Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Comparing and Contrasting Neural Net Solutions to Classical Statistical Solutions.

ERIC Educational Resources Information Center

Van Nelson, C.; Neff, Kathryn J.

Data from two studies in which subjects were classified as successful or unsuccessful were analyzed using neural net technology after being analyzed with a linear regression function. Data were obtained from admission records of 201 students admitted to undergraduate and 285 students admitted to graduate programs. Data included grade point…

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

An algebraic method for constructing stable and consistent autoregressive filters

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

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

NASA Astrophysics Data System (ADS)

Srimani, P. K.; Parimala, Y. G.

2011-12-01

A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

Structured functional additive regression in reproducing kernel Hilbert spaces.

PubMed

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2014-06-01

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Penalized nonparametric scalar-on-function regression via principal coordinates

PubMed Central

Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

2016-01-01

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

The swan-song phenomenon: last-works effects for 172 classical composers.

PubMed

Simonton, D K

1989-03-01

Creative individuals approaching their final years of life may undergo a transformation in outlook that is reflected in their last works. This hypothesized effect was quantitatively assessed for an extensive sample of 1,919 works by 172 classical composers. The works were independently gauged on seven aesthetic attributes (melodic originality, melodic variation, repertoire popularity, aesthetic significance, listener accessibility, performance duration, and thematic size), and potential last-works effects were operationally defined two separate ways (linearly and exponentially). Statistical controls were introduced for both longitudinal changes (linear, quadratic, and cubic age functions) and individual differences (eminence and lifetime productivity). Hierarchical regression analyses indicated that composers' swan songs tend to score lower in melodic originality and performance duration but higher in repertoire popularity and aesthetic significance. These last-works effects survive control for total compositional output, eminence, and most significantly, the composer's age when the last works were created.

Public and Private School Distinction, Regional Development Differences, and Other Factors Influencing the Success of Primary School Students in Turkey

ERIC Educational Resources Information Center

Sulku, Seher Nur; Abdioglu, Zehra

2015-01-01

This study investigates the factors influencing the success of students in primary schools in Turkey. TIMSS 2011 data for Turkey, measuring the success of eighth-grade students in the field of mathematics, were used in an econometric analysis, performed using classical linear regression models. Two hundred thirty-nine schools participated in the…

The Weight of Euro Coins: Its Distribution Might Not Be as Normal as You Would Expect

ERIC Educational Resources Information Center

Shkedy, Ziv; Aerts, Marc; Callaert, Herman

2006-01-01

Classical regression models, ANOVA models and linear mixed models are just three examples (out of many) in which the normal distribution of the response is an essential assumption of the model. In this paper we use a dataset of 2000 euro coins containing information (up to the milligram) about the weight of each coin, to illustrate that the…

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.

Structured functional additive regression in reproducing kernel Hilbert spaces

PubMed Central

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2013-01-01

Summary Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application. PMID:25013362

Quantum algorithm for linear regression

NASA Astrophysics Data System (ADS)

Wang, Guoming

2017-07-01

We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

Analytic Methods for Adjusting Subjective Rating Schemes

DTIC Science & Technology

1976-06-01

individual performance. The approach developed here is a variant of the classical linear regression model. Specifically, it la proposed that...values of y and X. Moreover, this difference la gener- ally independent of sample size, so that LS estimates are different from ML estimates at...baervationa. H^ever, aa T. -. - ,„ aU . th(. Hit (4.10) la aatlafled, and EKV and ML eatlnatea are equlvalent A practical proble, in applying

Glomerular structural-functional relationship models of diabetic nephropathy are robust in type 1 diabetic patients.

PubMed

Mauer, Michael; Caramori, Maria Luiza; Fioretto, Paola; Najafian, Behzad

2015-06-01

Studies of structural-functional relationships have improved understanding of the natural history of diabetic nephropathy (DN). However, in order to consider structural end points for clinical trials, the robustness of the resultant models needs to be verified. This study examined whether structural-functional relationship models derived from a large cohort of type 1 diabetic (T1D) patients with a wide range of renal function are robust. The predictability of models derived from multiple regression analysis and piecewise linear regression analysis was also compared. T1D patients (n = 161) with research renal biopsies were divided into two equal groups matched for albumin excretion rate (AER). Models to explain AER and glomerular filtration rate (GFR) by classical DN lesions in one group (T1D-model, or T1D-M) were applied to the other group (T1D-test, or T1D-T) and regression analyses were performed. T1D-M-derived models explained 70 and 63% of AER variance and 32 and 21% of GFR variance in T1D-M and T1D-T, respectively, supporting the substantial robustness of the models. Piecewise linear regression analyses substantially improved predictability of the models with 83% of AER variance and 66% of GFR variance explained by classical DN glomerular lesions alone. These studies demonstrate that DN structural-functional relationship models are robust, and if appropriate models are used, glomerular lesions alone explain a major proportion of AER and GFR variance in T1D patients. © The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

2001-10-01

- The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Predicting musically induced emotions from physiological inputs: linear and neural network models.

PubMed

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Generalized Onsager's reciprocal relations for the master and Fokker-Planck equations

NASA Astrophysics Data System (ADS)

Peng, Liangrong; Zhu, Yi; Hong, Liu

2018-06-01

The Onsager's reciprocal relation plays a fundamental role in the nonequilibrium thermodynamics. However, unfortunately, its classical version is valid only within a narrow region near equilibrium due to the linear regression hypothesis, which largely restricts its usage. In this paper, based on the conservation-dissipation formalism, a generalized version of Onsager's relations for the master equations and Fokker-Planck equations was derived. Nonlinear constitutive relations with nonsymmetric and positively stable operators, which become symmetric under the detailed balance condition, constitute key features of this new generalization. Similar conclusions also hold for many other classical models in physics and chemistry, which in turn make the current study as a benchmark for the application of generalized Onsager's relations in nonequilibrium thermodynamics.

Enhancement of partial robust M-regression (PRM) performance using Bisquare weight function

NASA Astrophysics Data System (ADS)

Mohamad, Mazni; Ramli, Norazan Mohamed; Ghani@Mamat, Nor Azura Md; Ahmad, Sanizah

2014-09-01

Partial Least Squares (PLS) regression is a popular regression technique for handling multicollinearity in low and high dimensional data which fits a linear relationship between sets of explanatory and response variables. Several robust PLS methods are proposed to accommodate the classical PLS algorithms which are easily affected with the presence of outliers. The recent one was called partial robust M-regression (PRM). Unfortunately, the use of monotonous weighting function in the PRM algorithm fails to assign appropriate and proper weights to large outliers according to their severity. Thus, in this paper, a modified partial robust M-regression is introduced to enhance the performance of the original PRM. A re-descending weight function, known as Bisquare weight function is recommended to replace the fair function in the PRM. A simulation study is done to assess the performance of the modified PRM and its efficiency is also tested in both contaminated and uncontaminated simulated data under various percentages of outliers, sample sizes and number of predictors.

Functional mixture regression.

PubMed

Yao, Fang; Fu, Yuejiao; Lee, Thomas C M

2011-04-01

In functional linear models (FLMs), the relationship between the scalar response and the functional predictor process is often assumed to be identical for all subjects. Motivated by both practical and methodological considerations, we relax this assumption and propose a new class of functional regression models that allow the regression structure to vary for different groups of subjects. By projecting the predictor process onto its eigenspace, the new functional regression model is simplified to a framework that is similar to classical mixture regression models. This leads to the proposed approach named as functional mixture regression (FMR). The estimation of FMR can be readily carried out using existing software implemented for functional principal component analysis and mixture regression. The practical necessity and performance of FMR are illustrated through applications to a longevity analysis of female medflies and a human growth study. Theoretical investigations concerning the consistent estimation and prediction properties of FMR along with simulation experiments illustrating its empirical properties are presented in the supplementary material available at Biostatistics online. Corresponding results demonstrate that the proposed approach could potentially achieve substantial gains over traditional FLMs.

Convert a low-cost sensor to a colorimeter using an improved regression method

NASA Astrophysics Data System (ADS)

Wu, Yifeng

2008-01-01

Closed loop color calibration is a process to maintain consistent color reproduction for color printers. To perform closed loop color calibration, a pre-designed color target should be printed, and automatically measured by a color measuring instrument. A low cost sensor has been embedded to the printer to perform the color measurement. A series of sensor calibration and color conversion methods have been developed. The purpose is to get accurate colorimetric measurement from the data measured by the low cost sensor. In order to get high accuracy colorimetric measurement, we need carefully calibrate the sensor, and minimize all possible errors during the color conversion. After comparing several classical color conversion methods, a regression based color conversion method has been selected. The regression is a powerful method to estimate the color conversion functions. But the main difficulty to use this method is to find an appropriate function to describe the relationship between the input and the output data. In this paper, we propose to use 1D pre-linearization tables to improve the linearity between the input sensor measuring data and the output colorimetric data. Using this method, we can increase the accuracy of the regression method, so as to improve the accuracy of the color conversion.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Oxygen Costs of the Incremental Shuttle Walk Test in Cardiac Rehabilitation Participants: An Historical and Contemporary Analysis.

PubMed

Buckley, John P; Cardoso, Fernando M F; Birkett, Stefan T; Sandercock, Gavin R H

2016-12-01

The incremental shuttle walk test (ISWT) is a standardised assessment for cardiac rehabilitation. Three studies have reported oxygen costs (VO 2 )/metabolic equivalents (METs) of the ISWT. In spite of classic representations from these studies graphically showing curvilinear VO 2 responses to incremented walking speeds, linear regression techniques (also used by the American College of Sports Medicine [ACSM]) have been used to estimate VO 2 . The two main aims of this study were to (i) resolve currently reported discrepancies in the ISWT VO 2 -walking speed relationship, and (ii) derive an appropriate VO 2 versus walking speed regression equation. VO 2 was measured continuously during an ISWT in 32 coronary heart disease [cardiac] rehabilitation (CHD-CR) participants and 30 age-matched controls. Both CHD-CR and control group VO 2 responses were curvilinear in nature. For CHD-CR VO 2  = 4.4e 0.23 × walkingspeed (km/h) . The integrated area under the curve (iAUC) VO 2 across nine ISWT stages was greater in the CHD-CR group versus the control group (p < 0.001): CHD-CR = 423 (±86) ml·kg -1 ·min -1 ·km·h -1 ; control = 316 (±52) ml·kg -1 ·min -1 ·km·h -1 . CHD-CR group vs. control VO 2 was up to 30 % greater at higher ISWT stages. The curvilinear nature of VO 2 responses during the ISWT concur with classic studies reported over 100 years. VO 2 estimates for walking using linear regression models (including the ACSM) clearly underestimate values in healthy and CHD-CR participants, and this study provides a resolution to this when the ISWT is used for CHD-CR populations.

Linear and nonlinear spectroscopy from quantum master equations.

PubMed

Fetherolf, Jonathan H; Berkelbach, Timothy C

2017-12-28

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

Linear and nonlinear spectroscopy from quantum master equations

NASA Astrophysics Data System (ADS)

Fetherolf, Jonathan H.; Berkelbach, Timothy C.

2017-12-01

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences

PubMed Central

Feingold, Alan

2013-01-01

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen’s d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration. PMID:23956615

Healthy life expectancy in Hong Kong Special Administrative Region of China.

PubMed Central

Law, C. K.; Yip, P. S. F.

2003-01-01

Sullivan's method and a regression model were used to calculate healthy life expectancy (HALE) for men and women in Hong Kong Special Administrative Region (Hong Kong SAR) of China. These methods need estimates of the prevalence and information on disability distributions of 109 diseases and HALE for 191 countries by age, sex and region of the world from the WHO's health assessment of 2000. The population of Hong Kong SAR has one of the highest healthy life expectancies in the world. Sullivan's method gives higher estimates than the classic linear regression method. Although Sullivan's method accurately calculates the influence of disease prevalence within small areas and regions, the regression method can approximate HALE for all economies for which information on life expectancy is available. This paper identifies some problems of the two methods and discusses the accuracy of estimates of HALE that rely on data from the WHO assessment. PMID:12640475

Notes on power of normality tests of error terms in regression models

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

Střelec, Luboš

2015-03-10

Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importancemore » of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models.« less

A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data.

PubMed

Lartillot, Nicolas

2014-02-15

Correlation between life history or ecological traits and genomic features such as nucleotide or amino acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be seen as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters coevolving along the phylogeny. Here, a Bayesian sampler is introduced, representing an alternative and more efficient algorithmic solution to this comparative regression problem, compared with currently existing generalized least-square approaches. Technically, ancestral trait reconstruction based on a molecular predictor is shown to be formally equivalent to a phylogenetic Kalman filter problem, for which backward and forward recursions are developed and implemented in the context of a Markov chain Monte Carlo sampler. The comparative regression method results in more accurate reconstructions and a more faithful representation of uncertainty, compared with simple linear regression. Application to the reconstruction of the evolution of optimal growth temperature in Archaea, using GC composition in ribosomal RNA stems and amino acid composition of a sample of protein-coding genes, confirms previous findings, in particular, pointing to a hyperthermophilic ancestor for the kingdom. The program is freely available at www.phylobayes.org.

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

NASA Astrophysics Data System (ADS)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

Detection of epistatic effects with logic regression and a classical linear regression model.

PubMed

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Weibull Modulus Estimated by the Non-linear Least Squares Method: A Solution to Deviation Occurring in Traditional Weibull Estimation

NASA Astrophysics Data System (ADS)

Li, T.; Griffiths, W. D.; Chen, J.

2017-11-01

The Maximum Likelihood method and the Linear Least Squares (LLS) method have been widely used to estimate Weibull parameters for reliability of brittle and metal materials. In the last 30 years, many researchers focused on the bias of Weibull modulus estimation, and some improvements have been achieved, especially in the case of the LLS method. However, there is a shortcoming in these methods for a specific type of data, where the lower tail deviates dramatically from the well-known linear fit in a classic LLS Weibull analysis. This deviation can be commonly found from the measured properties of materials, and previous applications of the LLS method on this kind of dataset present an unreliable linear regression. This deviation was previously thought to be due to physical flaws ( i.e., defects) contained in materials. However, this paper demonstrates that this deviation can also be caused by the linear transformation of the Weibull function, occurring in the traditional LLS method. Accordingly, it may not be appropriate to carry out a Weibull analysis according to the linearized Weibull function, and the Non-linear Least Squares method (Non-LS) is instead recommended for the Weibull modulus estimation of casting properties.

Linear Quantum Systems: Non-Classical States and Robust Stability

DTIC Science & Technology

2016-06-29

quantum linear systems subject to non-classical quantum fields. The major outcomes of this project are (i) derivation of quantum filtering equations for...derivation of quantum filtering equations for systems non-classical input states including single photon states, (ii) determination of how linear...history going back some 50 years, to the birth of modern control theory with Kalman’s foundational work on filtering and LQG optimal control

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Regression Model Optimization for the Analysis of Experimental Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2009-01-01

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

The Biharmonic Oscillator and Asymmetric Linear Potentials: From Classical Trajectories to Momentum-Space Probability Densities in the Extreme Quantum Limit

ERIC Educational Resources Information Center

Ruckle, L. J.; Belloni, M.; Robinett, R. W.

2012-01-01

The biharmonic oscillator and the asymmetric linear well are two confining power-law-type potentials for which complete bound-state solutions are possible in both classical and quantum mechanics. We examine these problems in detail, beginning with studies of their trajectories in position and momentum space, evaluation of the classical probability…

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

PubMed

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

Experimental quantum computing to solve systems of linear equations.

PubMed

Cai, X-D; Weedbrook, C; Su, Z-E; Chen, M-C; Gu, Mile; Zhu, M-J; Li, Li; Liu, Nai-Le; Lu, Chao-Yang; Pan, Jian-Wei

2013-06-07

Solving linear systems of equations is ubiquitous in all areas of science and engineering. With rapidly growing data sets, such a task can be intractable for classical computers, as the best known classical algorithms require a time proportional to the number of variables N. A recently proposed quantum algorithm shows that quantum computers could solve linear systems in a time scale of order log(N), giving an exponential speedup over classical computers. Here we realize the simplest instance of this algorithm, solving 2×2 linear equations for various input vectors on a quantum computer. We use four quantum bits and four controlled logic gates to implement every subroutine required, demonstrating the working principle of this algorithm.

Linear Regression Quantile Mapping (RQM) - A new approach to bias correction with consistent quantile trends

NASA Astrophysics Data System (ADS)

Passow, Christian; Donner, Reik

2017-04-01

Quantile mapping (QM) is an established concept that allows to correct systematic biases in multiple quantiles of the distribution of a climatic observable. It shows remarkable results in correcting biases in historical simulations through observational data and outperforms simpler correction methods which relate only to the mean or variance. Since it has been shown that bias correction of future predictions or scenario runs with basic QM can result in misleading trends in the projection, adjusted, trend preserving, versions of QM were introduced in the form of detrended quantile mapping (DQM) and quantile delta mapping (QDM) (Cannon, 2015, 2016). Still, all previous versions and applications of QM based bias correction rely on the assumption of time-independent quantiles over the investigated period, which can be misleading in the context of a changing climate. Here, we propose a novel combination of linear quantile regression (QR) with the classical QM method to introduce a consistent, time-dependent and trend preserving approach of bias correction for historical and future projections. Since QR is a regression method, it is possible to estimate quantiles in the same resolution as the given data and include trends or other dependencies. We demonstrate the performance of the new method of linear regression quantile mapping (RQM) in correcting biases of temperature and precipitation products from historical runs (1959 - 2005) of the COSMO model in climate mode (CCLM) from the Euro-CORDEX ensemble relative to gridded E-OBS data of the same spatial and temporal resolution. A thorough comparison with established bias correction methods highlights the strengths and potential weaknesses of the new RQM approach. References: A.J. Cannon, S.R. Sorbie, T.Q. Murdock: Bias Correction of GCM Precipitation by Quantile Mapping - How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6038, 2015 A.J. Cannon: Multivariate Bias Correction of Climate Model Outputs - Matching Marginal Distributions and Inter-variable Dependence Structure. Journal of Climate, 29, 7045, 2016

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.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

Identifying risk sources of air contamination by polycyclic aromatic hydrocarbons.

PubMed

Huzlik, Jiri; Bozek, Frantisek; Pawelczyk, Adam; Licbinsky, Roman; Naplavova, Magdalena; Pondelicek, Michael

2017-09-01

This article is directed to determining concentrations of polycyclic aromatic hydrocarbons (PAHs), which are sorbed to solid particles in the air. Pollution sources were identified on the basis of the ratio of benzo[ghi]perylene (BghiPe) to benzo[a]pyrene (BaP). Because various important information is lost by determining the simple ratio of concentrations, least squares linear regression (classic ordinary least squares regression), reduced major axis, orthogonal regression, and Kendall-Theil robust diagnostics were utilized for identification. Statistical evaluation using all aforementioned methods demonstrated different ratios of the monitored PAHs in the intervals examined during warmer and colder periods. Analogous outputs were provided by comparing gradients of the emission factors acquired from the measured concentrations of BghiPe and BaP in motor vehicle exhaust gases. Based on these outputs, it was possible plausibly to state that the influence of burning organic fuels in heating stoves is prevalent in colder periods whereas in warmer periods transport was the exclusive source because other sources of PAH emissions were not found in the examined locations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Burnout does not help predict depression among French school teachers.

PubMed

Bianchi, Renzo; Schonfeld, Irvin Sam; Laurent, Eric

2015-11-01

Burnout has been viewed as a phase in the development of depression. However, supportive research is scarce. We examined whether burnout predicted depression among French school teachers. We conducted a 2-wave, 21-month study involving 627 teachers (73% female) working in French primary and secondary schools. Burnout was assessed with the Maslach Burnout Inventory and depression with the 9-item depression module of the Patient Health Questionnaire (PHQ-9). The PHQ-9 grades depressive symptom severity and provides a provisional diagnosis of major depression. Depression was treated both as a continuous and categorical variable using linear and logistic regression analyses. We controlled for gender, age, and length of employment. Controlling for baseline depressive symptoms, linear regression analysis showed that burnout symptoms at time 1 (T1) did not predict depressive symptoms at time 2 (T2). Baseline depressive symptoms accounted for about 88% of the association between T1 burnout and T2 depressive symptoms. Only baseline depressive symptoms predicted depressive symptoms at follow-up. Similarly, logistic regression analysis revealed that burnout symptoms at T1 did not predict incident cases of major depression at T2 when depressive symptoms at T1 were included in the predictive model. Only baseline depressive symptoms predicted cases of major depression at follow-up. This study does not support the view that burnout is a phase in the development of depression. Assessing burnout symptoms in addition to "classical" depressive symptoms may not always improve our ability to predict future depression.

Quantifying the statistical importance of utilizing regression over classic energy intensity calculations for tracking efficiency improvements in industry

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

Nimbalkar, Sachin U.; Wenning, Thomas J.; Guo, Wei

In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero,more » which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.« less

Development and application of a local linearization algorithm for the integration of quaternion rate equations in real-time flight simulation problems

NASA Technical Reports Server (NTRS)

Barker, L. E., Jr.; Bowles, R. L.; Williams, L. H.

1973-01-01

High angular rates encountered in real-time flight simulation problems may require a more stable and accurate integration method than the classical methods normally used. A study was made to develop a general local linearization procedure of integrating dynamic system equations when using a digital computer in real-time. The procedure is specifically applied to the integration of the quaternion rate equations. For this application, results are compared to a classical second-order method. The local linearization approach is shown to have desirable stability characteristics and gives significant improvement in accuracy over the classical second-order integration methods.

Linearization of digital derived rate algorithm for use in linear stability analysis

NASA Technical Reports Server (NTRS)

Graham, R. E.; Porada, T. W.

1985-01-01

The digital derived rate (DDR) algorithm is used to calculate the rate of rotation of the Centaur upper-stage rocket. The DDR is highly nonlinear algorithm, and classical linear stability analysis of the spacecraft cannot be performed without linearization. The performance of this rate algorithm is characterized by a gain and phase curve that drop off at the same frequency. This characteristic is desirable for many applications. A linearization technique for the DDR algorithm is investigated. The linearization method is described. Examples of the results of the linearization technique are illustrated, and the effects of linearization are described. A linear digital filter may be used as a substitute for performing classical linear stability analyses, while the DDR itself may be used in time response analysis.

