; Megger, Dominik A; Trippler, Martin; Meckel, Hagen; Ahrens, Maike; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-Claudius; Baba, Hideo A; Sitek, Barbara; Schlaak, Jörg F; Meyer, Helmut E; Stephan, Christian; Eisenacher, Martin

2014-01-01

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. Copyright © 2013 Elsevier B.V. All rights reserved.

Communication skills of tutors and family medicine physician residents in Primary Care clinics.

PubMed

Valverde Bolívar, Francisco Javier; Pedregal González, Miguel; Pérez Fuentes, María Francisca; Alcalde Molina, María Dolores; Torío Durántez, Jesús; Delgado Rodríguez, Miguel

2016-12-01

To determine the communicative profiles of family physicians and the characteristics associated with an improved level of communication with the patient. A descriptive multicentre study. Primary Healthcare Centres in Almeria, Granada, Jaen and Huelva. 119 family physicians (tutors and 4th year resident physicians) filmed and observed with patients. Demographic and professional characteristics. Analysis of the communication between physicians and patients, using a CICAA (Connect, Identify, Understand, Agree and Assist, in English) scale. A descriptive, bivariate, multiple linear regression analysis was performed. There were 436 valid interviews. Almost 100% of physicians were polite and friendly, facilitating a dialogue with the patient and allowing them to express their doubts. However, few physicians attempted to explore the state of mind of the patient, or enquire about their family situation or any important stressful events, nor did they ask open questions. Furthermore, few physicians summarised the information gathered. The mean score was 21.43±5.91 points (maximum 58). There were no differences in the total score between gender, city, or type of centre. The linear regression verified that the highest scores were obtained from tutors (B: 2.98), from the duration of the consultations (B: 0.63), and from the age of the professionals (B: -0.1). Physicians excel in terms of creating a friendly environment, possessing good listening skills, and providing the patient with information. However the ability to empathise, exploring the psychosocial sphere, carrying out shared decision-making, and asking open questions must be improved. Being a tutor, devoting more time to consultations, and being younger, results in a significant improvement in communication with the patient. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity.

PubMed

Forno, Erick; Wang, Ting; Yan, Qi; Brehm, John; Acosta-Perez, Edna; Colon-Semidey, Angel; Alvarez, Maria; Boutaoui, Nadia; Cloutier, Michelle M; Alcorn, John F; Canino, Glorisa; Chen, Wei; Celedón, Juan C

2017-10-01

Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics "vertical" approach that integrates multiple analytical steps using linear and logistic regression models. In a case-control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression-protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2-3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4-8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate-severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.

Validation of cooling effect of insulated containers for the shipment of corneal tissue and recommendations for transport.

PubMed

Miller, Thomas D; Maxwell, Andrew J; Lindquist, Thomas D; Requard, Jake

2013-01-01

To determine the cooling effect of generic insulated shipping containers in ambient and high-temperature environments. Twenty-seven shipping containers were packed with wet ice according to industry standards. The ice in each container was weighed. Ambient temperatures were recorded by data loggers affixed to the exterior. Internal temperatures were recorded by data loggers packed inside the containers, for as long as the data loggers remained at ≤8°C. The cooling effect, or minutes per gram of ice a data logger maintained a temperature of ≤8°C, was calculated using linear regression; 8 similar containers were subjected to elevated summer temperatures. Small, medium, and large containers held mean masses of wet ice of 685, 1929, and 4439 g, respectively. The linear regression equation for grams of ice to duration of time at ≤8°C was y = 0.1994x + 385.13 for small containers, y = 0.1854x + 1273.3 for medium, and y = 0.5892x + 1410.3 for large containers, resulting in a cooling effect of 25.1 hours for small, 58.9 hours for medium, and 85.7 hours for large containers at ambient temperature. The duration of cooling effect in the summer profile group was consistent with that of the ambient temperature group. All of the container sizes successfully maintained proper cooling when packed with the appropriate grams of wet ice for the needed time interval. This study validates current practice for the shipment of corneal tissue in inexpensive, generic containers that can maintain effective cooling for the duration required for local, national, and international shipment.

Adiposity as a full mediator of the influence of cardiorespiratory fitness and inflammation in schoolchildren: The FUPRECOL Study.

PubMed

Garcia-Hermoso, A; Agostinis-Sobrinho, C; Mota, J; Santos, R M; Correa-Bautista, J E; Ramírez-Vélez, R

2017-06-01

Studies in the paediatric population have shown inconsistent associations between cardiorespiratory fitness and inflammation independently of adiposity. The purpose of this study was (i) to analyse the combined association of cardiorespiratory fitness and adiposity with high-sensitivity C-reactive protein (hs-CRP), and (ii) to determine whether adiposity acts as a mediator on the association between cardiorespiratory fitness and hs-CRP in children and adolescents. This cross-sectional study included 935 (54.7% girls) healthy children and adolescents from Bogotá, Colombia. The 20 m shuttle run test was used to estimate cardiorespiratory fitness. We assessed the following adiposity parameters: body mass index, waist circumference, and fat mass index and the sum of subscapular and triceps skinfold thickness. High sensitivity assays were used to obtain hs-CRP. Linear regression models were fitted for mediation analyses examined whether the association between cardiorespiratory fitness and hs-CRP was mediated by each of adiposity parameters according to Baron and Kenny procedures. Lower levels of hs-CRP were associated with the best schoolchildren profiles (high cardiorespiratory fitness + low adiposity) (p for trend <0.001 in the four adiposity parameters), compared with unfit and overweight (low cardiorespiratory fitness + high adiposity) counterparts. Linear regression models suggest a full mediation of adiposity on the association between cardiorespiratory fitness and hs-CRP levels. Our findings seem to emphasize the importance of obesity prevention in childhood, suggesting that having high levels of cardiorespiratory fitness may not counteract the negative consequences ascribed to adiposity on hs-CRP. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Masticatory performance and oral health-related quality of life before and after complete denture treatment.

PubMed

Yamamoto, Saori; Shiga, Hiroshi

2018-03-13

To clarify the relationship between masticatory performance and oral health-related quality of life (OHRQoL) before and after complete denture treatment. Thirty patients wearing complete dentures were asked to chew a gummy jelly on their habitual chewing side, and the amount of glucose extraction during chewing was measured as the parameter of masticatory performance. Subjects were asked to answer the Oral Health Impact Profile (OHIP-J49) questionnaire, which consists of 49 questions related to oral problems. The total score of 49 question items along with individual domain scores within the seven domains (functional limitation, pain, psychological discomfort, physical disability, psychological disability, social disability and handicap) were calculated and used as the parameters of OHRQoL. These records were obtained before treatment and 3 months after treatment. Each parameter of masticatory performance and OHRQoL was compared before treatment and after treatment. The relationship between masticatory performance and OHRQoL was investigated, and a stepwise multiple linear regression analysis was performed. Both masticatory performance and OHRQoL were significantly improved after treatment. Furthermore, masticatory performance was significantly correlated with some parameters of OHRQoL. The stepwise multiple linear regression analysis showed functional limitation and pain as important factors affecting masticatory performance before treatment and functional limitation as important factors affecting masticatory performance after treatment. These results suggested that masticatory performance and OHRQoL are significantly improved after treatment and that there is a close relationship between the two. Moreover, functional limitation was found to be the most important factor affecting masticatory performance. Copyright © 2018 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

Looking for the Perfect Mentor.