Integrability and correspondence of classical and quantum non-linear three-mode systems

NASA Astrophysics Data System (ADS)

Odzijewicz, A.; Wawreniuk, E.

2018-04-01

The relationship between classical and quantum three one-mode systems interacting in a non-linear way is described. We investigate the integrability of these systems by using the reduction procedure. The reduced coherent states for the quantum system are constructed. We find the explicit formulas for the reproducing measure for these states. Examples of some applications of the obtained results in non-linear quantum optics are presented.

Plasmon mass scale and quantum fluctuations of classical fields on a real time lattice

NASA Astrophysics Data System (ADS)

Kurkela, Aleksi; Lappi, Tuomas; Peuron, Jarkko

2018-03-01

Classical real-time lattice simulations play an important role in understanding non-equilibrium phenomena in gauge theories and are used in particular to model the prethermal evolution of heavy-ion collisions. Above the Debye scale the classical Yang-Mills (CYM) theory can be matched smoothly to kinetic theory. First we study the limits of the quasiparticle picture of the CYM fields by determining the plasmon mass of the system using 3 different methods. Then we argue that one needs a numerical calculation of a system of classical gauge fields and small linearized fluctuations, which correspond to quantum fluctuations, in a way that keeps the separation between the two manifest. We demonstrate and test an implementation of an algorithm with the linearized fluctuation showing that the linearization indeed works and that the Gauss's law is conserved.

Circular Regression in a Dual-Phase Lock-In Amplifier for Coherent Detection of Weak Signal

PubMed Central

Wang, Gaoxuan; Reboul, Serge; Fertein, Eric

2017-01-01

Lock-in amplification (LIA) is an effective approach for recovery of weak signal buried in noise. Determination of the input signal amplitude in a classical dual-phase LIA is based on incoherent detection which leads to a biased estimation at low signal-to-noise ratio. This article presents, for the first time to our knowledge, a new architecture of LIA involving phase estimation with a linear-circular regression for coherent detection. The proposed phase delay estimate, between the input signal and a reference, is defined as the maximum-likelihood of a set of observations distributed according to a von Mises distribution. In our implementation this maximum is obtained with a Newton Raphson algorithm. We show that the proposed LIA architecture provides an unbiased estimate of the input signal amplitude. Theoretical simulations with synthetic data demonstrate that the classical LIA estimates are biased for SNR of the input signal lower than −20 dB, while the proposed LIA is able to accurately recover the weak signal amplitude. The novel approach is applied to an optical sensor for accurate measurement of NO2 concentrations at the sub-ppbv level in the atmosphere. Side-by-side intercomparison measurements with a commercial LIA (SR830, Stanford Research Inc., Sunnyvale, CA, USA ) demonstrate that the proposed LIA has an identical performance in terms of measurement accuracy and precision but with simplified hardware architecture. PMID:29135951

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.

Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

PubMed

Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

2011-04-01

The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF clusters. © Georg Thieme Verlag KG Stuttgart · New York.

Radar modulation classification using time-frequency representation and nonlinear regression

NASA Astrophysics Data System (ADS)

De Luigi, Christophe; Arques, Pierre-Yves; Lopez, Jean-Marc; Moreau, Eric

1999-09-01

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Note: Wide-operating-range control for thermoelectric coolers.

PubMed

Peronio, P; Labanca, I; Ghioni, M; Rech, I

2017-11-01

A new algorithm for controlling the temperature of a thermoelectric cooler is proposed. Unlike a classic proportional-integral-derivative (PID) control, which computes the bias voltage from the temperature error, the proposed algorithm exploits the linear relation that exists between the cold side's temperature and the amount of heat that is removed per unit time. Since this control is based on an existing linear relation, it is insensitive to changes in the operating point that are instead crucial in classic PID control of a non-linear system.

Note: Wide-operating-range control for thermoelectric coolers

NASA Astrophysics Data System (ADS)

Peronio, P.; Labanca, I.; Ghioni, M.; Rech, I.

2017-11-01

A new algorithm for controlling the temperature of a thermoelectric cooler is proposed. Unlike a classic proportional-integral-derivative (PID) control, which computes the bias voltage from the temperature error, the proposed algorithm exploits the linear relation that exists between the cold side's temperature and the amount of heat that is removed per unit time. Since this control is based on an existing linear relation, it is insensitive to changes in the operating point that are instead crucial in classic PID control of a non-linear system.

Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX.

PubMed

Oh, Eric J; Shepherd, Bryan E; Lumley, Thomas; Shaw, Pamela A

2018-04-15

For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 common regression models, the Cox and Weibull models, in the setting of measurement error in the failure time outcome. We introduce an extension of the SIMEX method to correct for bias in hazard ratio estimates from the Cox model and discuss other analysis options to address measurement error in the response. A formula to estimate the bias induced into the hazard ratio by classical measurement error in the event time for a log-linear survival model is presented. Detailed numerical studies are presented to examine the performance of the proposed SIMEX method under varying levels and parametric forms of the error in the outcome. We further illustrate the method with observational data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic. Copyright © 2017 John Wiley & Sons, Ltd.

Non-linear quantum-classical scheme to simulate non-equilibrium strongly correlated fermionic many-body dynamics

PubMed Central

Kreula, J. M.; Clark, S. R.; Jaksch, D.

2016-01-01

We propose a non-linear, hybrid quantum-classical scheme for simulating non-equilibrium dynamics of strongly correlated fermions described by the Hubbard model in a Bethe lattice in the thermodynamic limit. Our scheme implements non-equilibrium dynamical mean field theory (DMFT) and uses a digital quantum simulator to solve a quantum impurity problem whose parameters are iterated to self-consistency via a classically computed feedback loop where quantum gate errors can be partly accounted for. We analyse the performance of the scheme in an example case. PMID:27609673

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.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets.

PubMed

Chen, Jie-Hao; Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets

PubMed Central

Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%. PMID:29209363

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prognostic scores in oesophageal or gastric variceal bleeding.

PubMed

Ohmann, C; Stöltzing, H; Wins, L; Busch, E; Thon, K

1990-05-01

Numerous scoring systems have been developed for the prediction of outcome of variceal bleeding; however, only a few have been evaluated adequately. The object of this study was to improve the classical Child-Pugh score (CPS) and to test other scores from the literature. Patients (n = 82) with endoscopically confirmed variceal bleeding and long-term sclerotherapy were included in the study. Linear logistic regression (LR) was applied to different sets of prognostic variables with regard to 30-day mortality. In addition, scores from the literature were evaluated on the data set. Performance was measured by the accuracy and receiver-operating characteristic curves. The application of LR to all five CPS variables (accuracy, 80%) was superior to the classical CPS (70%). LR with selection from the CPS variables or from other sets of variables resulted in no improvement. Compared with CPS only three scores from the literature, mainly based on subsets of the CPS variables, showed an improved accuracy. It is concluded that CPS is still a good scoring system; however, it can be improved by statistical analysis using the same variables.

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.

Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model

PubMed Central

Valeri, Linda; Lin, Xihong; VanderWeele, Tyler J.

2014-01-01

Mediation analysis is a popular approach to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. When the mediator is mis-measured the validity of mediation analysis can be severely undermined. In this paper we first study the bias of classical, non-differential measurement error on a continuous mediator in the estimation of direct and indirect causal effects in generalized linear models when the outcome is either continuous or discrete and exposure-mediator interaction may be present. Our theoretical results as well as a numerical study demonstrate that in the presence of non-linearities the bias of naive estimators for direct and indirect effects that ignore measurement error can take unintuitive directions. We then develop methods to correct for measurement error. Three correction approaches using method of moments, regression calibration and SIMEX are compared. We apply the proposed method to the Massachusetts General Hospital lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk. PMID:25220625

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

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…

Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research.

PubMed

Leung, Michael; Bassani, Diego G; Racine-Poon, Amy; Goldenberg, Anna; Ali, Syed Asad; Kang, Gagandeep; Premkumar, Prasanna S; Roth, Daniel E

2017-09-10

Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the "conditional random slope" (CRS) model, based on a linear-mixed effects model that incorporates a baseline-time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation (r = 0.65-0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38-0.78). Estimates of the velocity-stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses. © 2017 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc.

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

Bukhvostov-Lipatov model and quantum-classical duality

NASA Astrophysics Data System (ADS)

Bazhanov, Vladimir V.; Lukyanov, Sergei L.; Runov, Boris A.

2018-02-01

The Bukhvostov-Lipatov model is an exactly soluble model of two interacting Dirac fermions in 1 + 1 dimensions. The model describes weakly interacting instantons and anti-instantons in the O (3) non-linear sigma model. In our previous work [arxiv:arXiv:1607.04839] we have proposed an exact formula for the vacuum energy of the Bukhvostov-Lipatov model in terms of special solutions of the classical sinh-Gordon equation, which can be viewed as an example of a remarkable duality between integrable quantum field theories and integrable classical field theories in two dimensions. Here we present a complete derivation of this duality based on the classical inverse scattering transform method, traditional Bethe ansatz techniques and analytic theory of ordinary differential equations. In particular, we show that the Bethe ansatz equations defining the vacuum state of the quantum theory also define connection coefficients of an auxiliary linear problem for the classical sinh-Gordon equation. Moreover, we also present details of the derivation of the non-linear integral equations determining the vacuum energy and other spectral characteristics of the model in the case when the vacuum state is filled by 2-string solutions of the Bethe ansatz equations.

Quasi-Static Analysis of Round LaRC THUNDER Actuators

NASA Technical Reports Server (NTRS)

Campbell, Joel F.

2007-01-01

An analytic approach is developed to predict the shape and displacement with voltage in the quasi-static limit of round LaRC Thunder Actuators. The problem is treated with classical lamination theory and Von Karman non-linear analysis. In the case of classical lamination theory exact analytic solutions are found. It is shown that classical lamination theory is insufficient to describe the physical situation for large actuators but is sufficient for very small actuators. Numerical results are presented for the non-linear analysis and compared with experimental measurements. Snap-through behavior, bifurcation, and stability are presented and discussed.

Quasi-Static Analysis of LaRC THUNDER Actuators

NASA Technical Reports Server (NTRS)

Campbell, Joel F.

2007-01-01

An analytic approach is developed to predict the shape and displacement with voltage in the quasi-static limit of LaRC Thunder Actuators. The problem is treated with classical lamination theory and Von Karman non-linear analysis. In the case of classical lamination theory exact analytic solutions are found. It is shown that classical lamination theory is insufficient to describe the physical situation for large actuators but is sufficient for very small actuators. Numerical results are presented for the non-linear analysis and compared with experimental measurements. Snap-through behavior, bifurcation, and stability are presented and discussed.

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…

Coherent-state constellations and polar codes for thermal Gaussian channels

NASA Astrophysics Data System (ADS)

Lacerda, Felipe; Renes, Joseph M.; Scholz, Volkher B.

2017-06-01

Optical communication channels are ultimately quantum mechanical in nature, and we must therefore look beyond classical information theory to determine their communication capacity as well as to find efficient encoding and decoding schemes of the highest rates. Thermal channels, which arise from linear coupling of the field to a thermal environment, are of particular practical relevance; their classical capacity has been recently established, but their quantum capacity remains unknown. While the capacity sets the ultimate limit on reliable communication rates, it does not promise that such rates are achievable by practical means. Here we construct efficiently encodable codes for thermal channels which achieve the classical capacity and the so-called Gaussian coherent information for transmission of classical and quantum information, respectively. Our codes are based on combining polar codes with a discretization of the channel input into a finite "constellation" of coherent states. Encoding of classical information can be done using linear optics.

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.

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

PubMed Central

Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J.

2018-01-01

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. PMID:29538331

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

PubMed

Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

2018-03-14

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

A new model-free index of dynamic cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Panerai, Ronney B

2014-01-01

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free. This new index uses two parameters that are obtained directly from the response signal of the cerebral blood flow velocity to a transient decrease in arterial blood pressure provoked by the sudden release of bilateral thigh cuffs, and a third parameter measuring the difference in slope of this response and the change in arterial blood pressure achieved. With the values of these parameters, a corresponding classic autoregulatory index value could be calculated by using a linear regression model built from theoretical curves generated with the Aaslid-Tiecks model. In 16 healthy subjects who underwent repeated thigh-cuff manoeuvres, the model-free approach exhibited significantly lower intra-subject variability, as measured by the unbiased coefficient of variation, than the classic autoregulatory index (p = 0.032) and the Rate of Return (p<0.001), another measure of cerebral autoregulation used for this type of systemic pressure stimulus, from 39.23%±41.91% and 55.31%±31.27%, respectively, to 15.98%±7.75%.

A New Model-Free Index of Dynamic Cerebral Blood Flow Autoregulation

PubMed Central

Chacón, Max; Jara, José Luis; Panerai, Ronney B.

2014-01-01

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free. This new index uses two parameters that are obtained directly from the response signal of the cerebral blood flow velocity to a transient decrease in arterial blood pressure provoked by the sudden release of bilateral thigh cuffs, and a third parameter measuring the difference in slope of this response and the change in arterial blood pressure achieved. With the values of these parameters, a corresponding classic autoregulatory index value could be calculated by using a linear regression model built from theoretical curves generated with the Aaslid-Tiecks model. In 16 healthy subjects who underwent repeated thigh-cuff manoeuvres, the model-free approach exhibited significantly lower intra-subject variability, as measured by the unbiased coefficient of variation, than the classic autoregulatory index (p = 0.032) and the Rate of Return (p<0.001), another measure of cerebral autoregulation used for this type of systemic pressure stimulus, from 39.23%±41.91% and 55.31%±31.27%, respectively, to 15.98%±7.75%. PMID:25313519

Disorder-dominated linear magnetoresistance in topological insulator Bi2Se3 thin films

NASA Astrophysics Data System (ADS)

Wang, Wen Jie; Gao, Kuang Hong; Li, Qiu Lin; Li, Zhi-Qing

2017-12-01

The linear magnetoresistance (MR) effect is an interesting topic due to its potential applications. In topological insulator Bi2Se3, this effect has been reported to be dominated by the carrier mobility (μ) and hence has a classical origin. Here, we study the magnetotransport properties of Bi2Se3 thin films and observe the linear MR effect, which cannot be attributed to the quantum model. Unexpectedly, the linear MR does not show the linear dependence on μ, in conflict with the reported results. However, we find that the observed linear MR is dominated by the inverse disorder parameter 1 /kFl , where kF and l are the Fermi wave vector and the mean free path, respectively. This suggests that its origin is also classical and that no μ-dominated linear MR effect is observed which may be due to the very small μ values in our samples.

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

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.

Linearly first- and second-order, unconditionally energy stable schemes for the phase field crystal model

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

Yang, Xiaofeng, E-mail: xfyang@math.sc.edu; Han, Daozhi, E-mail: djhan@iu.edu

2017-02-01

In this paper, we develop a series of linear, unconditionally energy stable numerical schemes for solving the classical phase field crystal model. The temporal discretizations are based on the first order Euler method, the second order backward differentiation formulas (BDF2) and the second order Crank–Nicolson method, respectively. The schemes lead to linear elliptic equations to be solved at each time step, and the induced linear systems are symmetric positive definite. We prove that all three schemes are unconditionally energy stable rigorously. Various classical numerical experiments in 2D and 3D are performed to validate the accuracy and efficiency of the proposedmore » schemes.« less

Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy

NASA Astrophysics Data System (ADS)

Provazza, Justin; Coker, David F.

2018-05-01

The symmetrical quasi-classical approach for propagation of a many degree of freedom density matrix is explored in the context of computing linear spectra. Calculations on a simple two state model for which exact results are available suggest that the approach gives a qualitative description of peak positions, relative amplitudes, and line broadening. Short time details in the computed dipole autocorrelation function result in exaggerated tails in the spectrum.

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

Time evolution of linearized gauge field fluctuations on a real-time lattice

NASA Astrophysics Data System (ADS)

Kurkela, A.; Lappi, T.; Peuron, J.

2016-12-01

Classical real-time lattice simulations play an important role in understanding non-equilibrium phenomena in gauge theories and are used in particular to model the prethermal evolution of heavy-ion collisions. Due to instabilities, small quantum fluctuations on top of the classical background may significantly affect the dynamics of the system. In this paper we argue for the need for a numerical calculation of a system of classical gauge fields and small linearized fluctuations in a way that keeps the separation between the two manifest. We derive and test an explicit algorithm to solve these equations on the lattice, maintaining gauge invariance and Gauss' law.

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.…

Simulation-extrapolation method to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates, 1950-2003.

PubMed

Allodji, Rodrigue S; Schwartz, Boris; Diallo, Ibrahima; Agbovon, Césaire; Laurier, Dominique; de Vathaire, Florent

2015-08-01

Analyses of the Life Span Study (LSS) of Japanese atomic bombing survivors have routinely incorporated corrections for additive classical measurement errors using regression calibration. Recently, several studies reported that the efficiency of the simulation-extrapolation method (SIMEX) is slightly more accurate than the simple regression calibration method (RCAL). In the present paper, the SIMEX and RCAL methods have been used to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates. For instance, it is shown that using the SIMEX method, the ERR/Gy is increased by an amount of about 29 % for all solid cancer deaths using a linear model compared to the RCAL method, and the corrected EAR 10(-4) person-years at 1 Gy (the linear terms) is decreased by about 8 %, while the corrected quadratic term (EAR 10(-4) person-years/Gy(2)) is increased by about 65 % for leukaemia deaths based on a linear-quadratic model. The results with SIMEX method are slightly higher than published values. The observed differences were probably due to the fact that with the RCAL method the dosimetric data were partially corrected, while all doses were considered with the SIMEX method. Therefore, one should be careful when comparing the estimated risks and it may be useful to use several correction techniques in order to obtain a range of corrected estimates, rather than to rely on a single technique. This work will enable to improve the risk estimates derived from LSS data, and help to make more reliable the development of radiation protection standards.

Exotic states of matter with polariton chains

NASA Astrophysics Data System (ADS)

Kalinin, Kirill P.; Lagoudakis, Pavlos G.; Berloff, Natalia G.

2018-04-01

We consider linear periodic chains of exciton-polariton condensates formed by pumping polaritons nonresonantly into a linear network. To the leading order such a sequence of condensates establishes relative phases as to minimize a classical one-dimensional X Y Hamiltonian with nearest and next-to-nearest neighbors. We show that the low-energy states of polaritonic linear chains demonstrate various classical regimes: ferromagnetic, antiferromagnetic, and frustrated spiral phases where quantum or thermal fluctuations are expected to give rise to a spin-liquid state. At the same time nonlinear interactions at higher pumping intensities bring about phase chaos and novel exotic phases.

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...

Application of singular value decomposition to structural dynamics systems with constraints

NASA Technical Reports Server (NTRS)

Juang, J.-N.; Pinson, L. D.

1985-01-01

Singular value decomposition is used to construct a coordinate transformation for a linear dynamic system subject to linear, homogeneous constraint equations. The method is compared with two commonly used methods, namely classical Gaussian elimination and Walton-Steeves approach. Although the classical method requires fewer numerical operations, the singular value decomposition method is more accurate and convenient in eliminating the dependent coordinates. Numerical examples are presented to demonstrate the application of the method.

Classical and quantum communication without a shared reference frame.

PubMed

Bartlett, Stephen D; Rudolph, Terry; Spekkens, Robert W

2003-07-11

We show that communication without a shared reference frame is possible using entangled states. Both classical and quantum information can be communicated with perfect fidelity without a shared reference frame at a rate that asymptotically approaches one classical bit or one encoded qubit per transmitted qubit. We present an optical scheme to communicate classical bits without a shared reference frame using entangled photon pairs and linear optical Bell state measurements.

Spectrophotometric evaluation of stability constants of 1:1 weak complexes from continuous variation data.

PubMed

Sayago, Ana; Asuero, Agustin G

2006-09-14

A bilogarithmic hyperbolic cosine method for the spectrophotometric evaluation of stability constants of 1:1 weak complexes from continuous variation data has been devised and applied to literature data. A weighting scheme, however, is necessary in order to take into account the transformation for linearization. The method may be considered a useful alternative to methods in which one variable is involved on both sides of the basic equation (i.e. Heller and Schwarzenbach, Likussar and Adsul and Ramanathan). Classical least squares lead in those instances to biased and approximate stability constants and limiting absorbance values. The advantages of the proposed method are: the method gives a clear indication of the existence of only one complex in solution, it is flexible enough to allow for weighting of measurements and the computation procedure yield the best value of logbeta11 and its limit of error. The agreement between the values obtained by applying the weighted hyperbolic cosine method and the non-linear regression (NLR) method is good, being in both cases the mean quadratic error at a minimum.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Confidence in Altman-Bland plots: a critical review of the method of differences.

PubMed

Ludbrook, John

2010-02-01

1. Altman and Bland argue that the virtue of plotting differences against averages in method-comparison studies is that 95% confidence limits for the differences can be constructed. These allow authors and readers to judge whether one method of measurement could be substituted for another. 2. The technique is often misused. So I have set out, by statistical argument and worked examples, to advise pharmacologists and physiologists how best to construct these limits. 3. First, construct a scattergram of differences on averages, then calculate the line of best fit for the linear regression of differences on averages. If the slope of the regression is shown to differ from zero, there is proportional bias. 4. If there is no proportional bias and if the scatter of differences is uniform (homoscedasticity), construct 'classical' 95% confidence limits. 5. If there is proportional bias yet homoscedasticity, construct hyperbolic 95% confidence limits (prediction interval) around the line of best fit. 6. If there is proportional bias and the scatter of values for differences increases progressively as the average values increase (heteroscedasticity), log-transform the raw values from the two methods and replot differences against averages. If this eliminates proportional bias and heteroscedasticity, construct 'classical' 95% confidence limits. Otherwise, construct horizontal V-shaped 95% confidence limits around the line of best fit of differences on averages or around the weighted least products line of best fit to the original data. 7. In designing a method-comparison study, consult a qualified biostatistician, obey the rules of randomization and make replicate observations.

[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.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

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…

Cumulants, free cumulants and half-shuffles

PubMed Central

Ebrahimi-Fard, Kurusch; Patras, Frédéric

2015-01-01

Free cumulants were introduced as the proper analogue of classical cumulants in the theory of free probability. There is a mix of similarities and differences, when one considers the two families of cumulants. Whereas the combinatorics of classical cumulants is well expressed in terms of set partitions, that of free cumulants is described and often introduced in terms of non-crossing set partitions. The formal series approach to classical and free cumulants also largely differs. The purpose of this study is to put forward a different approach to these phenomena. Namely, we show that cumulants, whether classical or free, can be understood in terms of the algebra and combinatorics underlying commutative as well as non-commutative (half-)shuffles and (half-) unshuffles. As a corollary, cumulants and free cumulants can be characterized through linear fixed point equations. We study the exponential solutions of these linear fixed point equations, which display well the commutative, respectively non-commutative, character of classical and free cumulants. PMID:27547078

Precise measurement of coupling strength and high temperature quantum effect in a nonlinearly coupled qubit-oscillator system

NASA Astrophysics Data System (ADS)

Ge, Li; Zhao, Nan

2018-04-01

We study the coherence dynamics of a qubit coupled to a harmonic oscillator with both linear and quadratic interactions. As long as the linear coupling strength is much smaller than the oscillator frequency, the long time behavior of the coherence is dominated by the quadratic coupling strength g 2. The coherence decays and revives at a period , with the width of coherence peak decreasing as the temperature increases, hence providing a way to measure g 2 precisely without cooling. Unlike the case of linear coupling, here the coherence dynamics never reduces to the classical limit in which the oscillator is classical. Finally, the validity of linear coupling approximation is discussed and the coherence under Hahn-echo is evaluated.

The complexity of classical music networks

NASA Astrophysics Data System (ADS)

Rolla, Vitor; Kestenberg, Juliano; Velho, Luiz

2018-02-01

Previous works suggest that musical networks often present the scale-free and the small-world properties. From a musician's perspective, the most important aspect missing in those studies was harmony. In addition to that, the previous works made use of outdated statistical methods. Traditionally, least-squares linear regression is utilised to fit a power law to a given data set. However, according to Clauset et al. such a traditional method can produce inaccurate estimates for the power law exponent. In this paper, we present an analysis of musical networks which considers the existence of chords (an essential element of harmony). Here we show that only 52.5% of music in our database presents the scale-free property, while 62.5% of those pieces present the small-world property. Previous works argue that music is highly scale-free; consequently, it sounds appealing and coherent. In contrast, our results show that not all pieces of music present the scale-free and the small-world properties. In summary, this research is focused on the relationship between musical notes (Do, Re, Mi, Fa, Sol, La, Si, and their sharps) and accompaniment in classical music compositions. More information about this research project is available at https://eden.dei.uc.pt/~vitorgr/MS.html.

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).

Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

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…

Linear Quantum Systems: Non-Classical States and Robust Stability

DTIC Science & Technology

2016-06-29

has a history going back some 50 years, to the birth of modern control theory with Kalman’s foundational work on filtering and LQG optimal control ...information   if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD...analysis and control of quantum linear systems and their interactions with non-classical quantum fields by developing control theoretic concepts exploiting

Specificity vs. Generalizability: Emergence of Especial Skills in Classical Archery

PubMed Central

Czyż, Stanisław H.; Moss, Sarah J.

2016-01-01

There is evidence that the recall schema becomes more refined after constant practice. It is also believed that massive amounts of constant practice eventually leads to the emergence of especial skills, i.e., skills that have an advantage in performance over other actions from within the same class of actions. This advantage in performance was noticed when one-criterion practice, e.g., basketball free throws, was compared to non-practiced variations of the skill. However, there is no evidence whether multi-criterion massive amounts of practice would give an advantage to the trained variations of the skill over non-trained, i.e., whether such practice would eventually lead to the development of (multi)-especial skills. The purpose of this study was to determine whether massive amount of practice involving four criterion variations of the skill will give an advantage in performance to the criterions over the class of actions. In two experiments, we analyzed data from female (n = 8) and male classical archers (n = 10), who were required to shoot 30 shots from four accustomed distances, i.e., males at 30, 50, 70, and 90 m and females at 30, 50, 60, and 70 m. The shooting accuracy for the untrained distances (16 distances in men and 14 in women) was used to compile a regression line for distance over shooting accuracy. Regression determined (expected) values were then compared to the shooting accuracy of the trained distances. Data revealed no significant differences between real and expected results at trained distances, except for the 70 m shooting distance in men. The F-test for lack of fit showed that the regression computed for trained and non-trained shooting distances was linear. It can be concluded that especial skills emerge only after very specific practice, i.e., constant practice limited to only one variation of the skill. PMID:27547196

Specificity vs. Generalizability: Emergence of Especial Skills in Classical Archery.

PubMed

Czyż, Stanisław H; Moss, Sarah J

2016-01-01

There is evidence that the recall schema becomes more refined after constant practice. It is also believed that massive amounts of constant practice eventually leads to the emergence of especial skills, i.e., skills that have an advantage in performance over other actions from within the same class of actions. This advantage in performance was noticed when one-criterion practice, e.g., basketball free throws, was compared to non-practiced variations of the skill. However, there is no evidence whether multi-criterion massive amounts of practice would give an advantage to the trained variations of the skill over non-trained, i.e., whether such practice would eventually lead to the development of (multi)-especial skills. The purpose of this study was to determine whether massive amount of practice involving four criterion variations of the skill will give an advantage in performance to the criterions over the class of actions. In two experiments, we analyzed data from female (n = 8) and male classical archers (n = 10), who were required to shoot 30 shots from four accustomed distances, i.e., males at 30, 50, 70, and 90 m and females at 30, 50, 60, and 70 m. The shooting accuracy for the untrained distances (16 distances in men and 14 in women) was used to compile a regression line for distance over shooting accuracy. Regression determined (expected) values were then compared to the shooting accuracy of the trained distances. Data revealed no significant differences between real and expected results at trained distances, except for the 70 m shooting distance in men. The F-test for lack of fit showed that the regression computed for trained and non-trained shooting distances was linear. It can be concluded that especial skills emerge only after very specific practice, i.e., constant practice limited to only one variation of the skill.

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.

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.

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.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

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.

Climate regime shifts in paleoclimate time series from the Yucatán Peninsula: from the Preclassic to Classic period

NASA Astrophysics Data System (ADS)

Polanco Martínez, Josue M.; Medina-Elizalde, Martin; Burns, Stephen J.; Jiang, Xiuyang; Shen, Chuan-Chou

2015-04-01

It has been widely accepted by the paleoclimate and archaeology communities that extreme climate events (especially droughts) and past climate change played an important role in the cultural changes that occurred in at least some parts of the Maya Lowlands, from the Pre-Classic (2000 BC to 250 AD) to Post-Classic periods (1000 to 1521 AD) [1, 2]. In particular, a large number of studies suggest that the decline of the Maya civilization in the Terminal Classic Period was greatly influenced by prolonged severe drought events that probably triggered significant societal disruptions [1, 3, 4, 5]. Going further on these issues, the aim of this work is to detect climate regime shifts in several paleoclimate time series from the Yucatán Peninsula (México) that have been used as rainfall proxies [3, 5, 6, 7]. In order to extract information from the paleoclimate data studied, we have used a change point method [8] as implemented in the R package strucchange, as well as the RAMFIT method [9]. The preliminary results show for all the records analysed a prominent regime shift between 400 to 200 BCE (from a noticeable increase to a remarkable fall in precipitation), which is strongest in the recently obtained stalagmite (Itzamna) delta18-O precipitation record [7]. References [1] Gunn, J. D., Matheny, R. T., Folan, W. J., 2002. Climate-change studies in the Maya area. Ancient Mesoamerica, 13(01), 79-84. [2] Yaeger, J., Hodell, D. A., 2008. The collapse of Maya civilization: assessing the interaction of culture, climate, and environment. El Niño, Catastrophism, and Culture Change in Ancient America, 197-251. [3] Hodell, D. A., Curtis, J. H., Brenner, M., 1995. Possible role of climate in the collapse of Classic Maya civilization. Nature, 375(6530), 391-394. [4] Aimers, J., Hodell, D., 2011. Societal collapse: Drought and the Maya. Nature 479(7371), 44-45 (2011). [5] Medina-Elizalde, M., Rohling, E. J., 2012. Collapse of Classic Maya civilization related to modest reduction in precipitation. Science, 335(6071), 956-959. [6] Medina-Elizalde, M., Burns, S. J., Lea, D. W., Asmerom, Y., von Gunten, L., Polyak, V., Vuille, M., Karmalkar, A., 2010. High resolution stalagmite climate record from the Yucatán Peninsula spanning the Maya terminal classic period. Earth and Planetary Science Letters, 298(1), 255-262. [7] Medina-Elizalde, M., Burns, S. J, Jiang, X., Shen, C. C., Lases-Hernandez, F., Polanco-Martinez, J.M., High-resolution stalagmite record from the Yucatan Peninsula spanning the Preclassic period, work in progress to be submitted to the Global Planetary Change (by invitation). [8] Zeileis, A., Leisch, F., Hornik, K., Kleiber, C., 2002. strucchange: An R Package for Testing for Structural Change in Linear Regression Models. Journal of statistical software, 7(2), 1-38. [9] Mudelsee, M. (2000). Ramp function regression: a tool for quantifying climate transitions. Computers & Geosciences, 26(3), 293-307.

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.

Asymptotic Linear Spectral Statistics for Spiked Hermitian Random Matrices

NASA Astrophysics Data System (ADS)

Passemier, Damien; McKay, Matthew R.; Chen, Yang

2015-07-01

Using the Coulomb Fluid method, this paper derives central limit theorems (CLTs) for linear spectral statistics of three "spiked" Hermitian random matrix ensembles. These include Johnstone's spiked model (i.e., central Wishart with spiked correlation), non-central Wishart with rank-one non-centrality, and a related class of non-central matrices. For a generic linear statistic, we derive simple and explicit CLT expressions as the matrix dimensions grow large. For all three ensembles under consideration, we find that the primary effect of the spike is to introduce an correction term to the asymptotic mean of the linear spectral statistic, which we characterize with simple formulas. The utility of our proposed framework is demonstrated through application to three different linear statistics problems: the classical likelihood ratio test for a population covariance, the capacity analysis of multi-antenna wireless communication systems with a line-of-sight transmission path, and a classical multiple sample significance testing problem.

Computational simulations of hydrogen circular migration in protonated acetylene induced by circularly polarized light

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

Shi, Xuetao; Li, Wen; Schlegel, H. Bernhard, E-mail: hbs@chem.wayne.edu

2016-08-28

The hydrogens in protonated acetylene are very mobile and can easily migrate around the C{sub 2} core by moving between classical and non-classical structures of the cation. The lowest energy structure is the T-shaped, non-classical cation with a hydrogen bridging the two carbons. Conversion to the classical H{sub 2}CCH{sup +} ion requires only 4 kcal/mol. The effect of circularly polarized light on the migration of hydrogens in oriented C{sub 2}H{sub 3}{sup +} has been simulated by Born-Oppenheimer molecular dynamics. Classical trajectory calculations were carried out with the M062X/6-311+G(3df,2pd) level of theory using linearly and circularly polarized 32 cycle 7 μmmore » cosine squared pulses with peak intensity of 5.6 × 10{sup 13} W/cm{sup 2} and 3.15 × 10{sup 13} W/cm{sup 2}, respectively. These linearly and circularly polarized pulses transfer similar amounts of energy and total angular momentum to C{sub 2}H{sub 3}{sup +}. The average angular momentum vectors of the three hydrogens show opposite directions of rotation for right and left circularly polarized light, but no directional preference for linearly polarized light. This difference results in an appreciable amount of angular displacement of the three hydrogens relative to the C{sub 2} core for circularly polarized light, but only an insignificant amount for linearly polarized light. Over the course of the simulation with circularly polarized light, this corresponds to a propeller-like motion of the three hydrogens around the C{sub 2} core of protonated acetylene.« less

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.

Ultrasound-assisted extraction of bioactive compounds from lemon balm and peppermint leaves

NASA Astrophysics Data System (ADS)

Šic Žlabur, Jana; Voća, Sandra; Dobričević, Nadica; Pliestić, Stjepan; Galić, Ante; Boričević, Ana; Borić, Nataša

2016-01-01

The aim of this study was to investigate the influence of conventional and ultrasound-assisted extraction (frequency, time, temperature) on the content of bioactive compounds as well as on the antioxidant activity of aqueous extracts from fresh lemon balm and peppermint leaves. Total phenols, flavonoids, non-flavonoids, total chlorophylls, total carotenoids, and radical scavenging capacity were determined. Moreover, the relationship between bioactive compounds and antioxidant capacity was studied by linear regression. A significant increase in all studied bioactive compounds during ultrasonic extraction for 5 to 20 min was found. With the classical extraction method, the highest amounts of total phenols, flavonoids, and antioxidant activity were determined, and the maximum amounts of total chlorophylls and carotenoids were determined during 20 min ultrasonic extraction. The correlation analysis revealed a strong, positive relationship between antioxidant activity and total phenolic compounds.

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

Symplectic evolution of Wigner functions in Markovian open systems.

PubMed

Brodier, O; Almeida, A M Ozorio de

2004-01-01

The Wigner function is known to evolve classically under the exclusive action of a quadratic Hamiltonian. If the system also interacts with the environment through Lindblad operators that are complex linear functions of position and momentum, then the general evolution is the convolution of a non-Hamiltonian classical propagation of the Wigner function with a phase space Gaussian that broadens in time. We analyze the consequences of this in the three generic cases of elliptic, hyperbolic, and parabolic Hamiltonians. The Wigner function always becomes positive in a definite time, which does not depend on the initial pure state. We observe the influence of classical dynamics and dissipation upon this threshold. We also derive an exact formula for the evolving linear entropy as the average of a narrowing Gaussian taken over a probability distribution that depends only on the initial state. This leads to a long time asymptotic formula for the growth of linear entropy. We finally discuss the possibility of recovering the initial state.

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

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...

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.

Bayesian Inference for the Stereotype Regression Model: Application to a Case-control Study of Prostate Cancer

PubMed Central

Ahn, Jaeil; Mukherjee, Bhramar; Banerjee, Mousumi; Cooney, Kathleen A.

2011-01-01

Summary The stereotype regression model for categorical outcomes, proposed by Anderson (1984) is nested between the baseline category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline-category (or multinomial logistic) model due to a product representation of the log odds-ratios in terms of a common parameter corresponding to each predictor and category specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multidimensional in nature. As pointed out by Greenland (1994), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome-stratified sampling as in case-control studies. In addition, for matched case-control studies, the stereotype model is amenable to classical conditional likelihood principle, whereas there is no reduction due to sufficiency under the proportional odds model. In spite of these attractive features, the model has been applied less, as there are issues with maximum likelihood estimation and likelihood based testing approaches due to non-linearity and lack of identifiability of the parameters. We present comprehensive Bayesian inference and model comparison procedure for this class of models as an alternative to the classical frequentist approach. We illustrate our methodology by analyzing data from The Flint Men’s Health Study, a case-control study of prostate cancer in African-American men aged 40 to 79 years. We use clinical staging of prostate cancer in terms of Tumors, Nodes and Metastatsis (TNM) as the categorical response of interest. PMID:19731262

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

Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers.

PubMed

Curate, F; Umbelino, C; Perinha, A; Nogueira, C; Silva, A M; Cunha, E

2017-11-01

The assessment of sex is of paramount importance in the establishment of the biological profile of a skeletal individual. Femoral relevance for sex estimation is indisputable, particularly when other exceedingly dimorphic skeletal regions are missing. As such, this study intended to generate population-specific osteometric models for the estimation of sex with the femur and to compare the accuracy of the models obtained through classical and machine-learning classifiers. A set of 15 standard femoral measurements was acquired in a training sample (100 females; 100 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal) and models for sex classification were produced with logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM), and reduce error pruning trees (REPTree). Under cross-validation, univariable sectioning points generated with REPTree correctly estimated sex in 60.0-87.5% of cases (systematic error ranging from 0.0 to 37.0%), while multivariable models correctly classified sex in 84.0-92.5% of cases (bias from 0.0 to 7.0%). All models were assessed in a holdout sample (24 females; 34 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with an allocation accuracy ranging from 56.9 to 86.2% (bias from 4.4 to 67.0%) in the univariable models, and from 84.5 to 89.7% (bias from 3.7 to 23.3%) in the multivariable models. This study makes available a detailed description of sexual dimorphism in femoral linear dimensions in two Portuguese identified skeletal samples, emphasizing the relevance of the femur for the estimation of sex in skeletal remains in diverse conditions of completeness and preservation. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

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.

Determinants of Psychosocial Difficulties Experienced by Persons with Brain Disorders: Towards a ‘Horizontal Epidemiology’ Approach

PubMed Central

Sabariego, Carla; Coenen, Michaela; Ballert, Carolina; Cabello, Maria; Leonardi, Matilde; Anczewska, Marta; Pitkänen, Tuuli; Raggi, Alberto; Mellor, Blanca; Covelli, Venusia; Świtaj, Piotr; Levola, Jonna; Schiavolin, Silvia; Chrostek, Anna; Bickenbach, Jerome; Chatterji, Somnath; Cieza, Alarcos

2015-01-01

Background Persons with brain disorders experience significant psychosocial difficulties (PSD) in daily life, e.g. problems with managing daily routine or emotional lability, and the level of the PSD depends on social, physical and political environments, and psychologic-personal determinants. Our objective is to determine a brief set of environmental and psychologic-personal factors that are shared determinants of PSD among persons with different brain disorders. Methods Cross-sectional study, convenience sample of persons with either dementia, stroke, multiple sclerosis, epilepsy, migraine, depression, schizophrenia, substance dependence or Parkinson’s disease. Random forest regression and classical linear regression were used in the analyses. Results 722 subjects were interviewed in four European countries. The brief set of determinants encompasses presence of comorbidities, health status appraisal, stressful life events, personality changes, adaptation, self-esteem, self-worth, built environment, weather, and health problems in the family. Conclusions The identified brief set of common determinants of PSD can be used to support the implementation of cross-cutting interventions, social actions and policy tools to lower PSD experienced by persons with brain disorders. This set complements a recently proposed reliable and valid direct metric of PSD for brain disorders called PARADISE24. PMID:26675663

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

Classical and sequential limit analysis revisited

NASA Astrophysics Data System (ADS)

Leblond, Jean-Baptiste; Kondo, Djimédo; Morin, Léo; Remmal, Almahdi

2018-04-01

Classical limit analysis applies to ideal plastic materials, and within a linearized geometrical framework implying small displacements and strains. Sequential limit analysis was proposed as a heuristic extension to materials exhibiting strain hardening, and within a fully general geometrical framework involving large displacements and strains. The purpose of this paper is to study and clearly state the precise conditions permitting such an extension. This is done by comparing the evolution equations of the full elastic-plastic problem, the equations of classical limit analysis, and those of sequential limit analysis. The main conclusion is that, whereas classical limit analysis applies to materials exhibiting elasticity - in the absence of hardening and within a linearized geometrical framework -, sequential limit analysis, to be applicable, strictly prohibits the presence of elasticity - although it tolerates strain hardening and large displacements and strains. For a given mechanical situation, the relevance of sequential limit analysis therefore essentially depends upon the importance of the elastic-plastic coupling in the specific case considered.

Robust linear discriminant models to solve financial crisis in banking sectors

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

Comparison of adaptive critic-based and classical wide-area controllers for power systems.

PubMed

Ray, Swakshar; Venayagamoorthy, Ganesh Kumar; Chaudhuri, Balarko; Majumder, Rajat

2008-08-01

An adaptive critic design (ACD)-based damping controller is developed for a thyristor-controlled series capacitor (TCSC) installed in a power system with multiple poorly damped interarea modes. The performance of this ACD computational intelligence-based method is compared with two classical techniques, which are observer-based state-feedback (SF) control and linear matrix inequality LMI-H(infinity) robust control. Remote measurements are used as feedback signals to the wide-area damping controller for modulating the compensation of the TCSC. The classical methods use a linearized model of the system whereas the ACD method is purely measurement-based, leading to a nonlinear controller with fixed parameters. A comparative analysis of the controllers' performances is carried out under different disturbance scenarios. The ACD-based design has shown promising performance with very little knowledge of the system compared to classical model-based controllers. This paper also discusses the advantages and disadvantages of ACDs, SF, and LMI-H(infinity).

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.

Circulating CD34-Positive Cells Are Associated with Handgrip Strength in Japanese Older Men: The Nagasaki Islands Study.

PubMed

Yamanashi, H; Shimizu, Y; Koyamatsu, J; Nagayoshi, M; Kadota, K; Tamai, M; Maeda, T

2017-01-01

Handgrip strength is a simple measurement of overall muscular strength and is used to detect sarcopenia. It also predicts adverse events in later life. Many mechanisms of sarcopenia development have been reported. A hypertensive status impairs endothelial dysfunction, which might deteriorate skeletal muscle if vascular angiogenesis is not maintained. This study investigated muscle strength and circulating CD34-positive cells as a marker of vascular angiogenesis. Cross-sectional study. 262 male Japanese community dwellers aged 60 to 69 years. The participants' handgrip strength, medical history, and blood samples were taken. We stratified the participants by hypertensive status to investigate the association between handgrip strength and circulating CD34-positive cells according to hypertensive status. Pearson correlation and linear regression analyses were used. In the Pearson correlation analysis, handgrip strength and the logarithm of circulating CD34-positive cells were significantly associated in hypertensive participants (r=0.22, p=0.021), but not in non-hypertensive participants (r=-0.01, p=0.943). This relationship was only significant in hypertensive participants (ß=1.94, p=0.021) in the simple linear regression analysis, and it remained significant after adjusting for classic cardiovascular risk factors (ß=1.92, p=0.020). The relationship was not significant in non-hypertensive participants (ß=-0.09, p=0.903). We found a positive association between handgrip strength and circulating CD34-positive cells in hypertensive men. Vascular maintenance attributed by circulating CD34-positive cells is thought to be a background mechanism of this association after hypertension-induced vascular injury in skeletal muscle.