PubMed

Sá, Ana Pinheiro; Teixeira-Pinto, Cristina; Veríssimo, Rafaela; Vilas-Boas, Andreia; Firmino-Machado, João

2015-01-01

The authors established the profile of the Internal Medicine clinical teachers in Portugal aiming to define a future interventional strategy plan as adequate as possible to the target group and to the problems identified by the residents. Observational, transversal, analytic study. An online anonymous questionnaire was defined, evaluating the demographic characteristics of the clinical teachers, their path in Internal Medicine and their involvement in the residents learning process. We collected 213 valid questionnaires, making for an estimated response rate of 28.4%. Median global satisfaction with the clinical teacher was 4.52 (± 1.33 points) and the classification of the relationship between resident and clinical teacher was 4.86 ± 1.04 points. The perfect clinical teacher is defined by high standards of dedication and responsibility (4.9 ± 1.37 points), practical (4.8 ± 1.12 points) and theoretical skills (4.8 ± 1.07 points). The multiple linear regression model allowed to determine predictors of the residentâs satisfaction with their clinical teacher, justifying 82,5% of the variation of satisfaction with the clinical teacher (R2 = 0.83; R2 a = 0.82). Postgraduate medical education consists of an interaction between several areas of knowledge and intervening variables in the learning process having the clinical teacher in the central role. Overall, the pedagogical abilities were the most valued by the Internal Medicine residents regarding their clinical teacher, as determinants of a quality residentship. This study demonstrates the critical relevance of the clinical teacher in the satisfaction of residents with their residentship. The established multiple linear regression model highlights the impact of the clinical and pedagogical relantionship with the clinical teacher in a relevant increase in the satisfaction with the latter.

Periodontal status affects C-reactive protein and lipids in patients with stable heart disease from a tertiary care cardiovascular clinic.

PubMed

Flores, Manuela F; Montenegro, Marlon M; Furtado, Mariana V; Polanczyk, Carisi A; Rösing, Cassiano K; Haas, Alex N

2014-04-01

There are scarce data on the impact of the periodontal condition in the control of biomarkers in patients with cardiovascular disease (CVD). The aim of this study is to assess whether periodontal inflammation and tissue breakdown are associated with C-reactive protein (CRP) and lipids in patients with stable heart disease. This cross-sectional study included 93 patients with stable coronary artery disease (57 males; mean age: 63.5 ± 9.8 years) who were in outpatient care for at least 6 months. After applying a structured questionnaire, periodontal examinations were performed by two calibrated periodontists in six sites per tooth at all teeth. Blood samples were collected from patients on the day of periodontal examination to determine levels of CRP, lipids, and glycated hemoglobin. Multiple linear regression models were fitted to evaluate the association among different periodontal and blood parameters controlling for sex, body mass index, glycated hemoglobin, use of oral hypoglycemic drugs, and smoking. Overall, the sample presented high levels of periodontal inflammation and tissue breakdown. Unadjusted mean concentrations of triglycerides (TGs), very-low-density lipoprotein cholesterol, and glucose were significantly higher in individuals with severe periodontitis. When multiple linear regression models were applied, number of teeth with clinical attachment loss ≥6 mm and presence of severe periodontitis were significantly associated with higher CRP concentrations. Bleeding on probing was significantly associated with TGs, total cholesterol, and non-high-density lipoprotein cholesterol. In this sample of patients with stable CVD, current periodontal inflammation and tissue breakdown are associated with cardiovascular inflammatory markers, such as CRP and lipid profile.

Xenon elimination kinetics following brief exposure.

PubMed

Schaefer, Maximilian S; Piper, Thomas; Geyer, Hans; Schneemann, Julia; Neukirchen, Martin; Thevis, Mario; Kienbaum, Peter

2017-05-01

Xenon is a modern inhalative anaesthetic with a very low solubility in tissues providing rapid elimination and weaning from anaesthesia. Besides its anaesthetic properties, Xenon promotes the endogenous erythropoietin biosynthesis and thus has been enlisted as prohibited substance by the World Anti-Doping Agency (WADA). For effective doping controls, knowledge about the elimination kinetics of Xenon and the duration of traceability are of particular importance. Seventy-seven full blood samples were obtained from 7 normal weight patients undergoing routine Xenon-based general anaesthesia with a targeted inspiratory concentration of 60% Xenon in oxygen. Samples were taken before and during Xenon inhalation as well as one, two, 4, 8, 16, 24, 32, 40, and 48 h after exposure. Xenon concentrations were assessed in full blood by gas chromatography and triple quadrupole tandem mass spectrometry with a detection limit of 0.25 µmol/L. The elimination of Xenon was characterized by linear regression of log-transformed Xenon blood concentrations, as well as non-linear regression. Xenon exposure yielded maximum concentrations in arterial blood of 1.3 [1.1; 1.6] mmol/L. Xenon was traceable for 24 to 48 h. The elimination profile was characterized by a biphasic pattern with a rapid alpha phase, followed by a slower beta phase showing a first order kinetics (c[Xe] = 69.1e -0.26x , R 2  = 0.83, t 1/2  = 2.7 h). Time in hours after exposure could be estimated by 50*ln(1.39/c[Xe] 0.077 ). Xenon's elimination kinetics is biphasic with a delayed beta phase following a first order kinetics. Xenon can reliably be detected for at least 24 h after brief exposure. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Early pregnancy fasting plasma glucose and lipid concentrations in pregnancy and association to offspring size: a retrospective cohort study.

PubMed

Liu, Bin; Geng, Huizhen; Yang, Juan; Zhang, Ying; Deng, Langhui; Chen, Weiqing; Wang, Zilian

2016-03-17

Hyperlipidemia and high fasting plasma glucose levels at the first prenatal visit (First Visit FPG) are both related to gestational diabetes mellitus, maternal obesity/overweight and fetal overgrowth. The purpose of the present study is to investigate the correlation between First Visit FPG and lipid concentrations, and their potential association with offspring size at delivery. Pregnant women that received regular prenatal care and delivered in our center in 2013 were recruited for the study. Fasting plasma glucose levels were tested at the first prenatal visit (First Visit FPG) and prior to delivery (Before Delivery FPG). HbA1c and lipid profiles were examined at the time of OGTT test. Maternal and neonatal clinical data were collected for analysis. Data was analyzed by independent sample t test, Pearson correlation, and Chi-square test, followed by partial correlation and multiple linear regression analyses to confirm association. Statistical significance level was α =0.05. Analyses were based on 1546 mother-baby pairs. First Visit FPG was not correlated with any lipid parameters after adjusting for maternal pregravid BMI, maternal age and gestational age at First Visit FPG. HbA1c was positively correlated with triglyceride and Apolipoprotein B in the whole cohort and in the NGT group after adjusting for maternal age and maternal BMI at OGTT test. Multiple linear regression analyses showed neonatal birth weight, head circumference and shoulder circumference were all associated with First Visit FPG and triglyceride levels. Fasting plasma glucose at first prenatal visit is not associated with lipid concentrations in mid-pregnancy, but may influence fetal growth together with triglyceride concentration.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, K; Li, X; Liu, B

Purpose: To accurately measure CT bow-tie profiles from various manufacturers and to provide non-proprietary information for CT system modeling. Methods: A GOS-based linear detector (0.8 mm per pixel and 51.2 cm in length) with a fast data sampling speed (0.24 ms/sample) was used to measure the relative profiles of bow-tie filters from a collection of eight CT scanners by three different vendors, GE (LS Xtra, LS VCT, Discovery HD750), Siemens (Sensation 64, Edge, Flash, Force), and Philips (iBrilliance 256). The linear detector was first calibrated for its energy response within typical CT beam quality ranges and compared with an ionmore » chamber and analytical modeling (SPECTRA and TASMIP). A geometrical calibration process was developed to determine key parameters including the distance from the focal spot to the linear detector, the angular increment of the gantry at each data sampling, the location of the central x-ray on the linear detector, and the angular response of the detector pixel. Measurements were performed under axial-scan modes for most representative bow-tie filters and kV selections from each scanner. Bow-tie profiles were determined by re-binning the measured rotational data with an angular accuracy of 0.1 degree using the calibrated geometrical parameters. Results: The linear detector demonstrated an energy response as a solid state detector, which is close to the CT imaging detector. The geometrical calibration was proven to be sufficiently accurate (< 1mm in error for distances >550 mm) and the bow-tie profiles measured from rotational mode matched closely to those from the gantry-stationary mode. Accurate profiles were determined for a total of 21 bow-tie filters and 83 filter/kV combinations from the abovementioned scanner models. Conclusion: A new improved approach of CT bow-tie measurement was proposed and accurate bow-tie profiles were provided for a broad list of CT scanner models.« less

Computer-aided design of high-contact-ratio gears for minimum dynamic load and stress

NASA Technical Reports Server (NTRS)

Lin, Hsiang Hsi; Lee, Chinwai; Oswald, Fred B.; Townsend, Dennis P.

1990-01-01

A computer aided design procedure is presented for minimizing dynamic effects on high contact ratio gears by modification of the tooth profile. Both linear and parabolic tooth profile modifications of high contact ratio gears under various loading conditions are examined and compared. The effects of the total amount of modification and the length of the modification zone were systematically studied at various loads and speeds to find the optimum profile design for minimizing the dynamic load and the tooth bending stress. Parabolic profile modification is preferred over linear profile modification for high contact ratio gears because of its lower sensitivity to manufacturing errors. For parabolic modification, a greater amount of modification at the tooth tip and a longer modification zone are required. Design charts are presented for high contact ratio gears with various profile modifications operating under a range of loads. A procedure is illustrated for using the charts to find the optimum profile design.

A regression technique for evaluation and quantification for water quality parameters from remote sensing data

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.

1979-01-01

The objective of this paper is to define optical physics and/or environmental conditions under which the linear multiple-regression should be applicable. An investigation of the signal-response equations is conducted and the concept is tested by application to actual remote sensing data from a laboratory experiment performed under controlled conditions. Investigation of the signal-response equations shows that the exact solution for a number of optical physics conditions is of the same form as a linearized multiple-regression equation, even if nonlinear contributions from surface reflections, atmospheric constituents, or other water pollutants are included. Limitations on achieving this type of solution are defined.

Estimation of stature using hand and foot dimensions in Slovak adults.

PubMed

Uhrová, Petra; Beňuš, Radoslav; Masnicová, Soňa; Obertová, Zuzana; Kramárová, Daniela; Kyselicová, Klaudia; Dörnhöferová, Michaela; Bodoriková, Silvia; Neščáková, Eva

2015-03-01

Hand and foot dimensions used for stature estimation help to formulate a biological profile in the process of personal identification. Morphological variability of hands and feet shows the importance of generating population-specific equations to estimate stature. The stature, hand length, hand breadth, foot length and foot breadth of 250 young Slovak males and females, aged 18-24 years, were measured according to standard anthropometric procedures. The data were statistically analyzed using independent t-test for sex and bilateral differences. Pearson correlation coefficient was used for assessing relationship between stature and hand/foot parameters, and subsequently linear regression analysis was used to estimate stature. The results revealed significant sex differences in hand and foot dimensions as well as in stature (p<0.05). There was a positive and statistically significant correlation between stature and all measurements in both sexes (p<0.01). The highest correlation coefficient was found for foot length in males (r=0.71) as well as in females (r=0.63). Regression equations were computed separately for each sex. The accuracy of stature prediction ranged from ±4.6 to ±6.1cm. The results of this study indicate that hand and foot dimension can be used to estimate stature for Slovak for the purpose of forensic field. The regression equations can be of use for stature estimation particularly in cases of dismembered bodies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Stretch-dependent changes in surface profiles of the human crystalline lens during accommodation: a finite element study.

PubMed

Pour, Hooman Mohammad; Kanapathipillai, Sangarapillai; Zarrabi, Khosrow; Manns, Fabrice; Ho, Arthur

2015-03-01

A non-linear isotropic finite element (FE) model of a 29-year-old human crystalline lens was constructed to study the effects of various geometrical parameters on lens accommodation. The model simulates dis-accommodation by stretching of the lens and predicts the change in surface profiles of the lens capsule, cortex and nucleus at select states of stretching/accommodation. Multiple regression analysis (MRA) is used to develop a stretch-dependent mathematical model relating the lens sagittal height to the radial position of the lens surface as a function of dis-accommodative stretch. A load analysis is performed to compare the finite element results to empirical results from lens stretcher studies. Using the predicted geometrical changes, the optical response of the whole eye during accommodation was analysed by ray-tracing. Aspects of lens shape change relative to stretch were evaluated, including change in diameter, central thickness and accommodation. Maximum accommodation achieved was 10.29 D. From the multiple regression analysis, the stretch-dependent mathematical model of the lens shape related lens curvatures as a function of lens ciliary stretch well (maximum mean-square residual error 2.5 × 10(-3 ) μm, p < 0.001). The results are compared with those from in vitro studies. The finite element and ray-tracing predictions are consistent with Ex Vivo Accommodation Simulator (EVAS) studies in terms of load and power change versus change in thickness. The mathematical stretch-dependent model of accommodation presented may have utility in investigating lens behaviour at states other than the relaxed or fully accommodated states. © 2015 The Authors. Clinical and Experimental Optometry © 2015 Optometry Australia.

Ideal cardiovascular health and inflammation in European adolescents: The HELENA study.