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

Growth models of Rhizophora mangle L. seedlings in tropical southwestern Atlantic

NASA Astrophysics Data System (ADS)

Lima, Karen Otoni de Oliveira; Tognella, Mônica Maria Pereira; Cunha, Simone Rabelo; Andrade, Humber Agrelli de

2018-07-01

The present study selected and compared regression models that best describe the growth curves of Rhizophora mangle seedlings based on the height (cm) and time (days) variables. The Linear, Exponential, Power Law, Monomolecular, Logistic, and Gompertz models were adjusted with non-linear formulations and minimization of the sum of the squares of the residues. The Akaike Information Criterion was used to select the best model for each seedling. After this selection, the determination coefficient, which evaluates how well a model describes height variation as a time function, was inspected. Differing from the classic population ecology studies, the Monomolecular, Three-parameter Logistic, and Gompertz models presented the best performance in describing growth, suggesting they are the most adequate options for long-term studies. The different growth curves reflect the complexity of stem growth at the seedling stage for R. mangle. The analysis of the joint distribution of the parameters initial height, growth rate, and, asymptotic size allowed the study of the species ecological attributes and to observe its intraspecific variability in each model. Our results provide a basis for interpretation of the dynamics of seedlings growth during their establishment in a mature forest, as well as its regeneration processes.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

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.

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…

Physiological Strain in French Vineyard Workers Wearing Protective Equipment to Conduct Re-Entry Tasks in Humid Conditions.

PubMed

Grimbuhler, Sonia; Viel, Jean-François

2018-06-19

The proper use of personal protective equipment (PPE) plays an important role in reducing exposure to pesticides in vineyard farming activities, including re-entry tasks. However, discomfort from clothing systems may increase the physiological burden on workers. We compared the physiological burdens of vineyard workers wearing three different types of PPE during canopy management in field humid conditions while accounting for occupational, climatic, and geographical environments. The study was conducted in the Bordeaux vineyards of southern France during June 2012. A total of 42 workers from seven vineyards consented to field observations. The following PPE garments were randomly allocated: HF Estufa polyamide (Brisa®), Tyvek® Classic Plus, and Tychem® C Standard. Participant sociodemographic characteristics were collected using a structured questionnaire. Skin temperature and heart rate were monitored continuously using portable devices. Multivariate multilevel linear regression models were performed to account for the hierarchical structure of data. No significant difference was found for mean skin temperature during work. Regardless of the cardiac strain parameter considered, the Tyvek® Classic Plus garment produced the poorest results (P ≤ 0.03). Under the very humid conditions encountered during the field study, the thinness and breathability of the Tyvek® Classic Plus garment resulted in undergarment humidity, imposing additional physiological burden on vineyard workers. These results confirm that the idea of using generic coveralls in any farming activity is unsuitable. Compromises should be created between physiological costs and protection, depending on the agricultural task performed, the crop grown, and the environmental conditions encountered.

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.

Primordial helium abundance determination using sulphur as metallicity tracer

NASA Astrophysics Data System (ADS)

Fernández, Vital; Terlevich, Elena; Díaz, Angeles I.; Terlevich, Roberto; Rosales-Ortega, F. F.

2018-05-01

The primordial helium abundance YP is calculated using sulphur as metallicity tracer in the classical methodology (with YP as an extrapolation of Y to zero metals). The calculated value, YP, S = 0.244 ± 0.006, is in good agreement with the estimate from the Planck experiment, as well as, determinations in the literature using oxygen as the metallicity tracer. The chemical analysis includes the sustraction of the nebular continuum and of the stellar continuum computed from simple stellar population synthesis grids. The S+2 content is measured from the near infrared [SIII]λλ9069Å, 9532Å lines, while an ICF(S3 +) is proposed based on the Ar3 +/Ar2 + fraction. Finally, we apply a multivariable linear regression using simultaneously oxygen, nitrogen and sulphur abundances for the same sample to determine the primordial helium abundance resulting in YP - O, N, S = 0.245 ± 0.007.

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride.

PubMed

Katsarov, Plamen; Gergov, Georgi; Alin, Aylin; Pilicheva, Bissera; Al-Degs, Yahya; Simeonov, Vasil; Kassarova, Margarita

2018-03-01

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

Analytical solution of Luedeking-Piret equation for a batch fermentation obeying Monod growth kinetics.

PubMed

Garnier, Alain; Gaillet, Bruno

2015-12-01

Not so many fermentation mathematical models allow analytical solutions of batch process dynamics. The most widely used is the combination of the logistic microbial growth kinetics with Luedeking-Piret bioproduct synthesis relation. However, the logistic equation is principally based on formalistic similarities and only fits a limited range of fermentation types. In this article, we have developed an analytical solution for the combination of Monod growth kinetics with Luedeking-Piret relation, which can be identified by linear regression and used to simulate batch fermentation evolution. Two classical examples are used to show the quality of fit and the simplicity of the method proposed. A solution for the combination of Haldane substrate-limited growth model combined with Luedeking-Piret relation is also provided. These models could prove useful for the analysis of fermentation data in industry as well as academia. © 2015 Wiley Periodicals, Inc.

Predicting perceptual quality of images in realistic scenario using deep filter banks

NASA Astrophysics Data System (ADS)

Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

2018-03-01

Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

Examining Differential Resilience Mechanisms by Comparing 'Tipping Points' of the Effects of Neighborhood Conditions on Anxiety by Race/Ethnicity.

PubMed

Coman, Emil Nicolae; Wu, Helen Zhao

2018-02-20

Exposure to adverse environmental and social conditions affects physical and mental health through complex mechanisms. Different racial/ethnic (R/E) groups may be more or less vulnerable to the same conditions, and the resilience mechanisms that can protect them likely operate differently in each population. We investigate how adverse neighborhood conditions (neighborhood disorder, NDis) differentially impact mental health (anxiety, Anx) in a sample of white and Black (African American) young women from Southeast Texas, USA. We illustrate a simple yet underutilized segmented regression model where linearity is relaxed to allow for a shift in the strength of the effect with the levels of the predictor. We compare how these effects change within R/E groups with the level of the predictor, but also how the "tipping points," where the effects change in strength, may differ by R/E. We find with classic linear regression that neighborhood disorder adversely affects Black women's anxiety, while in white women the effect seems negligible. Segmented regressions show that the Ndis → Anx effects in both groups of women appear to shift at similar levels, about one-fifth of a standard deviation below the mean of NDis, but the effect for Black women appears to start out as negative, then shifts in sign, i.e., to increase anxiety, while for white women, the opposite pattern emerges. Our findings can aid in devising better strategies for reducing health disparities that take into account different coping or resilience mechanisms operating differentially at distinct levels of adversity. We recommend that researchers investigate when adversity becomes exceedingly harmful and whether this happens differentially in distinct populations, so that intervention policies can be planned to reverse conditions that are more amenable to change, in effect pushing back the overall social risk factors below such tipping points.

Classical boson sampling algorithms with superior performance to near-term experiments

NASA Astrophysics Data System (ADS)

Neville, Alex; Sparrow, Chris; Clifford, Raphaël; Johnston, Eric; Birchall, Patrick M.; Montanaro, Ashley; Laing, Anthony

2017-12-01

It is predicted that quantum computers will dramatically outperform their conventional counterparts. However, large-scale universal quantum computers are yet to be built. Boson sampling is a rudimentary quantum algorithm tailored to the platform of linear optics, which has sparked interest as a rapid way to demonstrate such quantum supremacy. Photon statistics are governed by intractable matrix functions, which suggests that sampling from the distribution obtained by injecting photons into a linear optical network could be solved more quickly by a photonic experiment than by a classical computer. The apparently low resource requirements for large boson sampling experiments have raised expectations of a near-term demonstration of quantum supremacy by boson sampling. Here we present classical boson sampling algorithms and theoretical analyses of prospects for scaling boson sampling experiments, showing that near-term quantum supremacy via boson sampling is unlikely. Our classical algorithm, based on Metropolised independence sampling, allowed the boson sampling problem to be solved for 30 photons with standard computing hardware. Compared to current experiments, a demonstration of quantum supremacy over a successful implementation of these classical methods on a supercomputer would require the number of photons and experimental components to increase by orders of magnitude, while tackling exponentially scaling photon loss.

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…

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.

Classical-trajectory simulation of accelerating neutral atoms with polarized intense laser pulses

NASA Astrophysics Data System (ADS)

Xia, Q. Z.; Fu, L. B.; Liu, J.

2013-03-01

In the present paper, we perform the classical trajectory Monte Carlo simulation of the complex dynamics of accelerating neutral atoms with linearly or circularly polarized intense laser pulses. Our simulations involve the ion motion as well as the tunneling ionization and the scattering dynamics of valence electron in the combined Coulomb and electromagnetic fields, for both helium (He) and magnesium (Mg). We show that for He atoms, only linearly polarized lasers can effectively accelerate the atoms, while for Mg atoms, we find that both linearly and circularly polarized lasers can successively accelerate the atoms. The underlying mechanism is discussed and the subcycle dynamics of accelerating trajectories is investigated. We have compared our theoretical results with a recent experiment [Eichmann Nature (London)NATUAS0028-083610.1038/nature08481 461, 1261 (2009)].

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…

Non-stationary pre-envelope covariances of non-classically damped systems

NASA Astrophysics Data System (ADS)

Muscolino, G.

1991-08-01

A new formulation is given to evaluate the stationary and non-stationary response of linear non-classically damped systems subjected to multi-correlated non-separable Gaussian input processes. This formulation is based on a new and more suitable definition of the impulse response function matrix for such systems. It is shown that, when using this definition, the stochastic response of non-classically damped systems involves the evaluation of quantities similar to those of classically damped ones. Furthermore, considerations about non-stationary cross-covariances, spectral moments and pre-envelope cross-covariances are presented for a monocorrelated input process.

Lenard-Balescu calculations and classical molecular dynamics simulations of electrical and thermal conductivities of hydrogen plasmas

DOE PAGES

Whitley, Heather D.; Scullard, Christian R.; Benedict, Lorin X.; ...

2014-12-04

Here, we present a discussion of kinetic theory treatments of linear electrical and thermal transport in hydrogen plasmas, for a regime of interest to inertial confinement fusion applications. In order to assess the accuracy of one of the more involved of these approaches, classical Lenard-Balescu theory, we perform classical molecular dynamics simulations of hydrogen plasmas using 2-body quantum statistical potentials and compute both electrical and thermal conductivity from out particle trajectories using the Kubo approach. Our classical Lenard-Balescu results employing the identical statistical potentials agree well with the simulations.

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…

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.

Symbolic Solution of Linear Differential Equations

NASA Technical Reports Server (NTRS)

Feinberg, R. B.; Grooms, R. G.

1981-01-01

An algorithm for solving linear constant-coefficient ordinary differential equations is presented. The computational complexity of the algorithm is discussed and its implementation in the FORMAC system is described. A comparison is made between the algorithm and some classical algorithms for solving differential equations.

Non-linear vibrations of sandwich viscoelastic shells

NASA Astrophysics Data System (ADS)

Benchouaf, Lahcen; Boutyour, El Hassan; Daya, El Mostafa; Potier-Ferry, Michel

2018-04-01

This paper deals with the non-linear vibration of sandwich viscoelastic shell structures. Coupling a harmonic balance method with the Galerkin's procedure, one obtains an amplitude equation depending on two complex coefficients. The latter are determined by solving a classical eigenvalue problem and two linear ones. This permits to get the non-linear frequency and the non-linear loss factor as functions of the displacement amplitude. To validate our approach, these relationships are illustrated in the case of a circular sandwich ring.

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.

Superrotation charge and supertranslation hair on black holes

DOE PAGES

Hawking, Stephen W.; Perry, Malcolm J.; Strominger, Andrew

2017-05-31

It is shown that black hole spacetimes in classical Einstein gravity are characterized by, in addition to their ADM mass M, momentummore » $$\\vec{P}$$, angular momentum $$\\vec{J}$$ and boost charge $$\\vec{/k}$$ , an infinite head of supertranslation hair. Furthermore, the distinct black holes are distinguished by classical superrotation charges measured at infinity. Solutions with supertranslation hair are diffeomorphic to the Schwarzschild spacetime, but the diffeomorphisms are part of the BMS subgroup and act nontrivially on the physical phase space. It is shown that a black hole can be supertranslated by throwing in an asymmetric shock wave. We derive a leading-order Bondi-gauge expression for the linearized horizon supertranslation charge and shown to generate, via the Dirac bracket, supertranslations on the linearized phase space of gravitational excitations of the horizon. The considerations of this paper are largely classical augmented by comments on their implications for the quantum theory.« less

Superrotation charge and supertranslation hair on black holes

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

Hawking, Stephen W.; Perry, Malcolm J.; Strominger, Andrew

It is shown that black hole spacetimes in classical Einstein gravity are characterized by, in addition to their ADM mass M, momentummore » $$\\vec{P}$$, angular momentum $$\\vec{J}$$ and boost charge $$\\vec{/k}$$ , an infinite head of supertranslation hair. Furthermore, the distinct black holes are distinguished by classical superrotation charges measured at infinity. Solutions with supertranslation hair are diffeomorphic to the Schwarzschild spacetime, but the diffeomorphisms are part of the BMS subgroup and act nontrivially on the physical phase space. It is shown that a black hole can be supertranslated by throwing in an asymmetric shock wave. We derive a leading-order Bondi-gauge expression for the linearized horizon supertranslation charge and shown to generate, via the Dirac bracket, supertranslations on the linearized phase space of gravitational excitations of the horizon. The considerations of this paper are largely classical augmented by comments on their implications for the quantum theory.« less

A squeezed light source operated under high vacuum

PubMed Central

Wade, Andrew R.; Mansell, Georgia L.; Chua, Sheon S. Y.; Ward, Robert L.; Slagmolen, Bram J. J.; Shaddock, Daniel A.; McClelland, David E.

2015-01-01

Non-classical squeezed states of light are becoming increasingly important to a range of metrology and other quantum optics applications in cryptography, quantum computation and biophysics. Applications such as improving the sensitivity of advanced gravitational wave detectors and the development of space-based metrology and quantum networks will require robust deployable vacuum-compatible sources. To date non-linear photonics devices operated under high vacuum have been simple single pass systems, testing harmonic generation and the production of classically correlated photon pairs for space-based applications. Here we demonstrate the production under high-vacuum conditions of non-classical squeezed light with an observed 8.6 dB of quantum noise reduction down to 10 Hz. Demonstration of a resonant non-linear optical device, for the generation of squeezed light under vacuum, paves the way to fully exploit the advantages of in-vacuum operations, adapting this technology for deployment into new extreme environments. PMID:26657616

Towards the Fundamental Quantum Limit of Linear Measurements of Classical Signals

NASA Astrophysics Data System (ADS)

Miao, Haixing; Adhikari, Rana X.; Ma, Yiqiu; Pang, Belinda; Chen, Yanbei

2017-08-01

The quantum Cramér-Rao bound (QCRB) sets a fundamental limit for the measurement of classical signals with detectors operating in the quantum regime. Using linear-response theory and the Heisenberg uncertainty relation, we derive a general condition for achieving such a fundamental limit. When applied to classical displacement measurements with a test mass, this condition leads to an explicit connection between the QCRB and the standard quantum limit that arises from a tradeoff between the measurement imprecision and quantum backaction; the QCRB can be viewed as an outcome of a quantum nondemolition measurement with the backaction evaded. Additionally, we show that the test mass is more a resource for improving measurement sensitivity than a victim of the quantum backaction, which suggests a new approach to enhancing the sensitivity of a broad class of sensors. We illustrate these points with laser interferometric gravitational-wave detectors.

A squeezed light source operated under high vacuum

NASA Astrophysics Data System (ADS)

Wade, Andrew R.; Mansell, Georgia L.; Chua, Sheon S. Y.; Ward, Robert L.; Slagmolen, Bram J. J.; Shaddock, Daniel A.; McClelland, David E.

2015-12-01

Non-classical squeezed states of light are becoming increasingly important to a range of metrology and other quantum optics applications in cryptography, quantum computation and biophysics. Applications such as improving the sensitivity of advanced gravitational wave detectors and the development of space-based metrology and quantum networks will require robust deployable vacuum-compatible sources. To date non-linear photonics devices operated under high vacuum have been simple single pass systems, testing harmonic generation and the production of classically correlated photon pairs for space-based applications. Here we demonstrate the production under high-vacuum conditions of non-classical squeezed light with an observed 8.6 dB of quantum noise reduction down to 10 Hz. Demonstration of a resonant non-linear optical device, for the generation of squeezed light under vacuum, paves the way to fully exploit the advantages of in-vacuum operations, adapting this technology for deployment into new extreme environments.

Superrotation charge and supertranslation hair on black holes

NASA Astrophysics Data System (ADS)

Hawking, Stephen W.; Perry, Malcolm J.; Strominger, Andrew

2017-05-01

It is shown that black hole spacetimes in classical Einstein gravity are characterized by, in addition to their ADM mass M, momentum \\overrightarrow{P} , angular momentum \\overrightarrow{J} and boost charge \\overrightarrow{K} , an infinite head of supertranslation hair. The distinct black holes are distinguished by classical superrotation charges measured at infinity. Solutions with super-translation hair are diffeomorphic to the Schwarzschild spacetime, but the diffeomorphisms are part of the BMS subgroup and act nontrivially on the physical phase space. It is shown that a black hole can be supertranslated by throwing in an asymmetric shock wave. A leading-order Bondi-gauge expression is derived for the linearized horizon supertranslation charge and shown to generate, via the Dirac bracket, supertranslations on the linearized phase space of gravitational excitations of the horizon. The considerations of this paper are largely classical augmented by comments on their implications for the quantum theory.

Colombeau algebra as a mathematical tool for investigating step load and step deformation of systems of nonlinear springs and dashpots

NASA Astrophysics Data System (ADS)

Průša, Vít; Řehoř, Martin; Tůma, Karel

2017-02-01

The response of mechanical systems composed of springs and dashpots to a step input is of eminent interest in the applications. If the system is formed by linear elements, then its response is governed by a system of linear ordinary differential equations. In the linear case, the mathematical method of choice for the analysis of the response is the classical theory of distributions. However, if the system contains nonlinear elements, then the classical theory of distributions is of no use, since it is strictly limited to the linear setting. Consequently, a question arises whether it is even possible or reasonable to study the response of nonlinear systems to step inputs. The answer is positive. A mathematical theory that can handle the challenge is the so-called Colombeau algebra. Building on the abstract result by Průša and Rajagopal (Int J Non-Linear Mech 81:207-221, 2016), we show how to use the theory in the analysis of response of nonlinear spring-dashpot and spring-dashpot-mass systems.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards classical spectrum generating algebras for f-deformations

NASA Astrophysics Data System (ADS)

Kullock, Ricardo; Latini, Danilo

2016-01-01

In this paper we revise the classical analog of f-oscillators, a generalization of q-oscillators given in Man'ko et al. (1997) [8], in the framework of classical spectrum generating algebras (SGA) introduced in Kuru and Negro (2008) [9]. We write down the deformed Poisson algebra characterizing the entire family of non-linear oscillators and construct its general solution algebraically. The latter, covering the full range of f-deformations, shows an energy dependence both in the amplitude and the frequency of the motion.

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.)

New insights into faster computation of uncertainties

NASA Astrophysics Data System (ADS)

Bhattacharya, Atreyee

2012-11-01

Heavy computation power, lengthy simulations, and an exhaustive number of model runs—often these seem like the only statistical tools that scientists have at their disposal when computing uncertainties associated with predictions, particularly in cases of environmental processes such as groundwater movement. However, calculation of uncertainties need not be as lengthy, a new study shows. Comparing two approaches—the classical Bayesian “credible interval” and a less commonly used regression-based “confidence interval” method—Lu et al. show that for many practical purposes both methods provide similar estimates of uncertainties. The advantage of the regression method is that it demands 10-1000 model runs, whereas the classical Bayesian approach requires 10,000 to millions of model runs.

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.

Bose-Einstein condensation of the classical axion field in cosmology?

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

Davidson, Sacha; Elmer, Martin, E-mail: s.davidson@ipnl.in2p3.fr, E-mail: m.elmer@ipnl.in2p3.fr

The axion is a motivated cold dark matter candidate, which it would be interesting to distinguish from weakly interacting massive particles. Sikivie has suggested that axions could behave differently during non-linear galaxy evolution, if they form a Bose-Einstein condensate, and argues that ''gravitational thermalisation'' drives them to a Bose-Einstein condensate during the radiation dominated era. Using classical equations of motion during linear structure formation, we explore whether the gravitational interactions of axions can generate enough entropy. At linear order in G{sub N}, we interpret that the principle activities of gravity are to expand the Universe and grow density fluctuations. Tomore » quantify the rate of entropy creation we use the anisotropic stress to estimate a short dissipation scale for axions which does not confirm previous estimates of their gravitational thermalisation rate.« less

Insight into efficient image registration techniques and the demons algorithm.

PubMed

Vercauteren, Tom; Pennec, Xavier; Malis, Ezio; Perchant, Aymeric; Ayache, Nicholas

2007-01-01

As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is considered as almost solved for linear registration, we show in this paper that some tools that have recently been developed in the field of vision-based robot control can outperform classical solutions. The adequacy of these tools for linear image registration leads us to revisit non-linear registration and allows us to provide interesting theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage to the symmetric forces variant of the demons algorithm. We show that, on controlled experiments, this advantage is confirmed, and yields a faster convergence.

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.

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

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

PubMed

Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi

2015-01-01

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

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.

Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

ERIC Educational Resources Information Center

He, Lingjun; Levine, Richard A.; Fan, Juanjuan; Beemer, Joshua; Stronach, Jeanne

2018-01-01

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Automated generation of quantum-accurate classical interatomic potentials for metals and semiconductors

NASA Astrophysics Data System (ADS)

Thompson, Aidan; Foiles, Stephen; Schultz, Peter; Swiler, Laura; Trott, Christian; Tucker, Garritt

2013-03-01

Molecular dynamics (MD) is a powerful condensed matter simulation tool for bridging between macroscopic continuum models and quantum models (QM) treating a few hundred atoms, but is limited by the accuracy of available interatomic potentials. Sound physical and chemical understanding of these interactions have resulted in a variety of concise potentials for certain systems, but it is difficult to extend them to new materials and properties. The growing availability of large QM data sets has made it possible to use more automated machine-learning approaches. Bartók et al. demonstrated that the bispectrum of the local neighbor density provides good regression surrogates for QM models. We adopt a similar bispectrum representation within a linear regression scheme. We have produced potentials for silicon and tantalum, and we are currently extending the method to III-V compounds. Results will be presented demonstrating the accuracy of these potentials relative to the training data, as well as their ability to accurately predict material properties not explicitly included in the training data. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Dept. of Energy Nat. Nuclear Security Admin. under Contract DE-AC04-94AL85000.

Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error

PubMed Central

Wang, Ching-Yun; Cullings, Harry; Song, Xiao; Kopecky, Kenneth J.

2017-01-01

SUMMARY Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses. PMID:29354018

FABP4 and Cardiovascular Events in Peripheral Arterial Disease.

PubMed

Höbaus, Clemens; Herz, Carsten Thilo; Pesau, Gerfried; Wrba, Thomas; Koppensteiner, Renate; Schernthaner, Gerit-Holger

2018-05-01

Fatty acid-binding protein 4 (FABP4) is a possible biomarker of atherosclerosis. We evaluated FABP4 levels, for the first time, in patients with peripheral artery disease (PAD) and the possible association between baseline FABP4 levels and cardiovascular events over time. Patients (n = 327; mean age 69 ± 10 years) with stable PAD were enrolled in this study. Serum FABP4 was measured by bead-based multiplex assay. Cardiovascular events were analyzed by FABP4 tertiles using Kaplan-Meier and Cox regression analyses after 5 years. Serum FABP4 levels showed a significant association with the classical 3-point major adverse cardiovascular event (MACE) end point (including death, nonlethal myocardial infarction, or nonfatal stroke) in patients with PAD ( P = .038). A standard deviation increase of FABP4 resulted in a hazard ratio (HR) of 1.33 (95% confidence interval [95% CI]: 1.03-1.71) for MACE. This association increased (HR: 1.47, 95% CI: 1.03-1.71) after multivariable adjustment ( P = .020). Additionally, in multivariable linear regression analysis, FABP4 was linked to estimated glomerular filtration rate ( P < .001), gender ( P = .005), fasting triglycerides ( P = .048), and body mass index ( P < .001). Circulating FABP4 may be a useful additional biomarker to evaluate patients with stable PAD at risk of major cardiovascular complications.

The Mantel-Haenszel procedure revisited: models and generalizations.

PubMed

Fidler, Vaclav; Nagelkerke, Nico

2013-01-01

Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented.

The Mantel-Haenszel Procedure Revisited: Models and Generalizations

PubMed Central

Fidler, Vaclav; Nagelkerke, Nico

2013-01-01

Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented. PMID:23516463

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.

Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction.

PubMed

Hoyer, Dirk; Leder, Uwe; Hoyer, Heike; Pompe, Bernd; Sommer, Michael; Zwiener, Ulrich

2002-01-01

The heart rate variability (HRV) is related to several mechanisms of the complex autonomic functioning such as respiratory heart rate modulation and phase dependencies between heart beat cycles and breathing cycles. The underlying processes are basically nonlinear. In order to understand and quantitatively assess those physiological interactions an adequate coupling analysis is necessary. We hypothesized that nonlinear measures of HRV and cardiorespiratory interdependencies are superior to the standard HRV measures in classifying patients after acute myocardial infarction. We introduced mutual information measures which provide access to nonlinear interdependencies as counterpart to the classically linear correlation analysis. The nonlinear statistical autodependencies of HRV were quantified by auto mutual information, the respiratory heart rate modulation by cardiorespiratory cross mutual information, respectively. The phase interdependencies between heart beat cycles and breathing cycles were assessed basing on the histograms of the frequency ratios of the instantaneous heart beat and respiratory cycles. Furthermore, the relative duration of phase synchronized intervals was acquired. We investigated 39 patients after acute myocardial infarction versus 24 controls. The discrimination of these groups was improved by cardiorespiratory cross mutual information measures and phase interdependencies measures in comparison to the linear standard HRV measures. This result was statistically confirmed by means of logistic regression models of particular variable subsets and their receiver operating characteristics.

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.

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

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.

Non-classical and potential symmetry analysis of Richard's equation for moisture flow in soil

NASA Astrophysics Data System (ADS)

Wiltshire, Ron; El-Kafri, Manal

2004-01-01

This paper focuses upon the derivation of the non-classical symmetries of Bluman and Cole as they apply to Richard's equation for water flow in an unsaturated uniform soil. It is shown that the determining equations for the non-classical case lead to four highly non-linear equations which have been solved in five particular cases. In each case the corresponding similarity ansatz has been derived and Richard's equation is reduced to an ordinary differential equation. Explicit solutions are produced when possible. Richard's equation is also expressed as a potential system and in reviewing the classical Lie solutions a new symmetry is derived together with its similarity ansatz. Determining equations are then produced for the potential system using the non-classical algorithm. This results in an under-determined set of equations and an example symmetry that reveals a missing classical case is presented. An example of a classical and a non-classical symmetry reduction applied to the infiltration of moisture in soil is presented. The condition for surface invariance is used to demonstrate the equivalence of a classical Lie and a potential symmetry.

Interacting charges and the classical electron radius

NASA Astrophysics Data System (ADS)

De Luca, Roberto; Di Mauro, Marco; Faella, Orazio; Naddeo, Adele

2018-03-01

The equation of the motion of a point charge q repelled by a fixed point-like charge Q is derived and studied. In solving this problem useful concepts in classical and relativistic kinematics, in Newtonian mechanics and in non-linear ordinary differential equations are revised. The validity of the approximations is discussed from the physical point of view. In particular the classical electron radius emerges naturally from the requirement that the initial distance is large enough for the non-relativistic approximation to be valid. The relevance of this topic for undergraduate physics teaching is pointed out.

Quantum Neural Nets

NASA Technical Reports Server (NTRS)

Zak, Michail; Williams, Colin P.

1997-01-01

The capacity of classical neurocomputers is limited by the number of classical degrees of freedom which is roughly proportional to the size of the computer. By Contrast, a Hypothetical quantum neurocomputer can implement an exponentially large number of the degrees of freedom within the same size. In this paper an attempt is made to reconcile linear reversible structure of quantum evolution with nonlinear irreversible dynamics for neural nets.

Polar codes for achieving the classical capacity of a quantum channel

NASA Astrophysics Data System (ADS)

Guha, Saikat; Wilde, Mark

2012-02-01

We construct the first near-explicit, linear, polar codes that achieve the capacity for classical communication over quantum channels. The codes exploit the channel polarization phenomenon observed by Arikan for classical channels. Channel polarization is an effect in which one can synthesize a set of channels, by ``channel combining'' and ``channel splitting,'' in which a fraction of the synthesized channels is perfect for data transmission while the other fraction is completely useless for data transmission, with the good fraction equal to the capacity of the channel. Our main technical contributions are threefold. First, we demonstrate that the channel polarization effect occurs for channels with classical inputs and quantum outputs. We then construct linear polar codes based on this effect, and the encoding complexity is O(N log N), where N is the blocklength of the code. We also demonstrate that a quantum successive cancellation decoder works well, i.e., the word error rate decays exponentially with the blocklength of the code. For a quantum channel with binary pure-state outputs, such as a binary-phase-shift-keyed coherent-state optical communication alphabet, the symmetric Holevo information rate is in fact the ultimate channel capacity, which is achieved by our polar code.

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.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-01-01

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

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.

The Quantum Arnold Transformation for the damped harmonic oscillator: from the Caldirola-Kanai model toward the Bateman model

NASA Astrophysics Data System (ADS)

López-Ruiz, F. F.; Guerrero, J.; Aldaya, V.; Cossío, F.

2012-08-01

Using a quantum version of the Arnold transformation of classical mechanics, all quantum dynamical systems whose classical equations of motion are non-homogeneous linear second-order ordinary differential equations (LSODE), including systems with friction linear in velocity such as the damped harmonic oscillator, can be related to the quantum free-particle dynamical system. This implies that symmetries and simple computations in the free particle can be exported to the LSODE-system. The quantum Arnold transformation is given explicitly for the damped harmonic oscillator, and an algebraic connection between the Caldirola-Kanai model for the damped harmonic oscillator and the Bateman system will be sketched out.

A simple white noise analysis of neuronal light responses.

PubMed

Chichilnisky, E J

2001-05-01

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

Quantum state engineering of light with continuous-wave optical parametric oscillators.

PubMed

Morin, Olivier; Liu, Jianli; Huang, Kun; Barbosa, Felippe; Fabre, Claude; Laurat, Julien

2014-05-30

Engineering non-classical states of the electromagnetic field is a central quest for quantum optics(1,2). Beyond their fundamental significance, such states are indeed the resources for implementing various protocols, ranging from enhanced metrology to quantum communication and computing. A variety of devices can be used to generate non-classical states, such as single emitters, light-matter interfaces or non-linear systems(3). We focus here on the use of a continuous-wave optical parametric oscillator(3,4). This system is based on a non-linear χ(2) crystal inserted inside an optical cavity and it is now well-known as a very efficient source of non-classical light, such as single-mode or two-mode squeezed vacuum depending on the crystal phase matching. Squeezed vacuum is a Gaussian state as its quadrature distributions follow a Gaussian statistics. However, it has been shown that number of protocols require non-Gaussian states(5). Generating directly such states is a difficult task and would require strong χ(3) non-linearities. Another procedure, probabilistic but heralded, consists in using a measurement-induced non-linearity via a conditional preparation technique operated on Gaussian states. Here, we detail this generation protocol for two non-Gaussian states, the single-photon state and a superposition of coherent states, using two differently phase-matched parametric oscillators as primary resources. This technique enables achievement of a high fidelity with the targeted state and generation of the state in a well-controlled spatiotemporal mode.

General Linearized Theory of Quantum Fluctuations around Arbitrary Limit Cycles

NASA Astrophysics Data System (ADS)

Navarrete-Benlloch, Carlos; Weiss, Talitha; Walter, Stefan; de Valcárcel, Germán J.

2017-09-01

The theory of Gaussian quantum fluctuations around classical steady states in nonlinear quantum-optical systems (also known as standard linearization) is a cornerstone for the analysis of such systems. Its simplicity, together with its accuracy far from critical points or situations where the nonlinearity reaches the strong coupling regime, has turned it into a widespread technique, being the first method of choice in most works on the subject. However, such a technique finds strong practical and conceptual complications when one tries to apply it to situations in which the classical long-time solution is time dependent, a most prominent example being spontaneous limit-cycle formation. Here, we introduce a linearization scheme adapted to such situations, using the driven Van der Pol oscillator as a test bed for the method, which allows us to compare it with full numerical simulations. On a conceptual level, the scheme relies on the connection between the emergence of limit cycles and the spontaneous breaking of the symmetry under temporal translations. On the practical side, the method keeps the simplicity and linear scaling with the size of the problem (number of modes) characteristic of standard linearization, making it applicable to large (many-body) systems.

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.

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

Classical multiparty computation using quantum resources

NASA Astrophysics Data System (ADS)

Clementi, Marco; Pappa, Anna; Eckstein, Andreas; Walmsley, Ian A.; Kashefi, Elham; Barz, Stefanie

2017-12-01

In this work, we demonstrate a way to perform classical multiparty computing among parties with limited computational resources. Our method harnesses quantum resources to increase the computational power of the individual parties. We show how a set of clients restricted to linear classical processing are able to jointly compute a nonlinear multivariable function that lies beyond their individual capabilities. The clients are only allowed to perform classical xor gates and single-qubit gates on quantum states. We also examine the type of security that can be achieved in this limited setting. Finally, we provide a proof-of-concept implementation using photonic qubits that allows four clients to compute a specific example of a multiparty function, the pairwise and.

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…

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

PubMed

Ojo, Oluwatobi Blessing; Lougue, Siaka; Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.

Modeling Music Emotion Judgments Using Machine Learning Methods

PubMed Central

Vempala, Naresh N.; Russo, Frank A.

2018-01-01

Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion. PMID:29354080

Modeling Music Emotion Judgments Using Machine Learning Methods.

PubMed

Vempala, Naresh N; Russo, Frank A

2017-01-01

Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

Quantum simulation from the bottom up: the case of rebits

NASA Astrophysics Data System (ADS)

Enshan Koh, Dax; Yuezhen Niu, Murphy; Yoder, Theodore J.

2018-05-01

Typically, quantum mechanics is thought of as a linear theory with unitary evolution governed by the Schrödinger equation. While this is technically true and useful for a physicist, with regards to computation it is an unfortunately narrow point of view. Just as a classical computer can simulate highly nonlinear functions of classical states, so too can the more general quantum computer simulate nonlinear evolutions of quantum states. We detail one particular simulation of nonlinearity on a quantum computer, showing how the entire class of -unitary evolutions (on n qubits) can be simulated using a unitary, real-amplitude quantum computer (consisting of n  +  1 qubits in total). These operators can be represented as the sum of a linear and antilinear operator, and add an intriguing new set of nonlinear quantum gates to the toolbox of the quantum algorithm designer. Furthermore, a subgroup of these nonlinear evolutions, called the -Cliffords, can be efficiently classically simulated, by making use of the fact that Clifford operators can simulate non-Clifford (in fact, non-linear) operators. This perspective of using the physical operators that we have to simulate non-physical ones that we do not is what we call bottom-up simulation, and we give some examples of its broader implications.

On new non-modal hydrodynamic stability modes and resulting non-exponential growth rates - a Lie symmetry approach

NASA Astrophysics Data System (ADS)

Oberlack, Martin; Nold, Andreas; Sanjon, Cedric Wilfried; Wang, Yongqi; Hau, Jan

2016-11-01

Classical hydrodynamic stability theory for laminar shear flows, no matter if considering long-term stability or transient growth, is based on the normal-mode ansatz, or, in other words, on an exponential function in space (stream-wise direction) and time. Recently, it became clear that the normal mode ansatz and the resulting Orr-Sommerfeld equation is based on essentially three fundamental symmetries of the linearized Euler and Navier-Stokes equations: translation in space and time and scaling of the dependent variable. Further, Kelvin-mode of linear shear flows seemed to be an exception in this context as it admits a fourth symmetry resulting in the classical Kelvin mode which is rather different from normal-mode. However, very recently it was discovered that most of the classical canonical shear flows such as linear shear, Couette, plane and round Poiseuille, Taylor-Couette, Lamb-Ossen vortex or asymptotic suction boundary layer admit more symmetries. This, in turn, led to new problem specific non-modal ansatz functions. In contrast to the exponential growth rate in time of the modal-ansatz, the new non-modal ansatz functions usually lead to an algebraic growth or decay rate, while for the asymptotic suction boundary layer a double-exponential growth or decay is observed.

The role of molecular conformation and polarizable embedding for one- and two-photon absorption of disperse orange 3 in solution.

PubMed

Silva, Daniel L; Murugan, N Arul; Kongsted, Jacob; Rinkevicius, Zilvinas; Canuto, Sylvio; Ågren, Hans

2012-07-19

Solvent effects on the one- and two-photon absorption (1PA and 2PA) of disperse orange 3 (DO3) in dimethyl sulfoxide (DMSO) are studied using a discrete polarizable embedding (PE) response theory. The scheme comprises a quantum region containing the chromophore and an atomically granulated classical region for the solvent accounting for full interactions within and between the two regions. Either classical molecular dynamics (MD) or hybrid Car-Parrinello (CP) quantum/classical (QM/MM) molecular dynamics simulations are employed to describe the solvation of DO3 in DMSO, allowing for an analysis of the effect of the intermolecular short-range repulsion, long-range attraction, and electrostatic interactions on the conformational changes of the chromophore and also the effect of the solute-solvent polarization. PE linear response calculations are performed to verify the character, solvatochromic shift, and overlap of the two lowest energy transitions responsible for the linear absorption spectrum of DO3 in DMSO in the visible spectral region. Results of the PE linear and quadratic response calculations, performed using uncorrelated solute-solvent configurations sampled from either the classical or hybrid CP QM/MM MD simulations, are used to estimate the width of the line shape function of the two electronic lowest energy excited states, which allow a prediction of the 2PA cross-sections without the use of empirical parameters. Appropriate exchange-correlation functionals have been employed in order to describe the charge-transfer process following the electronic transitions of the chromophore in solution.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4)

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-04-01

In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

Avoiding Communication in Dense Linear Algebra

DTIC Science & Technology

2013-08-16

Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Asymptotic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . 6...and parallelizing Strassen’s matrix multiplication algorithm (Chapter 11). 6 Chapter 2 Preliminaries 2.1 Notation and Definitions In this section we...between computations and algo- rithms). The following definition is based on [56]: Definition 2.1. A classical algorithm in linear algebra is one that

Linear Response Laws and Causality in Electrodynamics

ERIC Educational Resources Information Center

Yuffa, Alex J.; Scales, John A.

2012-01-01

Linear response laws and causality (the effect cannot precede the cause) are of fundamental importance in physics. In the context of classical electrodynamics, students often have a difficult time grasping these concepts because the physics is obscured by the intermingling of the time and frequency domains. In this paper, we analyse the linear…

Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies

ERIC Educational Resources Information Center

Letué, Frédérique; Martinez, Marie-José; Samson, Adeline; Vilain, Anne; Vilain, Coriandre

2018-01-01

Purpose: Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. Method: We propose a…

Creating Very True Quantum Algorithms for Quantum Energy Based Computing

NASA Astrophysics Data System (ADS)

Nagata, Koji; Nakamura, Tadao; Geurdes, Han; Batle, Josep; Abdalla, Soliman; Farouk, Ahmed; Diep, Do Ngoc

2018-04-01

An interpretation of quantum mechanics is discussed. It is assumed that quantum is energy. An algorithm by means of the energy interpretation is discussed. An algorithm, based on the energy interpretation, for fast determining a homogeneous linear function f( x) := s. x = s 1 x 1 + s 2 x 2 + ⋯ + s N x N is proposed. Here x = ( x 1, … , x N ), x j ∈ R and the coefficients s = ( s 1, … , s N ), s j ∈ N. Given the interpolation values (f(1), f(2),...,f(N))=ěc {y}, the unknown coefficients s = (s1(ěc {y}),\\dots , sN(ěc {y})) of the linear function shall be determined, simultaneously. The speed of determining the values is shown to outperform the classical case by a factor of N. Our method is based on the generalized Bernstein-Vazirani algorithm to qudit systems. Next, by using M parallel quantum systems, M homogeneous linear functions are determined, simultaneously. The speed of obtaining the set of M homogeneous linear functions is shown to outperform the classical case by a factor of N × M.

Creating Very True Quantum Algorithms for Quantum Energy Based Computing

NASA Astrophysics Data System (ADS)

Nagata, Koji; Nakamura, Tadao; Geurdes, Han; Batle, Josep; Abdalla, Soliman; Farouk, Ahmed; Diep, Do Ngoc

2017-12-01

An interpretation of quantum mechanics is discussed. It is assumed that quantum is energy. An algorithm by means of the energy interpretation is discussed. An algorithm, based on the energy interpretation, for fast determining a homogeneous linear function f(x) := s.x = s 1 x 1 + s 2 x 2 + ⋯ + s N x N is proposed. Here x = (x 1, … , x N ), x j ∈ R and the coefficients s = (s 1, … , s N ), s j ∈ N. Given the interpolation values (f(1), f(2),...,f(N))=ěc {y}, the unknown coefficients s = (s1(ěc {y}),\\dots , sN(ěc {y})) of the linear function shall be determined, simultaneously. The speed of determining the values is shown to outperform the classical case by a factor of N. Our method is based on the generalized Bernstein-Vazirani algorithm to qudit systems. Next, by using M parallel quantum systems, M homogeneous linear functions are determined, simultaneously. The speed of obtaining the set of M homogeneous linear functions is shown to outperform the classical case by a factor of N × M.

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.

Recurrence theorems: A unified account

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

Wallace, David, E-mail: david.wallace@balliol.ox.ac.uk

I discuss classical and quantum recurrence theorems in a unified manner, treating both as generalisations of the fact that a system with a finite state space only has so many places to go. Along the way, I prove versions of the recurrence theorem applicable to dynamics on linear and metric spaces and make some comments about applications of the classical recurrence theorem in the foundations of statistical mechanics.

An A Priori Multiobjective Optimization Model of a Search and Rescue Network

DTIC Science & Technology

1992-03-01

sequences. Classical sensitivity analysis and tolerance analysis were used to analyze the frequency assignments generated by the different weight...function for excess coverage of a frequency. Sensitivity analysis is used to investigate the robustness of the frequency assignments produced by the...interest. The linear program solution is used to produce classical sensitivity analysis for the weight ranges. 17 III. Model Formulation This chapter

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.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

PubMed

Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J

2016-11-01

A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

On solutions of the fifth-order dispersive equations with porous medium type non-linearity

NASA Astrophysics Data System (ADS)

Kocak, Huseyin; Pinar, Zehra

2018-07-01

In this work, we focus on obtaining the exact solutions of the fifth-order semi-linear and non-linear dispersive partial differential equations, which have the second-order diffusion-like (porous-type) non-linearity. The proposed equations were not studied in the literature in the sense of the exact solutions. We reveal solutions of the proposed equations using the classical Riccati equations method. The obtained exact solutions, which can play a key role to simulate non-linear waves in the medium with dispersion and diffusion, are illustrated and discussed in details.

Buying a Better Air Force

DTIC Science & Technology

2006-03-01

identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis

Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

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.

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.

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.

Electrophoresis in strong electric fields.