PubMed

González-Gil, E M; Santabárbara, J; Ruiz, J R; Bel-Serrat, S; Huybrechts, I; Pedrero-Chamizo, R; de la O, A; Gottrand, F; Kafatos, A; Widhalm, K; Manios, Y; Molnar, D; De Henauw, S; Plada, M; Ferrari, M; Palacios Le Blé, G; Siani, A; González-Gross, M; Gómez-Martínez, S; Marcos, A; Moreno Aznar, L A

2017-05-01

Inflammation plays a key role in atherosclerosis and this process seems to appear in childhood. The ideal cardiovascular health index (ICHI) has been inversely related to atherosclerotic plaque in adults. However, evidence regarding inflammation and ICHI in adolescents is scarce. The aim is to assess the association between ICHI and inflammation in European adolescents. As many as 543 adolescents (251 boys and 292 girls) from the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study, a cross-sectional multi-center study including 9 European countries, were measured. C-reactive protein (CRP), complement factors C3 and C4, leptin and white blood cell counts were used to compute an inflammatory score. Multilevel linear models and multilevel logistic regression were used to assess the association between ICHI and inflammation controlling by covariates. Higher ICHI was associated with a lower inflammatory score, as well as with several individual components, both in boys and girls (p < 0.01). In addition, adolescents with at least 4 ideal components of the ICHI had significantly lower inflammatory score and lower levels of the study biomarkers, except CRP. Finally, the multilevel logistic regression showed that for every unit increase in the ICHI, the probability of having an inflammatory profile decreased by 28.1% in girls. Results from this study suggest that a better ICHI is associated with a lower inflammatory profile already in adolescence. Improving these health behaviors, and health factors included in the ICHI, could play an important role in CVD prevention. Copyright © 2016 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization.

PubMed

Bhattacharya, Sayanti; Granger, Christopher B; Craig, Damian; Haynes, Carol; Bain, James; Stevens, Robert D; Hauser, Elizabeth R; Newgard, Christopher B; Kraus, William E; Newby, L Kristin; Shah, Svati H

2014-01-01

To validate independent associations between branched-chain amino acids (BCAA) and other metabolites with coronary artery disease (CAD). We conducted mass-spectrometry-based profiling of 63 metabolites in fasting plasma from 1983 sequential patients undergoing cardiac catheterization. Significant CAD was defined as CADindex ≥ 32 (at least one vessel with ≥ 95% stenosis; N = 995) and no CAD as CADindex ≤ 23 and no previous cardiac events (N = 610). Individuals (N = 378) with CAD severity between these extremes were excluded. Principal components analysis (PCA) reduced large numbers of correlated metabolites into uncorrelated factors. Association between metabolite factors and significant CAD vs. no CAD was tested using logistic regression; and between metabolite factors and severity of CAD was tested using linear regression. Of twelve PCA-derived metabolite factors, two were associated with CAD in multivariable models: factor 10, composed of BCAA (adjusted odds ratio, OR, 1.20; 95% CI 1.05-1.35, p = 0.005) and factor 7, composed of short-chain acylcarnitines, which include byproducts of BCAA metabolism (adjusted OR 1.30; 95% CI 1.14-1.48, p = 0.001). After adjustment for glycated albumin (marker of insulin resistance [IR]) both factors 7 (p = 0.0001) and 10 (p = 0.004) remained associated with CAD. Severity of CAD as a continuous variable (including patients with non-obstructive disease) was associated with metabolite factors 2, 3, 6, 7, 8 and 9; only factors 7 and 10 were associated in multivariable models. We validated the independent association of metabolites involved in BCAA metabolism with CAD extremes. These metabolites may be reporting on novel mechanisms of CAD pathogenesis that are independent of IR and diabetes. Copyright © 2013. Published by Elsevier Ireland Ltd.

Interannual variations of middle atmospheric temperature as measured by the JPL lidar at Mauna Loa Observatory, Hawaii (19.5°N, 155.6°W)

NASA Astrophysics Data System (ADS)

Li, Tao; Leblanc, Thierry; McDermid, I. Stuart

2008-07-01

The Jet Propulsion Laboratory Rayleigh-Raman lidar at Mauna Loa Observatory (MLO), Hawaii (19.5°N, 155.6°W) has been measuring atmospheric temperature vertical profiles routinely since 1993. Linear regression analysis was applied to the 13.5-yearlong (January 1994 to June 2007) deseasonalized monthly mean lidar temperature time series for each 1-km altitude bin between 15 and 85 km. The regression analysis included components representing the Quasi-Biennial Oscillation (QBO), El Niño-Southern Oscillation (ENSO), and the 11-year solar cycle. Where overlapping was possible, the results were compared to those obtained from the twice-daily National Weather Service (NWS) radiosonde profiles at Hilo (5-30 km) located 60 km east-north-east of the lidar site, and the four-times-daily temperature analysis of the European Centre for Medium Range Weather Forecast (ECMWF). The analysis revealed the dominance of the QBO (1-3 K) in the stratosphere and mesosphere, and a strong winter signature of ENSO in the troposphere and lowermost stratosphere (˜1.5 K/MEI). Additionally, and for the first time, a statistically significant signature of ENSO was observed in the mesosphere, consistent with the findings of recent model simulations. The annual mean response to the solar cycle shows two statistically significant maxima of ˜1.3 K/100 F10.7 units at 35 and 55 km. The temperature responses to QBO, ENSO, and solar cycle are all maximized in winter. Comparisons with the global ECMWF temperature analysis clearly showed that the middle atmosphere above MLO is under a subtropical/extratropical regime, i.e., generally out-of-phase with that in the equatorial regions, and synchronized to the northern hemisphere winter/spring.

Relationship between children's performance-based motor skills and child, parent, and teacher perceptions of children's motor abilities using self/informant-report questionnaires.

PubMed

Lalor, Aislinn; Brown, Ted; Murdolo, Yuki

2016-04-01

Occupational therapists often assess the motor skill performance of children referred to them as part of the assessment process. This study investigated whether children's, parents' and teachers' perceptions of children's motor skills using valid and reliable self/informant-report questionnaires were associated with and predictive of children's actual motor performance, as measured by a standardised performance-based motor skill assessment. Fifty-five typically developing children (8-12 years of age), their parents and classroom teachers were recruited to participate in the study. The children completed the Physical Self-Description Questionnaire (PSDQ) and the Self-Perception Profile for Children. The parents completed the Developmental Profile III (DP-III) and the Developmental Coordination Disorder Questionnaire, whereas the teachers completed the Developmental Coordination Disorder Questionnaire and the Teacher's Rating Scale of Child's Actual Behavior. Children's motor performance composite scores were determined using the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2). Spearman's rho correlation coefficients were calculated to identify if significant correlations existed and multiple linear regression was used to identify whether self/informant report data were significant predictors of children's motor skill performance. The child self-report scores had the largest number of significant correlations with the BOT-2 composites. Regression analysis found that the parent report DP-III Physical subscale was a significant predictor of the BOT-2 Manual Coordination composite and the child-report questionnaire PSDQ. Endurance subscale was a significant predictor of the BOT-2 Strength and Agility composite. The findings support the use of top-down assessment methods from a variety of sources when evaluating children's motor abilities. © 2016 Occupational Therapy Australia.

Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.

2017-12-01

During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.

[Quality of life in Latin American immigrant caregivers in Spain].

PubMed

Bover, Andreu; Taltavull, Joana Maria; Gastaldo, Denise; Luengo, Raquel; Izquierdo, María Dolores; Juando-Prats, Clara; Sáenz de Ormijana, Amaia; Robledo, Juana

2015-01-01

To describe perceived quality of life in Latin American caregivers working in Spain and how it varies in relation to certain variables shared by this group. We used the SF-36 to measure perceived quality of life in 517 women residing in five Spanish regions: the Balearic Islands, Catalonia, the Basque Country, the Canary Islands, and Madrid. Several variables related to the socio-demographic profile and migration process were studied using Student's t test, ANOVA and linear regression models. The participants scored very low on the dimensions of physical and emotional roles. The factors associated with lower quality of life scores within the group were working as a live-in caregiver, lack of contract, multitasking, irregular status, and younger age. The vulnerability of these women can be explained by poor working conditions and other factors related to the migratory process. Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.