PubMed

Barany, Sandor

2009-01-01

Two kinds of non-linear electrophoresis (ef) that can be detected in strong electric fields (several hundred V/cm) are considered. The first ("classical" non-linear ef) is due to the interaction of the outer field with field-induced ionic charges in the electric double layer (EDL) under conditions, when field-induced variations of electrolyte concentration remain to be small comparatively to its equilibrium value. According to the Shilov theory, the non-linear component of the electrophoretic velocity for dielectric particles is proportional to the cubic power of the applied field strength (cubic electrophoresis) and to the second power of the particles radius; it is independent of the zeta-potential but is determined by the surface conductivity of particles. The second one, the so-called "superfast electrophoresis" is connected with the interaction of a strong outer field with a secondary diffuse layer of counterions (space charge) that is induced outside the primary (classical) diffuse EDL by the external field itself because of concentration polarization. The Dukhin-Mishchuk theory of "superfast electrophoresis" predicts quadratic dependence of the electrophoretic velocity of unipolar (ionically or electronically) conducting particles on the external field gradient and linear dependence on the particle's size in strong electric fields. These are in sharp contrast to the laws of classical electrophoresis (no dependence of V(ef) on the particle's size and linear dependence on the electric field gradient). A new method to measure the ef velocity of particles in strong electric fields is developed that is based on separation of the effects of sedimentation and electrophoresis using videoimaging and a new flowcell and use of short electric pulses. To test the "classical" non-linear electrophoresis, we have measured the ef velocity of non-conducting polystyrene, aluminium-oxide and (semiconductor) graphite particles as well as Saccharomice cerevisiae yeast cells as a function of the electric field strength, particle size, electrolyte concentration and the adsorbed polymer amount. It has been shown that the electrophoretic velocity of the particles/cells increases with field strength linearly up to about 100 and 200 V/cm (for cells) without and with adsorbed polymers both in pure water and in electrolyte solutions. In line with the theoretical predictions, in stronger fields substantial non-linear effects were recorded (V(ef)~E(3)). The ef velocity of unipolar ion-type conducting (ion-exchanger particles and fibres), electron-type conducting (magnesium and Mg/Al alloy) and semiconductor particles (graphite, activated carbon, pyrite, molybdenite) increases significantly with the electric field (V(ef)~E(2)) and the particle's size but is almost independent of the ionic strength. These trends are inconsistent with Smoluchowski's equation for dielectric particles, but are consistent with the Dukhin-Mishchuk theory of superfast electrophoresis.

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

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.

Tug-of-war between classical and multicenter bonds in H-(Be)n-H species

NASA Astrophysics Data System (ADS)

Lundell, Katie A.; Boldyrev, Alexander I.

2018-05-01

Quantum chemical calculations were performed for beryllium homocatenated compounds [H-(Be)n-H]. Global minimum structures were found using machine searches (Coalescence Kick method) with density functional theory. Chemical bonding analysis was performed with the Adaptive Natural Density Partitioning method. It was found that H-(Be)2-H and H-(Be)3-H clusters are linear with classical two-center two-electron bonds, while for n > 3, three-dimensional structures are more stable with multicenter bonding. Thus, at n = 4, multicenter bonding wins the tug-of-war vs. the classical bonding.

Classical r-matrices for the generalised Chern–Simons formulation of 3d gravity

NASA Astrophysics Data System (ADS)

Osei, Prince K.; Schroers, Bernd J.

2018-04-01

We study the conditions for classical r-matrices to be compatible with the generalised Chern–Simons action for 3d gravity. Compatibility means solving the classical Yang–Baxter equations with a prescribed symmetric part for each of the real Lie algebras and bilinear pairings arising in the generalised Chern–Simons action. We give a new construction of r-matrices via a generalised complexification and derive a non-linear set of matrix equations determining the most general compatible r-matrix. We exhibit new families of solutions and show that they contain some known r-matrices for special parameter values.

Quantum correction to classical gravitational interaction between two polarizable objects

NASA Astrophysics Data System (ADS)

Wu, Puxun; Hu, Jiawei; Yu, Hongwei

2016-12-01

When gravity is quantized, there inevitably exist quantum gravitational vacuum fluctuations which induce quadrupole moments in gravitationally polarizable objects and produce a quantum correction to the classical Newtonian interaction between them. Here, based upon linearized quantum gravity and the leading-order perturbation theory, we study, from a quantum field-theoretic prospect, this quantum correction between a pair of gravitationally polarizable objects treated as two-level harmonic oscillators. We find that the interaction potential behaves like r-11 in the retarded regime and r-10 in the near regime. Our result agrees with what were recently obtained in different approaches. Our study seems to indicate that linearized quantum gravity is robust in dealing with quantum gravitational effects at low energies.

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.

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).

Quantum computation with classical light: Implementation of the Deutsch-Jozsa algorithm

NASA Astrophysics Data System (ADS)

Perez-Garcia, Benjamin; McLaren, Melanie; Goyal, Sandeep K.; Hernandez-Aranda, Raul I.; Forbes, Andrew; Konrad, Thomas

2016-05-01

We propose an optical implementation of the Deutsch-Jozsa Algorithm using classical light in a binary decision-tree scheme. Our approach uses a ring cavity and linear optical devices in order to efficiently query the oracle functional values. In addition, we take advantage of the intrinsic Fourier transforming properties of a lens to read out whether the function given by the oracle is balanced or constant.

Linear and angular coherence momenta in the classical second-order coherence theory of vector electromagnetic fields.

PubMed

Wang, Wei; Takeda, Mitsuo

2006-09-01

A new concept of vector and tensor densities is introduced into the general coherence theory of vector electromagnetic fields that is based on energy and energy-flow coherence tensors. Related coherence conservation laws are presented in the form of continuity equations that provide new insights into the propagation of second-order correlation tensors associated with stationary random classical electromagnetic fields.

Geodesic least squares regression on information manifolds

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

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be

We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply thismore » to scaling laws in magnetic confinement fusion.« less

Classical and quantum non-linear optical applications using the Mach-Zehnder interferometer

NASA Astrophysics Data System (ADS)

Prescod, Andru

Mach Zehnder (MZ) modulators are widely employed in a variety of applications, such as optical communications, optical imaging, metrology and encryption. In this dissertation, we explore two non-linear MZ applications; one classified as classical and one as quantum, in which the Mach Zehnder interferometer is used. In the first application, a classical non-linear application, we introduce and study a new electro-optic highly linear (e.g., >130 dB) modulator configuration. This modulator makes use of a phase modulator (PM) in one arm of the MZ interferometer (MZI) and a ring resonator (RR) located on the other arm. The modulator performance is obtained through the control of a combination of internal and external parameters. These parameters include the RR-coupling ratio (internal parameter); the RF power split ratio and the RF phase bias (external parameters). Results show the unique and superior features, such as high linearity (SFDR˜133 dB), modulation bandwidth extension (as much as 70%) over the previously proposed and demonstrated Resonator-Assisted Mach Zehnder (RAMZ) design. Furthermore the proposed electro-optic modulator of this dissertation also provides an inherent SFDR compensation capability, even in cases where a significant waveguide optical loss exists. This design also shows potential for increased flexibility, practicality and ease of use. In the second application, a quantum non-linear application, we experimentally demonstrate quantum optical coherence tomography (QOCT) using a type II non-linear crystal (periodically-poled potassium titanyl phosphate (KTiOPO4) or PPKTP). There have been several publications discussing the merits and disadvantages of QOCT compared to OCT and other imaging techniques. First, we discuss the issues and solutions for increasing the efficiency of the quantum entangled photons. Second, we use a free space QOCT experiment to generate a high flux of these quantum entangled photons in two orthogonal polarizations, by parametric down-conversion. Third, by ensuring that these down-converted photons have the same frequency, spatial-temporal mode, and the same polarization when they interfere at a beam splitter, quantum interference should occur. Quantum interference of these entangled photons enables high resolution probing of dispersive samples.

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.

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.

Extending the Distributed Lag Model framework to handle chemical mixtures.

PubMed

Bello, Ghalib A; Arora, Manish; Austin, Christine; Horton, Megan K; Wright, Robert O; Gennings, Chris

2017-07-01

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures. Copyright © 2017 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;…

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

Quantum computation for solving linear systems

NASA Astrophysics Data System (ADS)

Cao, Yudong

Quantum computation is a subject born out of the combination between physics and computer science. It studies how the laws of quantum mechanics can be exploited to perform computations much more efficiently than current computers (termed classical computers as oppose to quantum computers). The thesis starts by introducing ideas from quantum physics and theoretical computer science and based on these ideas, introducing the basic concepts in quantum computing. These introductory discussions are intended for non-specialists to obtain the essential knowledge needed for understanding the new results presented in the subsequent chapters. After introducing the basics of quantum computing, we focus on the recently proposed quantum algorithm for linear systems. The new results include i) special instances of quantum circuits that can be implemented using current experimental resources; ii) detailed quantum algorithms that are suitable for a broader class of linear systems. We show that for some particular problems the quantum algorithm is able to achieve exponential speedup over their classical counterparts.

Numerical methods for coupled fracture problems

NASA Astrophysics Data System (ADS)

Viesca, Robert C.; Garagash, Dmitry I.

2018-04-01

We consider numerical solutions in which the linear elastic response to an opening- or sliding-mode fracture couples with one or more processes. Classic examples of such problems include traction-free cracks leading to stress singularities or cracks with cohesive-zone strength requirements leading to non-singular stress distributions. These classical problems have characteristic square-root asymptotic behavior for stress, relative displacement, or their derivatives. Prior work has shown that such asymptotics lead to a natural quadrature of the singular integrals at roots of Chebyhsev polynomials of the first, second, third, or fourth kind. We show that such quadratures lead to convenient techniques for interpolation, differentiation, and integration, with the potential for spectral accuracy. We further show that these techniques, with slight amendment, may continue to be used for non-classical problems which lack the classical asymptotic behavior. We consider solutions to example problems of both the classical and non-classical variety (e.g., fluid-driven opening-mode fracture and fault shear rupture driven by thermal weakening), with comparisons to analytical solutions or asymptotes, where available.

Effective dynamics of a classical point charge

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

Polonyi, Janos, E-mail: polonyi@iphc.cnrs.fr

2014-03-15

The effective Lagrangian of a point charge is derived by eliminating the electromagnetic field within the framework of the classical closed time path formalism. The short distance singularity of the electromagnetic field is regulated by an UV cutoff. The Abraham–Lorentz force is recovered and its similarity to quantum anomalies is underlined. The full cutoff-dependent linearized equation of motion is obtained, no runaway trajectories are found but the effective dynamics shows acausality if the cutoff is beyond the classical charge radius. The strength of the radiation reaction force displays a pole in its cutoff-dependence in a manner reminiscent of the Landau-polemore » of perturbative QED. Similarity between the dynamical breakdown of the time reversal invariance and dynamical symmetry breaking is pointed out. -- Highlights: •Extension of the classical action principle for dissipative systems. •New derivation of the Abraham–Lorentz force for a point charge. •Absence of a runaway solution of the Abraham–Lorentz force. •Acausality in classical electrodynamics. •Renormalization of classical electrodynamics of point charges.« less

Numerical Asymptotic Solutions Of Differential Equations

NASA Technical Reports Server (NTRS)

Thurston, Gaylen A.

1992-01-01

Numerical algorithms derived and compared with classical analytical methods. In method, expansions replaced with integrals evaluated numerically. Resulting numerical solutions retain linear independence, main advantage of asymptotic solutions.

On foundations of discrete element analysis of contact in diarthrodial joints.

PubMed

Volokh, K Y; Chao, E Y S; Armand, M

2007-06-01

Information about the stress distribution on contact surfaces of adjacent bones is indispensable for analysis of arthritis, bone fracture and remodeling. Numerical solution of the contact problem based on the classical approaches of solid mechanics is sophisticated and time-consuming. However, the solution can be essentially simplified on the following physical grounds. The bone contact surfaces are covered with a layer of articular cartilage, which is a soft tissue as compared to the hard bone. The latter allows ignoring the bone compliance in analysis of the contact problem, i.e. rigid bones are considered to interact through a compliant cartilage. Moreover, cartilage shear stresses and strains can be ignored because of the negligible friction between contacting cartilage layers. Thus, the cartilage can be approximated by a set of unilateral compressive springs normal to the bone surface. The forces in the springs can be computed from the equilibrium equations iteratively accounting for the changing contact area. This is the essence of the discrete element analysis (DEA). Despite the success in applications of DEA to various bone contact problems, its classical formulation required experimental validation because the springs approximating the cartilage were assumed linear while the real articular cartilage exhibited non-linear mechanical response in reported tests. Recent experimental results of Ateshian and his co-workers allow for revisiting the classical DEA formulation and establishing the limits of its applicability. In the present work, it is shown that the linear spring model is remarkably valid within a wide range of large deformations of the cartilage. It is also shown how to extend the classical DEA to the case of strong nonlinearity if necessary.

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.

Children with classic congenital adrenal hyperplasia have elevated serum leptin concentrations and insulin resistance: potential clinical implications.

PubMed

Charmandari, Evangelia; Weise, Martina; Bornstein, Stefan R; Eisenhofer, Graeme; Keil, Margaret F; Chrousos, George P; Merke, Deborah P

2002-05-01

Leptin is secreted by the white adipose tissue and modulates energy homeostasis. Nutritional, neural, neuroendocrine, paracrine, and autocrine factors, including the sympathetic nervous system and the adrenal medulla, have been implicated in the regulation of leptin secretion. Classic congenital adrenal hyperplasia (CAH) is characterized by a defect in cortisol and aldosterone secretion, impaired development and function of the adrenal medulla, and adrenal hyperandrogenism. To examine leptin secretion in patients with classic CAH in relation to their adrenomedullary function and insulin and androgen secretion, we studied 18 children with classic CAH (12 boys and 6 girls; age range 2-12 yr) and 28 normal children (16 boys and 12 girls; age range 5-12 yr) matched for body mass index (BMI). Serum leptin concentrations were significantly higher in patients with CAH than in control subjects (8.1 +/- 2.0 vs. 2.5 +/- 0.6 ng/ml, P = 0.01), and this difference persisted when leptin values were corrected for BMI. When compared with their normal counterparts, children with CAH had significantly lower plasma epinephrine (7.1 +/- 1.3 vs. 50.0 +/- 4.2, P < 0.001) and free metanephrine concentrations (18.4 +/- 2.4 vs. 46.5 +/- 4.0, P < 0.001) and higher fasting serum insulin (10.6 +/- 1.4 vs. 3.2 +/- 0.2 microU/ml, P < 0.001) and testosterone (23.7 +/- 5.3 vs. 4.6 +/- 0.5 ng/dl, P = 0.003) concentrations. Insulin resistance determined by the homeostasis model assessment method was significantly greater in children with classic CAH than in normal children (2.2 +/- 0.3 vs. 0.7 +/- 0.04, P < 0.001). Leptin concentrations were significantly and negatively correlated with epinephrine (r = -0.50, P = 0.001) and free metanephrine (r = -0.48, P = 0.002) concentrations. Stepwise multiple linear regression analysis indicated that serum leptin concentrations were best predicted by BMI in both patients and controls. Gender predicted serum leptin concentrations in controls but not in patients with classic CAH. No association was found between the dose of hydrocortisone and serum leptin (r = -0.17, P = 0.5) or insulin (r = 0.24, P = 0.3) concentrations in children with CAH. Our findings indicate that children with classic CAH have elevated fasting serum leptin and insulin concentrations, and insulin resistance. These most likely reflect differences in long-term adrenomedullary hypofunction and glucocorticoid therapy. Elevated leptin and insulin concentrations in patients with CAH may further enhance adrenal and ovarian androgen production, decrease the therapeutic efficacy of glucocorticoids, and contribute to later development of polycystic ovary syndrome and/or the metabolic syndrome and their complications.

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.

Fluid overpressures and strength of the sedimentary upper crust

NASA Astrophysics Data System (ADS)

Suppe, John

2014-12-01

The classic crustal strength-depth profile based on rock mechanics predicts a brittle strength σ1 -σ3 = κ(ρbar gz -Pf) that increases linearly with depth as a consequence of [1] the intrinsic brittle pressure dependence κ plus [2] an assumption of hydrostatic pore-fluid pressure, Pf = ρwgz. Many deep borehole stress data agree with a critical state of failure of this form. In contrast, fluid pressures greater than hydrostatic ρbar gz >Pf >ρw gz are normally observed in clastic continental margins and shale-rich mountain belts. Therefore we explore the predicted shapes of strength-depth profiles using data from overpressured regions, especially those dominated by the widespread disequilibrium-compaction mechanism, in which fluid pressures are hydrostatic above the fluid-retention depth zFRD and overpressured below, increasing parallel to the lithostatic gradient ρbar gz . Both brittle crustal strength and frictional fault strength below the zFRD must be constant with depth because effective stress (ρbar gz -Pf) is constant, in contrast with the classic linearly increasing profile. Borehole stress and fluid-pressure measurements in several overpressured deforming continental margins agree with this constant-strength prediction, with the same pressure-dependence κ as the overlying hydrostatic strata. The role of zFRD in critical-taper wedge mechanics and jointing is illustrated. The constant-strength approximation is more appropriate for overpressured crust than classic linearly increasing models.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Use of biopartitioning micellar chromatography and RP-HPLC for the determination of blood-brain barrier penetration of α-adrenergic/imidazoline receptor ligands, and QSPR analysis.

PubMed

Vucicevic, J; Popovic, M; Nikolic, K; Filipic, S; Obradovic, D; Agbaba, D

2017-03-01

For this study, 31 compounds, including 16 imidazoline/α-adrenergic receptor (IRs/α-ARs) ligands and 15 central nervous system (CNS) drugs, were characterized in terms of the retention factors (k) obtained using biopartitioning micellar and classical reversed phase chromatography (log k BMC and log k wRP , respectively). Based on the retention factor (log k wRP ) and slope of the linear curve (S) the isocratic parameter (φ 0 ) was calculated. Obtained retention factors were correlated with experimental log BB values for the group of examined compounds. High correlations were obtained between logarithm of biopartitioning micellar chromatography (BMC) retention factor and effective permeability (r(log k BMC /log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log k wRP /log BB): 0.58; r(S/log BB): -0.50; r(φ 0 /P e ): 0.61). Based on the log k BMC retention data and calculated molecular parameters of the examined compounds, quantitative structure-permeability relationship (QSPR) models were developed using partial least squares, stepwise multiple linear regression, support vector machine and artificial neural network methodologies. A high degree of structural diversity of the analysed IRs/α-ARs ligands and CNS drugs provides wide applicability domain of the QSPR models for estimation of blood-brain barrier penetration of the related compounds.

Accurate measurements of the true column efficiency and of the instrument band broadening contributions in the presence of a chromatographic column.

PubMed

Gritti, Fabrice; Guiochon, Georges

2014-01-31

A rapid and simple validated experimental protocol is proposed for the accurate determination of the true intrinsic column efficiency and for that of the variance of the extra-column volume of the instrument used, the latter being obtained without requiring the removal of the chromatographic column from the HPLC system. This protocol was applied to 2.1mm×100mm columns packed with sub-3 (2.7μm Halo Peptide ES-C18) and sub-2μm (1.6μm prototype) core-shell particles. It was validated by observing the linear behavior of the plot of the apparent column plate height versus the reciprocal of (1+k')(2) for at least three homologous compounds, with a linear regression coefficient R(2) larger than 0.999. Irrespective of the contribution of the several, different instruments used to the total band broadening, the same column HETP value was obtained within 5%. This new protocol outperform the classical one in which the chromatographic column is replaced with a zero dead volume (ZDV) union connector to measure the extra-column volume variance, which is subtracted from the variance measured with the column to measure the intrinsic HETP. This protocol fails because it significantly underestimates the system volume variance. Copyright © 2013 Elsevier B.V. All rights reserved.

Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept

PubMed Central

Bolduc, David L.; Villa, Vilmar; Sandgren, David J.; Ledney, G. David; Blakely, William F.; Bünger, Rolf

2014-01-01

Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (Macaca mulatta) given 6.5-Gy 60Co Υ-rays (0.4 Gy min−1) TBI. A correlation matrix was formulated with the RC3 severity level designated as the “dependent variable” and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the “CBC” RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry “CBC-SCHEM” RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness. PMID:25165485

Charge-transfer complexes of 2,3-dichloro-5,6-dicyano-1,4-benzoquinone with amino molecules in polar solvents

NASA Astrophysics Data System (ADS)

Berto, Silvia; Chiavazza, Enrico; Ribotta, Valentina; Daniele, Pier Giuseppe; Barolo, Claudia; Giacomino, Agnese; Vione, Davide; Malandrino, Mery

2015-10-01

The charge-transfer complexes have scientific relevance because this type of molecular interaction is at the basis of the activity of pharmacological compounds and because the absorption bands of the complexes can be used for the quantification of electron donor molecules. This work aims to assess the stability of the charge-transfer complexes between the electron acceptor 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) and two drugs, procaine and atenolol, in acetonitrile and ethanol. The stability of DDQ in solution and the time required to obtain the maximum complex formation were evaluated. The stoichiometry and the stability of the complexes were determined, respectively, by Job's plot method and by the elaboration of UV-vis titrations data. The latter task was carried out by using the non-linear global analysis approach to determine the equilibrium constants. This approach to data elaboration allowed us to overcome the disadvantages of the classical linear-regression method, to obtain reliable values of the association constants and to calculate the entire spectra of the complexes. NMR spectra were recorded to identify the portion of the donor molecule that was involved in the interaction. The data support the participation of the aliphatic amino groups in complex formation and exclude the involvement of the aromatic amine present in the procaine molecule.

Measurement error in environmental epidemiology and the shape of exposure-response curves.

PubMed

Rhomberg, Lorenz R; Chandalia, Juhi K; Long, Christopher M; Goodman, Julie E

2011-09-01

Both classical and Berkson exposure measurement errors as encountered in environmental epidemiology data can result in biases in fitted exposure-response relationships that are large enough to affect the interpretation and use of the apparent exposure-response shapes in risk assessment applications. A variety of sources of potential measurement error exist in the process of estimating individual exposures to environmental contaminants, and the authors review the evaluation in the literature of the magnitudes and patterns of exposure measurement errors that prevail in actual practice. It is well known among statisticians that random errors in the values of independent variables (such as exposure in exposure-response curves) may tend to bias regression results. For increasing curves, this effect tends to flatten and apparently linearize what is in truth a steeper and perhaps more curvilinear or even threshold-bearing relationship. The degree of bias is tied to the magnitude of the measurement error in the independent variables. It has been shown that the degree of bias known to apply to actual studies is sufficient to produce a false linear result, and that although nonparametric smoothing and other error-mitigating techniques may assist in identifying a threshold, they do not guarantee detection of a threshold. The consequences of this could be great, as it could lead to a misallocation of resources towards regulations that do not offer any benefit to public health.

Toward an in situ phosphate sensor in seawater using Square Wave Voltammetry.

PubMed

Barus, C; Romanytsia, I; Striebig, N; Garçon, V

2016-11-01

A Square Wave Voltammetry electrochemical method is proposed to measure phosphate in seawater as pulse techniques offer a higher sensitivity as compared to classical cyclic voltammetry. Chronoamperometry cannot be either adapted for an in situ sensor since this method requires to have controlled convection which will be impossible in a miniaturised sensor. Tests and validation of Square Wave Voltammetry parameters have been performed using an open cell and for the first time with a small volume (<400µL) laboratory prototypes. Two designs of prototypes have been compared. Using high frequency (f=250Hz) allows to obtain a linear behaviour between 0.1 and 1µmolL(-1) with a very low limit of detection of 0.05 µmolL(-1) after 60min of complexation waiting time. In order to obtain a linear regression for a larger concentration range i.e. 0.25-4µmolL(-1), a lower frequency of 2.5Hz is needed. A limit of detection of 0.1µmolL(-1) is obtained in this case after 30min of complexation waiting time for the peak measured at E=0.12V. Changing the position of the molybdenum electrode for the complexation step and moving the detection into another electrochemical cell allow to decrease the reaction time down to 5min. Copyright © 2016 Elsevier B.V. All rights reserved.

Changing Trends in the Clinical Presentation and Management of Complete Hydatidiform Mole Among Brazilian Women.

PubMed

Braga, Antonio; Moraes, Valéria; Maestá, Izildinha; Amim Júnior, Joffre; Rezende-Filho, Jorge de; Elias, Kevin; Berkowitz, Ross

2016-06-01

The aim of the study was to evaluate potential changes in the clinical, diagnostic, and therapeutic parameters of complete hydatidiform mole in the last 25 years in Brazil. A retrospective cohort study was conducted involving the analysis of 2163 medical records of patients diagnosed with complete hydatidiform mole who received treatment at the Rio de Janeiro Reference Center for Gestational Trophoblastic Disease between January 1988 and December 2012. For the statistical analysis of the natural history of the patients with complete molar pregnancies, time series were evaluated using the Cox-Stuart test and adjusted by linear regression models. A downward linear temporal trend was observed for gestational age of complete hydatidiform mole at diagnosis, which is also reflected in the reduced occurrence of vaginal bleeding, hyperemesis and pre-eclampsia. We also observed an increase in the use of uterine vacuum aspiration to treat molar pregnancy. Although the duration of postmolar follow-up was found to decline, this was not accompanied by any alteration in the time to remission of the disease or its progression to gestational trophoblastic neoplasia. Early diagnosis of complete hydatidiform mole has altered the natural history of molar pregnancy, especially with a reduction in classical clinical symptoms. However, early diagnosis has not resulted in a reduction in the development of gestational trophoblastic neoplasia, a dilemma that still challenges professionals working with gestational trophoblastic disease.

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.

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

Application of variational and Galerkin equations to linear and nonlinear finite element analysis

NASA Technical Reports Server (NTRS)

Yu, Y.-Y.

1974-01-01

The paper discusses the application of the variational equation to nonlinear finite element analysis. The problem of beam vibration with large deflection is considered. The variational equation is shown to be flexible in both the solution of a general problem and in the finite element formulation. Difficulties are shown to arise when Galerkin's equations are used in the consideration of the finite element formulation of two-dimensional linear elasticity and of the linear classical beam.

Multilevel Preconditioners for Reaction-Diffusion Problems with Discontinuous Coefficients

DOE PAGES

Kolev, Tzanio V.; Xu, Jinchao; Zhu, Yunrong

2015-08-23

In this study, we extend some of the multilevel convergence results obtained by Xu and Zhu, to the case of second order linear reaction-diffusion equations. Specifically, we consider the multilevel preconditioners for solving the linear systems arising from the linear finite element approximation of the problem, where both diffusion and reaction coefficients are piecewise-constant functions. We discuss in detail the influence of both the discontinuous reaction and diffusion coefficients to the performance of the classical BPX and multigrid V-cycle preconditioner.

Frequency analysis via the method of moment functionals

NASA Technical Reports Server (NTRS)

Pearson, A. E.; Pan, J. Q.

1990-01-01

Several variants are presented of a linear-in-parameters least squares formulation for determining the transfer function of a stable linear system at specified frequencies given a finite set of Fourier series coefficients calculated from transient nonstationary input-output data. The basis of the technique is Shinbrot's classical method of moment functionals using complex Fourier based modulating functions to convert a differential equation model on a finite time interval into an algebraic equation which depends linearly on frequency-related parameters.

Numerical studies on the electromagnetic properties of the nonlinear Lorentz Computational model for the dielectric media

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

Abe, H.; Okuda, H.

We study linear and nonlinear properties of a new computer simulation model developed to study the propagation of electromagnetic waves in a dielectric medium in the linear and nonlinear regimes. The model is constructed by combining a microscopic model used in the semi-classical approximation for the dielectric media and the particle model developed for the plasma simulations. It is shown that the model may be useful for studying linear and nonlinear wave propagation in the dielectric media.

On the derivation of linear irreversible thermodynamics for classical fluids

PubMed Central

Theodosopulu, M.; Grecos, A.; Prigogine, I.

1978-01-01

We consider the microscopic derivation of the linearized hydrodynamic equations for an arbitrary simple fluid. Our discussion is based on the concept of hydrodynamical modes, and use is made of the ideas and methods of the theory of subdynamics. We also show that this analysis leads to the Gibbs relation for the entropy of the system. PMID:16592516

Feature Extraction of High-Dimensional Structures for Exploratory Analytics

DTIC Science & Technology

2013-04-01

Comparison of Euclidean vs. geodesic distance. LDRs use metric based on the Euclidean distance between two points, while the NLDRs are based on...geodesic distance. An NLDR successfully unrolls the curved manifold, whereas an LDR fails. ...........................3 1 1. Introduction An...and classical metric multidimensional scaling, are a linear DR ( LDR ). An LDR is based on a linear combination of

Lagrangians and Euler morphisms from connections on the frame bundle

NASA Astrophysics Data System (ADS)

Kurek, J.; Mikulski, W. M.

2011-07-01

We classify all natural operators transforming torsion free classical linear connections ∇ on m-dimensional manifolds M into r-th order Lagrangians λ(∇) and Euler morphisms E(∇) on the linear frame bundle P1M. We also briefly write how this classification result can be generalized on higher order frame bundles PkM instead of P1M.

New Equating Methods and Their Relationships with Levine Observed Score Linear Equating under the Kernel Equating Framework

ERIC Educational Resources Information Center

Chen, Haiwen; Holland, Paul

2010-01-01

In this paper, we develop a new curvilinear equating for the nonequivalent groups with anchor test (NEAT) design under the assumption of the classical test theory model, that we name curvilinear Levine observed score equating. In fact, by applying both the kernel equating framework and the mean preserving linear transformation of…

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

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.

Meissner effect in normal-superconducting proximity-contact double layers

NASA Astrophysics Data System (ADS)

Higashitani, Seiji; Nagai, Katsuhiko

1995-02-01

The Meissner effect in normal-superconducting proximity-contact double layers is discussed in the clean limit. The diamagnetic current is calculated using the quasi-classical Green's function. We obtain the quasi-classical Green's function linear in the vector potential in the proximity-contact double layers with a finite reflection coefficient at the interface. It is found that the diamagnetic current in the clean normal layer is constant in space, therefore, the magnetic field linearly decreases in the clean normal layer. We give an explicit expression for the screening length in the clean normal layer and study its temperature dependence. We show that the temperature dependence in the clean normal layer is considerably different from that in the dirty normal layer and agrees with a recent experiment in Au-Nb system.

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.

Non-Classical Smoothening of Nano-Scale Surface Corrugations

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

Aziz, Michael J.; Chason, Eric; Erlebacher, Jonah

1999-05-20

We report the first experimental observation of non-classical morphological equilibration of a corrugated crystalline surface. Periodic rippled structures with wavelengths of 290-550 nm were made on Si(OO1) by sputter rippling and then annealed at 650 - 750 °C. In contrast to the classical exponential decay with time, the ripple amplitude, A {lambda}(t), followed an inverse linear decay, A {lambda}(t)= A {lambda}(0)/(1 +k {lambda}t), agreeing with a prediction of Ozdemir and Zangwill. We measure the activation energy for surface relaxation to be 1.6±0.2 eV, consistent with an interpretation that dimers mediate transport.

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

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

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)

Quantum machine learning: a classical perspective

NASA Astrophysics Data System (ADS)

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Quantum machine learning: a classical perspective

PubMed Central

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. PMID:29434508

Quantum machine learning: a classical perspective.

PubMed

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

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.

Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.

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...

Novel point estimation from a semiparametric ratio estimator (SPRE): long-term health outcomes from short-term linear data, with application to weight loss in obesity.

PubMed

Weissman-Miller, Deborah

2013-11-02

Point estimation is particularly important in predicting weight loss in individuals or small groups. In this analysis, a new health response function is based on a model of human response over time to estimate long-term health outcomes from a change point in short-term linear regression. This important estimation capability is addressed for small groups and single-subject designs in pilot studies for clinical trials, medical and therapeutic clinical practice. These estimations are based on a change point given by parameters derived from short-term participant data in ordinary least squares (OLS) regression. The development of the change point in initial OLS data and the point estimations are given in a new semiparametric ratio estimator (SPRE) model. The new response function is taken as a ratio of two-parameter Weibull distributions times a prior outcome value that steps estimated outcomes forward in time, where the shape and scale parameters are estimated at the change point. The Weibull distributions used in this ratio are derived from a Kelvin model in mechanics taken here to represent human beings. A distinct feature of the SPRE model in this article is that initial treatment response for a small group or a single subject is reflected in long-term response to treatment. This model is applied to weight loss in obesity in a secondary analysis of data from a classic weight loss study, which has been selected due to the dramatic increase in obesity in the United States over the past 20 years. A very small relative error of estimated to test data is shown for obesity treatment with the weight loss medication phentermine or placebo for the test dataset. An application of SPRE in clinical medicine or occupational therapy is to estimate long-term weight loss for a single subject or a small group near the beginning of treatment.

Removal of pesticides from aqueous solution: Quantitative relationship between activated carbon characteristics and adsorption properties.

PubMed

Cougnaud, A; Faur, C; Le Cloirec, P

2005-08-01

The adsorption of pesticides (atrazin, atrazin-desethyl and triflusulfuron-methyl) from aqueous solution is performed by activated carbon fibers (ACF) and granular activated carbons (GAC) in static and dynamic reactors, in order to study the co-influence of adsorbent and adsorbate characteristics on the adsorption mechanisms. First, mono-component adsorption equilibrium is carried out in a batch reactor for a wide range of concentrations (from 5 microg 1(-1) to 21.4 mg 1(-1)). Classic models, like Freundlich and Langmuir equations, are applied: the maximum adsorption capacities are high, ranging between 63 and 509 mg g(-1). The comparison of single-solute isotherms tends to confirm the decisive role of the adsorbent properties in the adsorption capacity of pesticides by the activated carbons: the performance of ACF is significantly higher than that of GAC due to a narrower pore size distribution of fibers in the area of micropores. Furthermore, their small diameter (10 microm compared with 1 mm for grains) enables faster adsorption kinetics because of the larger surface area exposed to the fluid. The influence of adsorbate size is also demonstrated. A multiple linear regression enables the co-influence of adsorbent and adsorbate properties to be quantified, a relationship being assessed between Langmuir maximum adsorption capacity and pesticide molecular weight and adsorbent diameter (R2 = 0.90). Secondly, the adsorption of the three pesticides is studied in a dynamic reactor: in this case, the influence of operating conditions (inlet concentration C0, flow velocity U0) is also taken into account. As the initial concentration or flow velocity decreases, the column performance significantly improves. Both operating factors are included in a multiple linear regression (R2 = 0.91) used to predict saturation adsorption capacity, with molecular weight and particle diameter being again designed as influent explicative variables.

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.

Music preferences with hearing aids: effects of signal properties, compression settings, and listener characteristics.

PubMed

Croghan, Naomi B H; Arehart, Kathryn H; Kates, James M

2014-01-01

Current knowledge of how to design and fit hearing aids to optimize music listening is limited. Many hearing-aid users listen to recorded music, which often undergoes compression limiting (CL) in the music industry. Therefore, hearing-aid users may experience twofold effects of compression when listening to recorded music: music-industry CL and hearing-aid wide dynamic-range compression (WDRC). The goal of this study was to examine the roles of input-signal properties, hearing-aid processing, and individual variability in the perception of recorded music, with a focus on the effects of dynamic-range compression. A group of 18 experienced hearing-aid users made paired-comparison preference judgments for classical and rock music samples using simulated hearing aids. Music samples were either unprocessed before hearing-aid input or had different levels of music-industry CL. Hearing-aid conditions included linear gain and individually fitted WDRC. Combinations of four WDRC parameters were included: fast release time (50 msec), slow release time (1,000 msec), three channels, and 18 channels. Listeners also completed several psychophysical tasks. Acoustic analyses showed that CL and WDRC reduced temporal envelope contrasts, changed amplitude distributions across the acoustic spectrum, and smoothed the peaks of the modulation spectrum. Listener judgments revealed that fast WDRC was least preferred for both genres of music. For classical music, linear processing and slow WDRC were equally preferred, and the main effect of number of channels was not significant. For rock music, linear processing was preferred over slow WDRC, and three channels were preferred to 18 channels. Heavy CL was least preferred for classical music, but the amount of CL did not change the patterns of WDRC preferences for either genre. Auditory filter bandwidth as estimated from psychophysical tuning curves was associated with variability in listeners' preferences for classical music. Fast, multichannel WDRC often leads to poor music quality, whereas linear processing or slow WDRC are generally preferred. Furthermore, the effect of WDRC is more important for music preferences than music-industry CL applied to signals before the hearing-aid input stage. Variability in hearing-aid users' perceptions of music quality may be partially explained by frequency resolution abilities.

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.

High-Order Accurate Solutions to the Helmholtz Equation in the Presence of Boundary Singularities

DTIC Science & Technology

2015-03-31

FD scheme is only consistent for classical solutions of the PDE . For this reason, we implement the method of singularity subtraction as a means for...regularity due to the boundary conditions. This is because the FD scheme is only consistent for classical solutions of the PDE . For this reason, we...Introduction In the present work, we develop a high-order numerical method for solving linear elliptic PDEs with well-behaved variable coefficients on

Asymptotics of quasi-classical localized states in 2D system of charged hard-core bosons

NASA Astrophysics Data System (ADS)

Panov, Yu. D.; Moskvin, A. S.

2018-05-01

The continuous quasi-classical two-sublattice approximation is constructed for the 2D system of charged hard-core bosons to explore metastable inhomogeneous states analogous to inhomogeneous localized excitations in magnetic systems. The types of localized excitations are determined by asymptotic analysis and compared with numerical results. Depending on the homogeneous ground state, the excitations are the ferro and antiferro type vortices, the skyrmion-like topological excitations or linear domain walls.

The Physics of Vibration

NASA Astrophysics Data System (ADS)

Pippard, A. B.

1989-11-01

The study of vibration in physical systems is an important part of almost all fields in physics and engineering. This work, originally published in two volumes, examines the classical aspects in Part I and the quantum oscillator in Part II. The classical linear vibrator is treated first and the underlying unity of all linear oscillations in electrical, mechanical and acoustic systems is emphasized. Following this the book turns to the treatment of nonlinear vibrations, a field with which engineers and physicists are generally less familiar. In Part II the emphasis turns to quantum systems, that is those systems which can only be adequately described by quantum mechanics. The treatment concentrates on vibrations in atoms and molecules and their interaction with electromagnetic radiation. The similarities of classical and quantum methods are stressed and the limits of the classical treatment are examined. Throughout the book, each phenomenon discussed is illustrated with many examples and theory and experiment are compared. Although the reader may find that the physics discussed is demanding and the concepts are subtle in places, all mathematics used is familiar to both engineers and experimental scientists. Although not a textbook this is a useful introduction to the more advanced mathematical treatment of vibrations as it bridges the gap between the basic principles and more specialized concepts. It will be of great interest to advanced undergraduates and postgraduates as well as applied mathematicians, physicists and engineers in university and industry.

Periodic solutions of second-order nonlinear difference equations containing a small parameter. II - Equivalent linearization

NASA Technical Reports Server (NTRS)

Mickens, R. E.

1985-01-01

The classical method of equivalent linearization is extended to a particular class of nonlinear difference equations. It is shown that the method can be used to obtain an approximation of the periodic solutions of these equations. In particular, the parameters of the limit cycle and the limit points can be determined. Three examples illustrating the method are presented.

An O({radical}nL) primal-dual affine scaling algorithm for linear programming

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

Huang, Siming

1994-12-31

We present a new primal-dual affine scaling algorithm for linear programming. The search direction of the algorithm is a combination of classical affine scaling direction of Dikin and a recent new affine scaling direction of Jansen, Roos and Terlaky. The algorithm has an iteration complexity of O({radical}nL), comparing to O(nL) complexity of Jansen, Roos and Terlaky.

Intelligent Distributed Systems

DTIC Science & Technology

2015-10-23

periodic gossiping algorithms by using convex combination rules rather than standard averaging rules. On a ring graph, we have discovered how to sequence...the gossips within a period to achieve the best possible convergence rate and we have related this optimal value to the classic edge coloring problem...consensus. There are three different approaches to distributed averaging: linear iterations, gossiping , and dou- ble linear iterations which are also known as

CONTRIBUTIONS TO RATIONAL APPROXIMATION,

DTIC Science & Technology

Some of the key results of linear Chebyshev approximation theory are extended to generalized rational functions. Prominent among these is Haar’s...linear theorem which yields necessary and sufficient conditions for uniqueness. Some new results in the classic field of rational function Chebyshev...Furthermore a Weierstrass type theorem is proven for rational Chebyshev approximation. A characterization theorem for rational trigonometric Chebyshev approximation in terms of sign alternation is developed. (Author)

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.

Accurate frequency domain measurement of the best linear time-invariant approximation of linear time-periodic systems including the quantification of the time-periodic distortions

NASA Astrophysics Data System (ADS)

Louarroudi, E.; Pintelon, R.; Lataire, J.

2014-10-01

Time-periodic (TP) phenomena occurring, for instance, in wind turbines, helicopters, anisotropic shaft-bearing systems, and cardiovascular/respiratory systems, are often not addressed when classical frequency response function (FRF) measurements are performed. As the traditional FRF concept is based on the linear time-invariant (LTI) system theory, it is only approximately valid for systems with varying dynamics. Accordingly, the quantification of any deviation from this ideal LTI framework is more than welcome. The “measure of deviation” allows us to define the notion of the best LTI (BLTI) approximation, which yields the best - in mean square sense - LTI description of a linear time-periodic LTP system. By taking into consideration the TP effects, it is shown in this paper that the variability of the BLTI measurement can be reduced significantly compared with that of classical FRF estimators. From a single experiment, the proposed identification methods can handle (non-)linear time-periodic [(N)LTP] systems in open-loop with a quantification of (i) the noise and/or the NL distortions, (ii) the TP distortions and (iii) the transient (leakage) errors. Besides, a geometrical interpretation of the BLTI approximation is provided, leading to a framework called vector FRF analysis. The theory presented is supported by numerical simulations as well as real measurements mimicking the well-known mechanical Mathieu oscillator.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of the Modified Glasgow Coma Scale Score Criteria for Mild Traumatic Brain Injury on Mortality Prediction: Comparing Classic and Modified Glasgow Coma Scale Score Model Scores of 13

PubMed Central

Mena, Jorge Humberto; Sanchez, Alvaro Ignacio; Rubiano, Andres M.; Peitzman, Andrew B.; Sperry, Jason L.; Gutierrez, Maria Isabel; Puyana, Juan Carlos

2011-01-01

Objective The Glasgow Coma Scale (GCS) classifies Traumatic Brain Injuries (TBI) as Mild (14–15); Moderate (9–13) or Severe (3–8). The ATLS modified this classification so that a GCS score of 13 is categorized as mild TBI. We investigated the effect of this modification on mortality prediction, comparing patients with a GCS of 13 classified as moderate TBI (Classic Model) to patients with GCS of 13 classified as mild TBI (Modified Model). Methods We selected adult TBI patients from the Pennsylvania Outcome Study database (PTOS). Logistic regressions adjusting for age, sex, cause, severity, trauma center level, comorbidities, and isolated TBI were performed. A second evaluation included the time trend of mortality. A third evaluation also included hypothermia, hypotension, mechanical ventilation, screening for drugs, and severity of TBI. Discrimination of the models was evaluated using the area under receiver operating characteristic curve (AUC). Calibration was evaluated using the Hoslmer-Lemershow goodness of fit (GOF) test. Results In the first evaluation, the AUCs were 0.922 (95 %CI, 0.917–0.926) and 0.908 (95 %CI, 0.903–0.912) for classic and modified models, respectively. Both models showed poor calibration (p<0.001). In the third evaluation, the AUCs were 0.946 (95 %CI, 0.943 – 0.949) and 0.938 (95 %CI, 0.934 –0.940) for the classic and modified models, respectively, with improvements in calibration (p=0.30 and p=0.02 for the classic and modified models, respectively). Conclusion The lack of overlap between ROC curves of both models reveals a statistically significant difference in their ability to predict mortality. The classic model demonstrated better GOF than the modified model. A GCS of 13 classified as moderate TBI in a multivariate logistic regression model performed better than a GCS of 13 classified as mild. PMID:22071923

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.