New observations of molecular nitrogen in the Martian upper atmosphere by IUVS on MAVEN

NASA Astrophysics Data System (ADS)

Stevens, M. H.; Evans, J. S.; Schneider, N. M.; Stewart, A. I. F.; Deighan, J.; Jain, S. K.; Crismani, M.; Stiepen, A.; Chaffin, M. S.; McClintock, W. E.; Holsclaw, G. M.; Lefèvre, F.; Lo, D. Y.; Clarke, J. T.; Montmessin, F.; Bougher, S. W.; Jakosky, B. M.

2015-11-01

We identify molecular nitrogen (N2) emissions in the Martian upper atmosphere using the Imaging Ultraviolet Spectrograph (IUVS) on NASA's Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. We report the first observations of the N2 Lyman-Birge-Hopfield (LBH) bands at Mars and confirm the tentative identification of the N2 Vegard-Kaplan (VK) bands. We retrieve N2 density profiles from the VK limb emissions and compare calculated limb radiances between 90 and 210 km against both observations and predictions from a Mars general circulation model (GCM). Contrary to earlier analyses using other satellite data, we find that N2 abundances exceed GCM results by about a factor of 2 at 130 km but are in agreement at 150 km. The analysis and interpretation are enabled by a linear regression method used to extract components of UV spectra from IUVS limb observations.

Influence of xerostomia on oral health-related quality of life in the elderly: a 5-year longitudinal study.

PubMed

Enoki, Kaori; Matsuda, Ken-Ich; Ikebe, Kazunori; Murai, Shunsuke; Yoshida, Minoru; Maeda, Yoshinobu; Thomson, William Murray

2014-06-01

Xerostomia and tooth loss are major oral health problems in the elderly. The aim of this longitudinal study was to characterize the influence of xerostomia on oral health-related quality of life (OHRQoL) among elderly Japanese people. A total of 99 community-dwelling, independently living individuals aged 60 years and older were interviewed and underwent dental examination at baseline and at a 5-year follow-up. The Oral Health Impact Profile-14 and the Xerostomia Inventory were used to assess OHRQoL and xerostomia severity, respectively. Participants whose xerostomia worsened over the 5-year period had a significantly poorer follow-up OHRQoL. Linear regression models showed that tooth loss and worsening xerostomia were significant predictors of poorer follow-up OHRQoL. Tooth loss and worsening xerostomia result in poorer OHRQoL among older Japanese people. Copyright © 2014 Elsevier Inc. All rights reserved.

Molecular relaxation processes of 2-bromopropane in solutions from IR ν(C-Br) band shape analysis

NASA Astrophysics Data System (ADS)

Bratu, I.; Grecu, R.; Constantinescu, R.; Iliescu, T.

1998-03-01

The infrared (C-Br) stretching band profile of 2-bromopropane in pure liquid and in solution was studied. The frequency shifts, described by the Buckingham equation, account for the influence of the polarity and polarizability of the solvents. To evaluate the importance of the last term in the Buckingham equation, which describes the mutual influence of these two effects, a linear multidimensional regression analysis was done. The correlation factor increased when the cross term was considered. The concentration dependence of the FWHH (full width at half height) can be related to the vibrational relaxation processes, among them vibrational dephasing being the most important. More information about mechanisms responsible for the vibrational bandshape can be obtained from the correlation function Φ( t). As a result of modelling the experimental CF with Kubo-Rothschild's model, the modulation of the vibrational frequencies is found to be of intermediate type.

Mixed convective/dynamic roll vortices and their effects on initial wind and temperature profiles

NASA Technical Reports Server (NTRS)

Haack, Tracy; Shirer, Hampton N.

1991-01-01

The onset and development of both dynamically and convectively forced boundary layer rolls are studied with linear and nonlinear analyses of a truncated spectral model of shallow Boussinesq flow. Emphasis is given here on the energetics of the dominant roll modes, on the magnitudes of the roll-induced modifications of the initial basic state wind and temperature profiles, and on the sensitivity of the linear stability results to the use of modified profiles as basic states. It is demonstrated that the roll circulations can produce substantial changes to the cross-roll component of the initial wind profile and that significant changes in orientation angle estimates can result from use of a roll-modified profile in the stability analysis. These results demonstrate that roll contributions must be removed from observed background wind profiles before using them to investigate the mechanisms underlying actual secondary flows in the boundary layer. The model is developed quite generally to accept arbitrary basic state wind profiles as dynamic forcing. An Ekman profile is chosen here merely to provide a means for easy comparison with other theoretical boundary layer studies; the ultimate application of the model is to study observed boundary layer profiles. Results of the analytic stability analysis are validated by comparing them with results from a larger linear model. For an appropriate Ekman depth, a complete set of transition curves is given in forcing parameter space for roll modes driven both thermally and dynamically. Preferred orientation angles, horizontal wavelengths and propagation frequencies, as well as energetics and wind profile modifications, are all shown to agree rather well with results from studies on Ekman layers as well as with studies on near-neutral and convective atmospheric boundary layers.

Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

DTIC Science & Technology

1981-09-01

corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

Study on power grid characteristics in summer based on Linear regression analysis

NASA Astrophysics Data System (ADS)

Tang, Jin-hui; Liu, You-fei; Liu, Juan; Liu, Qiang; Liu, Zhuan; Xu, Xi

2018-05-01

The correlation analysis of power load and temperature is the precondition and foundation for accurate load prediction, and a great deal of research has been made. This paper constructed the linear correlation model between temperature and power load, then the correlation of fault maintenance work orders with the power load is researched. Data details of Jiangxi province in 2017 summer such as temperature, power load, fault maintenance work orders were adopted in this paper to develop data analysis and mining. Linear regression models established in this paper will promote electricity load growth forecast, fault repair work order review, distribution network operation weakness analysis and other work to further deepen the refinement.

Profiling of barrier capacitance and spreading resistance using a transient linearly increasing voltage technique.

PubMed

Gaubas, E; Ceponis, T; Kusakovskij, J

2011-08-01

A technique for the combined measurement of barrier capacitance and spreading resistance profiles using a linearly increasing voltage pulse is presented. The technique is based on the measurement and analysis of current transients, due to the barrier and diffusion capacitance, and the spreading resistance, between a needle probe and sample. To control the impact of deep traps in the barrier capacitance, a steady state bias illumination with infrared light was employed. Measurements of the spreading resistance and barrier capacitance profiles using a stepwise positioned probe on cross sectioned silicon pin diodes and pnp structures are presented.

Scarp degraded by linear diffusion: inverse solution for age.

USGS Publications Warehouse

Andrews, D.J.; Hanks, T.C.

1985-01-01

Under the assumption that landforms unaffected by drainage channels are degraded according to the linear diffusion equation, a procedure is developed to invert a scarp profile to find its 'diffusion age'. The inverse procedure applied to synthetic data yields the following rules of thumb. Evidence of initial scarp shape has been lost when apparent age reaches twice its initial value. A scarp that appears to have been formed by one event may have been formed by two with an interval between them as large as apparent age. The simplicity of scarp profile measurement and this inversion makes profile analysis attractive. -from Authors

Interpreting Regression Results: beta Weights and Structure Coefficients are Both Important.

ERIC Educational Resources Information Center

Thompson, Bruce

Various realizations have led to less frequent use of the "OVA" methods (analysis of variance--ANOVA--among others) and to more frequent use of general linear model approaches such as regression. However, too few researchers understand all the various coefficients produced in regression. This paper explains these coefficients and their…

Spatial Assessment of Model Errors from Four Regression Techniques

Treesearch

Lianjun Zhang; Jeffrey H. Gove; Jeffrey H. Gove

2005-01-01

Fomst modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographicalIy weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study...

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Comparing global-scale topographic and climatic metrics to long-term erosion rates using ArcSwath, an efficient new ArcGIS tool for swath profile analysis

NASA Astrophysics Data System (ADS)

Blomqvist, Niclas; Whipp, David

2016-04-01

The topography of the Earth's surface is the result of the interaction of tectonics, erosion and climate. Thus, topography should contain a record of these processes that can be extracted by topographic analysis. The question considered in this study is whether the spatial variations in erosion that have sculpted the modern topography are representative of the long-term erosion rates in mountainous regions. We compare long-term erosion rates derived from low-temperature thermochronometry to erosional proxies calculated from topographic and climatic data analysis. The study has been performed on a global scale including six orogens: The Himalaya, Andes, Taiwan, Olympic Mountains, Southern Alps in New Zealand and European Alps. The data was analyzed using a new swath profile analysis tool for ArcGIS called ArcSwath (https://github.com/HUGG/ArcSwath) to determine the correlations between the long-term erosion rates and modern elevations, slope angles, relief in 2.5-km- and 5-km-diameter circles, erosion potential, normalized channel steepness index ksn, and annual rainfall. ArcSwath uses a Python script that has been incorporated into an ArcMap 10.2 add-in tool, extracting swath profiles in about ten seconds compared to earlier workflows that could take more than an hour. In ArcMap, UTM-projected point or raster files can be used for creating swath profiles. Point data are projected onto the swath and the statistical parameters (minimum, mean and maximum of the values across the swath) are calculated for the raster data. Both can be immediately plotted using the Python matplotlib library, or plotted externally using the csv-file that is produced by ArcSwath. When raster and point data are plotted together, it is easier to make comparisons and see correlations between the selected data. An unambiguous correlation between the topographic or climatic metrics and long-term erosion rates was not found. Fitting of linear regression lines to the topographic/ climatic metric data and the long-term erosion rates shows that 86 of 288 plots (30%) have "good" R2 values (> 0.35) and 135 of 288 (47%) have an "acceptable" R2 value (> 0.2). The "good" and "acceptable" values have been selected on the basis of visual fit to the regression line. The majority of the plots with a "good" correlation value have positive correlations, while 11/86 plots have negative slopes for the regression lines. Interestingly, two topographic profile shapes were clear in swath profiles: Concave-up (e.g., the central-western Himalaya and the northern Bolivian Andes) and concave-down or straight (e.g., the eastern Himalayas and the southern Bolivian Andes). On the orogen scale, the concave-up shape is often related to relatively high precipitation and erosion rates on the slopes of steep topography. The concave-down/straight profiles seem to occur in association of low rainfall and/or erosion rates. Though we cannot say with confidence, the lack of a clear correlation between long-term erosion rates and climate or topography may be due to the difference in their respective timescales as climate can vary over shorter timescales than 105-107 years. In that case, variations between fluvial and glacial erosion may have overprinted the erosional effects of one another.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Regression analysis using dependent Polya trees.

PubMed

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

Expression signature as a biomarker for prenatal diagnosis of trisomy 21.

PubMed

Volk, Marija; Maver, Aleš; Lovrečić, Luca; Juvan, Peter; Peterlin, Borut

2013-01-01

A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker's high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal and reactive cellular mechanisms, transcriptomic changes may thus have future potential in the diagnosis of a wide array of heterogeneous diseases that result from genetic disturbances.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

A New SEYHAN's Approach in Case of Heterogeneity of Regression Slopes in ANCOVA.

PubMed

Ankarali, Handan; Cangur, Sengul; Ankarali, Seyit

2018-06-01

In this study, when the assumptions of linearity and homogeneity of regression slopes of conventional ANCOVA are not met, a new approach named as SEYHAN has been suggested to use conventional ANCOVA instead of robust or nonlinear ANCOVA. The proposed SEYHAN's approach involves transformation of continuous covariate into categorical structure when the relationship between covariate and dependent variable is nonlinear and the regression slopes are not homogenous. A simulated data set was used to explain SEYHAN's approach. In this approach, we performed conventional ANCOVA in each subgroup which is constituted according to knot values and analysis of variance with two-factor model after MARS method was used for categorization of covariate. The first model is a simpler model than the second model that includes interaction term. Since the model with interaction effect has more subjects, the power of test also increases and the existing significant difference is revealed better. We can say that linearity and homogeneity of regression slopes are not problem for data analysis by conventional linear ANCOVA model by helping this approach. It can be used fast and efficiently for the presence of one or more covariates.

The Influential Effect of Blending, Bump, Changing Period, and Eclipsing Cepheids on the Leavitt Law

NASA Astrophysics Data System (ADS)

García-Varela, A.; Muñoz, J. R.; Sabogal, B. E.; Vargas Domínguez, S.; Martínez, J.

2016-06-01

The investigation of the nonlinearity of the Leavitt law (LL) is a topic that began more than seven decades ago, when some of the studies in this field found that the LL has a break at about 10 days. The goal of this work is to investigate a possible statistical cause of this nonlinearity. By applying linear regressions to OGLE-II and OGLE-IV data, we find that to obtain the LL by using linear regression, robust techniques to deal with influential points and/or outliers are needed instead of the ordinary least-squares regression traditionally used. In particular, by using M- and MM-regressions we establish firmly and without doubt the linearity of the LL in the Large Magellanic Cloud, without rejecting or excluding Cepheid data from the analysis. This implies that light curves of Cepheids suggesting blending, bumps, eclipses, or period changes do not affect the LL for this galaxy. For the Small Magellanic Cloud, when including Cepheids of this kind, it is not possible to find an adequate model, probably because of the geometry of the galaxy. In that case, a possible influence of these stars could exist.

Multiple regression technique for Pth degree polynominals with and without linear cross products

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Linear and non-linear regression analysis for the sorption kinetics of methylene blue onto activated carbon.

PubMed

Kumar, K Vasanth

2006-10-11

Batch kinetic experiments were carried out for the sorption of methylene blue onto activated carbon. The experimental kinetics were fitted to the pseudo first-order and pseudo second-order kinetics by linear and a non-linear method. The five different types of Ho pseudo second-order expression have been discussed. A comparison of linear least-squares method and a trial and error non-linear method of estimating the pseudo second-order rate kinetic parameters were examined. The sorption process was found to follow a both pseudo first-order kinetic and pseudo second-order kinetic model. Present investigation showed that it is inappropriate to use a type 1 and type pseudo second-order expressions as proposed by Ho and Blanachard et al. respectively for predicting the kinetic rate constants and the initial sorption rate for the studied system. Three correct possible alternate linear expressions (type 2 to type 4) to better predict the initial sorption rate and kinetic rate constants for the studied system (methylene blue/activated carbon) was proposed. Linear method was found to check only the hypothesis instead of verifying the kinetic model. Non-linear regression method was found to be the more appropriate method to determine the rate kinetic parameters.

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Associations between Verbal Learning Slope and Neuroimaging Markers across the Cognitive Aging Spectrum.

PubMed

Gifford, Katherine A; Phillips, Jeffrey S; Samuels, Lauren R; Lane, Elizabeth M; Bell, Susan P; Liu, Dandan; Hohman, Timothy J; Romano, Raymond R; Fritzsche, Laura R; Lu, Zengqi; Jefferson, Angela L

2015-07-01

A symptom of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is a flat learning profile. Learning slope calculation methods vary, and the optimal method for capturing neuroanatomical changes associated with MCI and early AD pathology is unclear. This study cross-sectionally compared four different learning slope measures from the Rey Auditory Verbal Learning Test (simple slope, regression-based slope, two-slope method, peak slope) to structural neuroimaging markers of early AD neurodegeneration (hippocampal volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral prefrontal cortex) across the cognitive aging spectrum [normal control (NC); (n=198; age=76±5), MCI (n=370; age=75±7), and AD (n=171; age=76±7)] in ADNI. Within diagnostic group, general linear models related slope methods individually to neuroimaging variables, adjusting for age, sex, education, and APOE4 status. Among MCI, better learning performance on simple slope, regression-based slope, and late slope (Trial 2-5) from the two-slope method related to larger parahippocampal thickness (all p-values<.01) and hippocampal volume (p<.01). Better regression-based slope (p<.01) and late slope (p<.01) were related to larger ventrolateral prefrontal cortex in MCI. No significant associations emerged between any slope and neuroimaging variables for NC (p-values ≥.05) or AD (p-values ≥.02). Better learning performances related to larger medial temporal lobe (i.e., hippocampal volume, parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI only. Regression-based and late slope were most highly correlated with neuroimaging markers and explained more variance above and beyond other common memory indices, such as total learning. Simple slope may offer an acceptable alternative given its ease of calculation.

Gestational dating by metabolic profile at birth: a California cohort study.

PubMed

Jelliffe-Pawlowski, Laura L; Norton, Mary E; Baer, Rebecca J; Santos, Nicole; Rutherford, George W

2016-04-01

Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11-20 weeks' gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset. When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Gestational dating by metabolic profile at birth: a California cohort study

PubMed Central

Jelliffe-Pawlowski, Laura L.; Norton, Mary E.; Baer, Rebecca J.; Santos, Nicole; Rutherford, George W.

2016-01-01

Background Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. Objective We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. Study Design We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11–20 weeks’ gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Results Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset. Conclusion When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care. PMID:26688490

An evaluation of bias in propensity score-adjusted non-linear regression models.

PubMed

Wan, Fei; Mitra, Nandita

2018-03-01

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

Quantitative sensory testing profiles in children, adolescents and young adults (6-20 years) with cerebral palsy: Hints for a neuropathic genesis of pain syndromes.

PubMed

Blankenburg, M; Junker, J; Hirschfeld, G; Michel, E; Aksu, F; Wager, J; Zernikow, B

2018-05-01

Many patients with cerebral palsy (CP) suffer chronic pain as one of the most limiting factors in their quality of life. In CP patients, pain mechanisms are not well understood, and pain therapy remains a challenge. Quantitative sensory testing (QST) might provide unique information about the functional status of the somatosensory system and therefore better guide pain treatment. To understand better the underlying pain mechanisms in pediatric CP patients, we aimed to assess clinical and pain parameters, as well as QST profiles, which were matched to the patients' cerebral imaging pathology. Thirty CP patients aged 6-20 years old (mean age 12 years) without intellectual impairment underwent standardized assessments of QST. Cerebral imaging was reassessed. QST results were compared to age- and sex-matched controls (multiple linear regression; Fisher's exact test; linear correlation analysis). CP patients were less sensitive to all mechanical and thermal stimuli than healthy controls but more sensitive to all mechanical pain stimuli (each p < 0.001). Fifty percent of CP patients showed a combination of mechanical hypoesthesia, thermal hypoesthesia and mechanical hyperalgesia; 67% of CP patients had periventricular leukomalacia (PVL), which was correlated with mechanic (r = 0.661; p < 0.001) and thermal (r = 0.624; p = 0.001) hypoesthesia. The combination of mechanical hypoesthesia, thermal hypoesthesia and mechanical hyperalgesia in our CP patients implicates lemniscal and extralemniscal neuron dysfunction in the thalamus region, likely due to PVL. We suspect that extralemniscal tracts are involved in the original of pain in our CP patients, as in adults. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Simultaneous separation of antihyperlipidemic drugs by green ultrahigh-performance liquid chromatography-diode array detector method: Improving the health of liquid chromatography.

PubMed

Alghazi, Mansoor; Alanazi, Fars; Mohsin, Kazi; Siddiqui, Nasir Ali; Shakeel, Faiyaz; Haq, Nazrul

2017-04-01

Statins in combination with fibrates show beneficial effects on the lipoprotein profile of patients because they have positive complimentary effects on lipid profile. A new green ultrahigh-performance liquid chromatography-diode array detector method for simultaneous analysis of simvastatin (SMV) and fenofibrate (FNF) in standard form, marketed formulations, and self-emulsifying drug delivery system formulations was developed and validated in the present investigation. The method utilized C 18 as stationary phase and a combination of methanol:water (8:2) as an eluent. It was found that selected eluent provided short run time (2.5 minutes), better peak symmetry and satisfactory values of other chromatographic parameters such as resolution (Rs=2.325), capacity factor (k, 3.0 and 4.2 for SMV and FNF, respectively), selectivity (α =1.4), and number of theoretical plates (N, 4265 and 5285 for SMV and FNF, respectively). An excellent linear relationship (r 2 0.998 and 0.997 for SMV and FNF, respectively) was observed for linear regression data for the calibration plots. The developed system was validated for accuracy, precision, robustness (˃ 2% for both drugs) and recovery (98-102% for both drugs). Results obtained from the statistical treatment of the values obtained for different parameters proved that the method is suitable, reproducible, and selective for the simultaneous analysis of SMV and FNF in bulk, marketed, and self-emulsifying drug delivery system formulations. The replacement of commonly applied toxic solvents with innocuous and environmentally benign solvents provides a better option than the more toxic processes in drug analysis. Copyright © 2016. Published by Elsevier B.V.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

Postmolar gestational trophoblastic neoplasia: beyond the traditional risk factors.

PubMed

Bakhtiyari, Mahmood; Mirzamoradi, Masoumeh; Kimyaiee, Parichehr; Aghaie, Abbas; Mansournia, Mohammd Ali; Ashrafi-Vand, Sepideh; Sarfjoo, Fatemeh Sadat

2015-09-01

To investigate the slope of linear regression of postevacuation serum hCG as an independent risk factor for postmolar gestational trophoblastic neoplasia (GTN). Multicenter retrospective cohort study. Academic referral health care centers. All subjects with confirmed hydatidiform mole and at least four measurements of β-hCG titer. None. Type and magnitude of the relationship between the slope of linear regression of β-hCG as a new risk factor and GTN using Bayesian logistic regression with penalized log-likelihood estimation. Among the high-risk and low-risk molar pregnancy cases, 11 (18.6%) and 19 cases (13.3%) had GTN, respectively. No significant relationship was found between the components of a high-risk pregnancy and GTN. The β-hCG return slope was higher in the spontaneous cure group. However, the initial level of this hormone in the first measurement was higher in the GTN group compared with in the spontaneous recovery group. The average time for diagnosing GTN in the high-risk molar pregnancy group was 2 weeks less than that of the low-risk molar pregnancy group. In addition to slope of linear regression of β-hCG (odds ratio [OR], 12.74, confidence interval [CI], 5.42-29.2), abortion history (OR, 2.53; 95% CI, 1.27-5.04) and large uterine height for gestational age (OR, 1.26; CI, 1.04-1.54) had the maximum effects on GTN outcome, respectively. The slope of linear regression of β-hCG was introduced as an independent risk factor, which could be used for clinical decision making based on records of β-hCG titer and subsequent prevention program. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Tolerance of ciliated protozoan Paramecium bursaria (Protozoa, Ciliophora) to ammonia and nitrites

NASA Astrophysics Data System (ADS)

Xu, Henglong; Song, Weibo; Lu, Lu; Alan, Warren

2005-09-01

The tolerance to ammonia and nitrites in freshwater ciliate Paramecium bursaria was measured in a conventional open system. The ciliate was exposed to different concentrations of ammonia and nitrites for 2h and 12h in order to determine the lethal concentrations. Linear regression analysis revealed that the 2h-LC50 value for ammonia was 95.94 mg/L and for nitrite 27.35 mg/L using probit scale method (with 95% confidence intervals). There was a linear correlation between the mortality probit scale and logarithmic concentration of ammonia which fit by a regression equation y=7.32 x 9.51 ( R 2=0.98; y, mortality probit scale; x, logarithmic concentration of ammonia), by which 2 h-LC50 value for ammonia was found to be 95.50 mg/L. A linear correlation between mortality probit scales and logarithmic concentration of nitrite is also followed the regression equation y=2.86 x+0.89 ( R 2=0.95; y, mortality probit scale; x, logarithmic concentration of nitrite). The regression analysis of toxicity curves showed that the linear correlation between exposed time of ammonia-N LC50 value and ammonia-N LC50 value followed the regression equation y=2 862.85 e -0.08 x ( R 2=0.95; y, duration of exposure to LC50 value; x, LC50 value), and that between exposed time of nitrite-N LC50 value and nitrite-N LC50 value followed the regression equation y=127.15 e -0.13 x ( R 2=0.91; y, exposed time of LC50 value; x, LC50 value). The results demonstrate that the tolerance to ammonia in P. bursaria is considerably higher than that of the larvae or juveniles of some metozoa, e.g. cultured prawns and oysters. In addition, ciliates, as bacterial predators, are likely to play a positive role in maintaining and improving water quality in aquatic environments with high-level ammonium, such as sewage treatment systems.

GWAS with longitudinal phenotypes: performance of approximate procedures

PubMed Central

Sikorska, Karolina; Montazeri, Nahid Mostafavi; Uitterlinden, André; Rivadeneira, Fernando; Eilers, Paul HC; Lesaffre, Emmanuel

2015-01-01

Analysis of genome-wide association studies with longitudinal data using standard procedures, such as linear mixed model (LMM) fitting, leads to discouragingly long computation times. There is a need to speed up the computations significantly. In our previous work (Sikorska et al: Fast linear mixed model computations for genome-wide association studies with longitudinal data. Stat Med 2012; 32.1: 165–180), we proposed the conditional two-step (CTS) approach as a fast method providing an approximation to the P-value for the longitudinal single-nucleotide polymorphism (SNP) effect. In the first step a reduced conditional LMM is fit, omitting all the SNP terms. In the second step, the estimated random slopes are regressed on SNPs. The CTS has been applied to the bone mineral density data from the Rotterdam Study and proved to work very well even in unbalanced situations. In another article (Sikorska et al: GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics 2013; 14: 166), we suggested semi-parallel computations, greatly speeding up fitting many linear regressions. Combining CTS with fast linear regression reduces the computation time from several weeks to a few minutes on a single computer. Here, we explore further the properties of the CTS both analytically and by simulations. We investigate the performance of our proposal in comparison with a related but different approach, the two-step procedure. It is analytically shown that for the balanced case, under mild assumptions, the P-value provided by the CTS is the same as from the LMM. For unbalanced data and in realistic situations, simulations show that the CTS method does not inflate the type I error rate and implies only a minimal loss of power. PMID:25712081

Local linear regression for function learning: an analysis based on sample discrepancy.

PubMed

Cervellera, Cristiano; Macciò, Danilo

2014-11-01

Local linear regression models, a kind of nonparametric structures that locally perform a linear estimation of the target function, are analyzed in the context of empirical risk minimization (ERM) for function learning. The analysis is carried out with emphasis on geometric properties of the available data. In particular, the discrepancy of the observation points used both to build the local regression models and compute the empirical risk is considered. This allows to treat indifferently the case in which the samples come from a random external source and the one in which the input space can be freely explored. Both consistency of the ERM procedure and approximating capabilities of the estimator are analyzed, proving conditions to ensure convergence. Since the theoretical analysis shows that the estimation improves as the discrepancy of the observation points becomes smaller, low-discrepancy sequences, a family of sampling methods commonly employed for efficient numerical integration, are also analyzed. Simulation results involving two different examples of function learning are provided.

Adaptive local linear regression with application to printer color management.

PubMed

Gupta, Maya R; Garcia, Eric K; Chin, Erika

2008-06-01

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

PubMed

Agha, Salah R; Alnahhal, Mohammed J

2012-11-01

The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

PubMed

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

TG study of the Li0.4Fe2.4Zn0.2O4 ferrite synthesis

NASA Astrophysics Data System (ADS)

Lysenko, E. N.; Nikolaev, E. V.; Surzhikov, A. P.

2016-02-01

In this paper, the kinetic analysis of Li-Zn ferrite synthesis was studied using thermogravimetry (TG) method through the simultaneous application of non-linear regression to several measurements run at different heating rates (multivariate non-linear regression). Using TG-curves obtained for the four heating rates and Netzsch Thermokinetics software package, the kinetic models with minimal adjustable parameters were selected to quantitatively describe the reaction of Li-Zn ferrite synthesis. It was shown that the experimental TG-curves clearly suggest a two-step process for the ferrite synthesis and therefore a model-fitting kinetic analysis based on multivariate non-linear regressions was conducted. The complex reaction was described by a two-step reaction scheme consisting of sequential reaction steps. It is established that the best results were obtained using the Yander three-dimensional diffusion model at the first stage and Ginstling-Bronstein model at the second step. The kinetic parameters for lithium-zinc ferrite synthesis reaction were found and discussed.