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…

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.

Safety of the lateral trauma position in cervical spine injuries: a cadaver model study.

PubMed

Hyldmo, P K; Horodyski, M B; Conrad, B P; Dubose, D N; Røislien, J; Prasarn, M; Rechtine, G R; Søreide, E

2016-08-01

Endotracheal intubation is not always an option for unconscious trauma patients. Prehospital personnel are then faced with the dilemma of maintaining an adequate airway without risking deleterious movement of a potentially unstable cervical spine. To address these two concerns various alternatives to the classical recovery position have been developed. This study aims to determine the amount of motion induced by the recovery position, two versions of the HAINES (High Arm IN Endangered Spine) position, and the novel lateral trauma position (LTP). We surgically created global cervical instability between the C5 and C6 vertebrae in five fresh cadavers. We measured the rotational and translational (linear) range of motion during the different maneuvers using an electromagnetic tracking device and compared the results using a general linear mixed model (GLMM) for regression. In the recovery position, the range of motion for lateral bending was 11.9°. While both HAINES positions caused a similar range of motion, the motion caused by the LTP was 2.6° less (P = 0.037). The linear axial range of motion in the recovery position was 13.0 mm. In comparison, the HAINES 1 and 2 positions showed significantly less motion (-5.8 and -4.6 mm, respectively), while the LTP did not (-4.0 mm, P = 0.067). Our results indicate that in unconscious trauma patients, the LTP or one of the two HAINES techniques is preferable to the standard recovery position in cases of an unstable cervical spine injury. © 2016 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.

Stunting at birth: recognition of early-life linear growth failure in the western highlands of Guatemala.

PubMed

Solomons, Noel W; Vossenaar, Marieke; Chomat, Anne-Marie; Doak, Colleen M; Koski, Kristine G; Scott, Marilyn E

2015-07-01

Measurements of length at birth, or in the neonatal period, are challenging to obtain and often discounted for lack of validity. Hence, classical 'under-5' stunting rates have been derived from surveys on children from 6 to 59 months of age. Guatemala has a high prevalence of stunting (49.8%), but the age of onset of growth failure is not clearly defined. The objective of the study was to assess length-for-age within the first 1.5 months of life among Guatemalan infants. As part of a cross-sectional observational study, supine length was measured in young infants. Mothers' height was measured. Length-for-age Z-scores (HAZ) were generated and stunting was defined as HAZ <-2 using WHO growth standards. Eight rural, indigenous Mam-Mayan villages (n 200, 100% of Mayan indigenous origin) and an urban clinic of Quetzaltenango (n 106, 27% of Mayan indigenous origin), Guatemala. Three hundred and six newborns with a median age of 19 d. The median rural HAZ was -1.56 and prevalence of stunting was 38%; the respective urban values were -1.41 and 25%. Linear regression revealed no relationship between infant age and HAZ (r = 0.101, r(2) = 0.010, P = 0.077). Maternal height explained 3% of the variability in HAZ (r = 0.171, r(2) = 0.029, P = 0.003). Stunting must be carried over from in utero growth retardation in short-stature Guatemalan mothers. As linear growth failure in this setting begins in utero, its prevention must be linked to maternal care strategies during gestation, or even before. A focus on maternal nutrition and health in an intergenerational dimension is needed to reduce its prevalence.

Efficient classical simulation of the Deutsch-Jozsa and Simon's algorithms

NASA Astrophysics Data System (ADS)

Johansson, Niklas; Larsson, Jan-Åke

2017-09-01

A long-standing aim of quantum information research is to understand what gives quantum computers their advantage. This requires separating problems that need genuinely quantum resources from those for which classical resources are enough. Two examples of quantum speed-up are the Deutsch-Jozsa and Simon's problem, both efficiently solvable on a quantum Turing machine, and both believed to lack efficient classical solutions. Here we present a framework that can simulate both quantum algorithms efficiently, solving the Deutsch-Jozsa problem with probability 1 using only one oracle query, and Simon's problem using linearly many oracle queries, just as expected of an ideal quantum computer. The presented simulation framework is in turn efficiently simulatable in a classical probabilistic Turing machine. This shows that the Deutsch-Jozsa and Simon's problem do not require any genuinely quantum resources, and that the quantum algorithms show no speed-up when compared with their corresponding classical simulation. Finally, this gives insight into what properties are needed in the two algorithms and calls for further study of oracle separation between quantum and classical computation.

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.

Does ℏ play a role in multidimensional spectroscopy? Reduced hierarchy equations of motion approach to molecular vibrations.

PubMed

Sakurai, Atsunori; Tanimura, Yoshitaka

2011-04-28

To investigate the role of quantum effects in vibrational spectroscopies, we have carried out numerically exact calculations of linear and nonlinear response functions for an anharmonic potential system nonlinearly coupled to a harmonic oscillator bath. Although one cannot carry out the quantum calculations of the response functions with full molecular dynamics (MD) simulations for a realistic system which consists of many molecules, it is possible to grasp the essence of the quantum effects on the vibrational spectra by employing a model Hamiltonian that describes an intra- or intermolecular vibrational motion in a condensed phase. The present model fully includes vibrational relaxation, while the stochastic model often used to simulate infrared spectra does not. We have employed the reduced quantum hierarchy equations of motion approach in the Wigner space representation to deal with nonperturbative, non-Markovian, and nonsecular system-bath interactions. Taking the classical limit of the hierarchy equations of motion, we have obtained the classical equations of motion that describe the classical dynamics under the same physical conditions as in the quantum case. By comparing the classical and quantum mechanically calculated linear and multidimensional spectra, we found that the profiles of spectra for a fast modulation case were similar, but different for a slow modulation case. In both the classical and quantum cases, we identified the resonant oscillation peak in the spectra, but the quantum peak shifted to the red compared with the classical one if the potential is anharmonic. The prominent quantum effect is the 1-2 transition peak, which appears only in the quantum mechanically calculated spectra as a result of anharmonicity in the potential or nonlinearity of the system-bath coupling. While the contribution of the 1-2 transition is negligible in the fast modulation case, it becomes important in the slow modulation case as long as the amplitude of the frequency fluctuation is small. Thus, we observed a distinct difference between the classical and quantum mechanically calculated multidimensional spectra in the slow modulation case where spectral diffusion plays a role. This fact indicates that one may not reproduce the experimentally obtained multidimensional spectrum for high-frequency vibrational modes based on classical molecular dynamics simulations if the modulation that arises from surrounding molecules is weak and slow. A practical way to overcome the difference between the classical and quantum simulations was discussed.

Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study.

PubMed

Smith, E M D; Jorgensen, A L; Beresford, M W

2017-10-01

Background Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus (JSLE) patients. The value of commonly available biomarkers, such as anti-dsDNA antibodies, complement (C3/C4), ESR and full blood count parameters in the identification of active LN remains uncertain. Methods Participants from the UK JSLE Cohort Study, aged <16 years at diagnosis, were categorized as having active or inactive LN according to the renal domain of the British Isles Lupus Assessment Group score. Classic biomarkers: anti-dsDNA, C3, C4, ESR, CRP, haemoglobin, total white cells, neutrophils, lymphocytes, platelets and immunoglobulins were assessed for their ability to identify active LN using binary logistic regression modeling, with stepAIC function applied to select a final model. Receiver-operating curve analysis was used to assess diagnostic accuracy. Results A total of 370 patients were recruited; 191 (52%) had active LN and 179 (48%) had inactive LN. Binary logistic regression modeling demonstrated a combination of ESR, C3, white cell count, neutrophils, lymphocytes and IgG to be best for the identification of active LN (area under the curve 0.724). Conclusions At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a 'fair' ability to identify active LN. Urine biomarkers were not included in these analyses. These results add to the concern that classic blood biomarkers are limited in monitoring discrete JSLE manifestations such as LN.

Geometric Theory of Reduction of Nonlinear Control Systems

NASA Astrophysics Data System (ADS)

Elkin, V. I.

2018-02-01

The foundations of a differential geometric theory of nonlinear control systems are described on the basis of categorical concepts (isomorphism, factorization, restrictions) by analogy with classical mathematical theories (of linear spaces, groups, etc.).

Time Domain Stability Margin Assessment Method

NASA Technical Reports Server (NTRS)

Clements, Keith

2017-01-01

The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation.

Quantum Discord for d⊗2 Systems

PubMed Central

Ma, Zhihao; Chen, Zhihua; Fanchini, Felipe Fernandes; Fei, Shao-Ming

2015-01-01

We present an analytical solution for classical correlation, defined in terms of linear entropy, in an arbitrary system when the second subsystem is measured. We show that the optimal measurements used in the maximization of the classical correlation in terms of linear entropy, when used to calculate the quantum discord in terms of von Neumann entropy, result in a tight upper bound for arbitrary systems. This bound agrees with all known analytical results about quantum discord in terms of von Neumann entropy and, when comparing it with the numerical results for 106 two-qubit random density matrices, we obtain an average deviation of order 10−4. Furthermore, our results give a way to calculate the quantum discord for arbitrary n-qubit GHZ and W states evolving under the action of the amplitude damping noisy channel. PMID:26036771

Time-Domain Stability Margin Assessment

NASA Technical Reports Server (NTRS)

Clements, Keith

2016-01-01

The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation.

On the stability and instantaneous velocity of grasped frictionless objects

NASA Technical Reports Server (NTRS)

Trinkle, Jeffrey C.

1992-01-01

A quantitative test for form closure valid for any number of contact points is formulated as a linear program, the optimal objective value of which provides a measure of how far a grasp is from losing form closure. Another contribution of the study is the formulation of a linear program whose solution yields the same information as the classical approach. The benefit of the formulation is that explicit testing of all possible combinations of contact interactions can be avoided by the algorithm used to solve the linear program.

Influence of an asymmetric ring on the modeling of an orthogonally stiffened cylindrical shell

NASA Technical Reports Server (NTRS)

Rastogi, Naveen; Johnson, Eric R.

1994-01-01

Structural models are examined for the influence of a ring with an asymmetrical cross section on the linear elastic response of an orthogonally stiffened cylindrical shell subjected to internal pressure. The first structural model employs classical theory for the shell and stiffeners. The second model employs transverse shear deformation theories for the shell and stringer and classical theory for the ring. Closed-end pressure vessel effects are included. Interacting line load intensities are computed in the stiffener-to-skin joints for an example problem having the dimensions of the fuselage of a large transport aircraft. Classical structural theory is found to exaggerate the asymmetric response compared to the transverse shear deformation theory.

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors

PubMed Central

Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014. PMID:28257437

Task-driven dictionary learning.

PubMed

Mairal, Julien; Bach, Francis; Ponce, Jean

2012-04-01

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

Ratio manipulating spectrophotometry versus chemometry as stability indicating methods for cefquinome sulfate determination

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Arafa, Reham M.; Abbas, Samah S.; Amer, Sawsan M.

2016-01-01

Spectral resolution of cefquinome sulfate (CFQ) in the presence of its degradation products was studied. Three selective, accurate and rapid spectrophotometric methods were performed for the determination of CFQ in the presence of either its hydrolytic, oxidative or photo-degradation products. The proposed ratio difference, derivative ratio and mean centering are ratio manipulating spectrophotometric methods that were satisfactorily applied for selective determination of CFQ within linear range of 5.0-40.0 μg mL- 1. Concentration Residuals Augmented Classical Least Squares was applied and evaluated for the determination of the cited drug in the presence of its all degradation products. Traditional Partial Least Squares regression was also applied and benchmarked against the proposed advanced multivariate calibration. Experimentally designed 25 synthetic mixtures of three factors at five levels were used to calibrate and validate the multivariate models. Advanced chemometrics succeeded in quantitative and qualitative analyses of CFQ along with its hydrolytic, oxidative and photo-degradation products. The proposed methods were applied successfully for different pharmaceutical formulations analyses. These developed methods were simple and cost-effective compared with the manufacturer's RP-HPLC method.

A new class of ensemble conserving algorithms for approximate quantum dynamics: Theoretical formulation and model problems.

PubMed

Smith, Kyle K G; Poulsen, Jens Aage; Nyman, Gunnar; Rossky, Peter J

2015-06-28

We develop two classes of quasi-classical dynamics that are shown to conserve the initial quantum ensemble when used in combination with the Feynman-Kleinert approximation of the density operator. These dynamics are used to improve the Feynman-Kleinert implementation of the classical Wigner approximation for the evaluation of quantum time correlation functions known as Feynman-Kleinert linearized path-integral. As shown, both classes of dynamics are able to recover the exact classical and high temperature limits of the quantum time correlation function, while a subset is able to recover the exact harmonic limit. A comparison of the approximate quantum time correlation functions obtained from both classes of dynamics is made with the exact results for the challenging model problems of the quartic and double-well potentials. It is found that these dynamics provide a great improvement over the classical Wigner approximation, in which purely classical dynamics are used. In a special case, our first method becomes identical to centroid molecular dynamics.

Multivariable control of the Space Shuttle Remote Manipulator System using linearization by state feedback. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gettman, Chang-Ching LO

1993-01-01

This thesis develops and demonstrates an approach to nonlinear control system design using linearization by state feedback. The design provides improved transient response behavior allowing faster maneuvering of payloads by the SRMS. Modeling uncertainty is accounted for by using a second feedback loop designed around the feedback linearized dynamics. A classical feedback loop is developed to provide the easy implementation required for the relatively small on board computers. Feedback linearization also allows the use of higher bandwidth model based compensation in the outer loop, since it helps maintain stability in the presence of the nonlinearities typically neglected in model based designs.

Linear canonical transformations of coherent and squeezed states in the Wigner phase space

NASA Technical Reports Server (NTRS)

Han, D.; Kim, Y. S.; Noz, Marilyn E.

1988-01-01

It is shown that classical linear canonical transformations are possible in the Wigner phase space. Coherent and squeezed states are shown to be linear canonical transforms of the ground-state harmonic oscillator. It is therefore possible to evaluate the Wigner functions for coherent and squeezed states from that for the harmonic oscillator. Since the group of linear canonical transformations has a subgroup whose algebraic property is the same as that of the (2+1)-dimensional Lorentz group, it may be possible to test certain properties of the Lorentz group using optical devices. A possible experiment to measure the Wigner rotation angle is discussed.

Anderson acceleration of the Jacobi iterative method: An efficient alternative to Krylov methods for large, sparse linear systems

DOE PAGES

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

2015-12-01

We employ Anderson extrapolation to accelerate the classical Jacobi iterative method for large, sparse linear systems. Specifically, we utilize extrapolation at periodic intervals within the Jacobi iteration to develop the Alternating Anderson–Jacobi (AAJ) method. We verify the accuracy and efficacy of AAJ in a range of test cases, including nonsymmetric systems of equations. We demonstrate that AAJ possesses a favorable scaling with system size that is accompanied by a small prefactor, even in the absence of a preconditioner. In particular, we show that AAJ is able to accelerate the classical Jacobi iteration by over four orders of magnitude, with speed-upsmore » that increase as the system gets larger. Moreover, we find that AAJ significantly outperforms the Generalized Minimal Residual (GMRES) method in the range of problems considered here, with the relative performance again improving with size of the system. As a result, the proposed method represents a simple yet efficient technique that is particularly attractive for large-scale parallel solutions of linear systems of equations.« less

Linear enamel hypoplasia at Xcambó, Yucatán, during the Maya Classic period: An evaluation of coastal marshland impact on ancient human populations.

PubMed

Méndez Collí, C; Sierra Sosa, T N; Tiesler, V; Cucina, A

2009-01-01

Non-specific stress markers such as linear enamel hypoplasia (LEH) have been associated in the literature with a large number of possible conditions disrupting the individual's homeostasis, though metabolic strain originating synergistically by disease and malnutrition has been held to be the main cause behind enamel disruption. The analysis of LEH in the Maya Classic period site of Xcambó, located along the northern coast of the Yucatán peninsula, reveals high exposure to stressful conditions during infancy regardless of age and sex. Yet, the inhabitants of the site were of a medium to high social and economic status, with access to balanced and protein-rich nutritional resources, which should have functioned as a cultural buffer to the impact of stress. In the light of this apparent contradiction, this paper discusses the impact of environmental conditions on the record of metabolic stress. Our conclusions pose a cautionary caveat for inferring nutrition and status in ancient pre-antibiotic populations solely from the occurrence of linear enamel hypoplasia.

Anderson acceleration of the Jacobi iterative method: An efficient alternative to Krylov methods for large, sparse linear systems

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

Pratapa, Phanisri P.; Suryanarayana, Phanish; Pask, John E.

We employ Anderson extrapolation to accelerate the classical Jacobi iterative method for large, sparse linear systems. Specifically, we utilize extrapolation at periodic intervals within the Jacobi iteration to develop the Alternating Anderson–Jacobi (AAJ) method. We verify the accuracy and efficacy of AAJ in a range of test cases, including nonsymmetric systems of equations. We demonstrate that AAJ possesses a favorable scaling with system size that is accompanied by a small prefactor, even in the absence of a preconditioner. In particular, we show that AAJ is able to accelerate the classical Jacobi iteration by over four orders of magnitude, with speed-upsmore » that increase as the system gets larger. Moreover, we find that AAJ significantly outperforms the Generalized Minimal Residual (GMRES) method in the range of problems considered here, with the relative performance again improving with size of the system. As a result, the proposed method represents a simple yet efficient technique that is particularly attractive for large-scale parallel solutions of linear systems of equations.« less

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

On vertical profile of ozone at Syowa

NASA Technical Reports Server (NTRS)

Chubachi, Shigeru

1994-01-01

The difference in the vertical ozone profile at Syowa between 1966-1981 and 1982-1988 is shown. The month-height cross section of the slope of the linear regressions between ozone partial pressure and 100-mb temperature is also shown. The vertically integrated values of the slopes are in close agreement with the slopes calculated by linear regression of Dobson total ozone on 100-mb temperature in the period of 1982-1988.

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.

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Entangled γ-photons—classical laboratory exercise with modern detectors

NASA Astrophysics Data System (ADS)

Hetfleiš, Jakub; Lněnička, Jindřich; Šlégr, Jan

2018-03-01

This paper describes the application of modern semiconductor detectors of γ and β radiation, which can be used in undergraduate laboratory experiments and lecture demonstrations as a replacement for Geiger-Müller (GM) tubes. Unlike GM tubes, semiconductor detectors do not require a high voltage power source or shaping circuits. The principle of operation of semiconductor detectors is discussed briefly, and classical experiments from nuclear physics are described, ranging from the measurements of linear and mass attenuation coefficient to a demonstration of entangled γ-photons.

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.

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.

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…

Transition of recollision trajectories from linear to elliptical polarization

DOE PAGES

Li, Yingbin; Yu, Benhai; Tang, Qingbin; ...

2016-03-15

Using a classical ensemble method, we revisit the topic of recollision and nonsequential double ionization with elliptically polarized laser fields. We focus on how the recollision mechanism transitions from short trajectories with linear polarization to long trajectories with elliptical polarization. Furthermore, we propose how this transition can be observed by measuring the carrier-envelop-phase dependence of the correlated electron momentum spectra using currently available few-cycle laser pulses.

Sequential design of discrete linear quadratic regulators via optimal root-locus techniques

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Yates, Robert E.; Ganesan, Sekar

1989-01-01

A sequential method employing classical root-locus techniques has been developed in order to determine the quadratic weighting matrices and discrete linear quadratic regulators of multivariable control systems. At each recursive step, an intermediate unity rank state-weighting matrix that contains some invariant eigenvectors of that open-loop matrix is assigned, and an intermediate characteristic equation of the closed-loop system containing the invariant eigenvalues is created.

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.

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.

Reversed-phase liquid chromatography column testing: robustness study of the test.

PubMed

Le Mapihan, K; Vial, J; Jardy, A

2004-12-24

Choosing the right RPLC column for an actual separation among the more than 600 commercially available ones still represents a real challenge for the analyst particularly when basic solutes are involved. Many tests dedicated to the characterization and the classification of stationary phases have been proposed in the literature and some of them highlighted the need of a better understanding of retention properties to lead to a rational choice of columns. However, unlike classical chromatographic methods, the problem of their robustness evaluation has often been left unaddressed. In the present study, we present a robustness study that was applied to the chromatographic testing procedure we had developed and optimized previously. A design of experiment (DoE) approach was implemented. Four factors, previously identified as potentially influent, were selected and subjected to small controlled variations: solvent fraction, temperature, pH and buffer concentration. As our model comprised quadratic terms instead of a simple linear model, we chose a D-optimal design in order to minimize the experiment number. As a previous batch-to-batch study [K. Le Mapihan, Caractérisation et classification des phases stationnaires utilisées pour l'analyse CPL de produits pharmaceutiques, Ph.D. Thesis, Pierre and Marie Curie University, 2004] had shown a low variability on the selected stationary phase, it was then possible to split the design into two parts, according to the solvent nature, each using one column. Actually, our testing procedure involving assays both with methanol and with acetonitrile as organic modifier, such an approach enabled to avoid a possible bias due to the column ageing considering the number of experiments required (16 + 6 center points). Experimental results were computed thanks to a Partial Least Squares regression procedure, more adapted than the classical regression to handle factors and responses not completely independent. The results showed the behavior of the solutes in relation to their physico-chemical properties and the relevance of the second term degree of our model. Finally, the robust domain of the test has been fairly identified, so that any potential user precisely knows to which extend each experimental parameter must be controlled when our testing procedure is to be implemented.

Calories and portion sizes in recipes throughout 100 years: an overlooked factor in the development of overweight and obesity?

PubMed

Eidner, Maj Bloch; Lund, Anne-Sofie Qvistgaard; Harboe, Bodil Schroll; Clemmensen, Inge Haunstrup

2013-12-01

Large portion sizes have been associated with large energy intake, which can contribute to the development of overweight and obesity. Portion sizes of non-home cooked food have increased in the past 20 years, however, less is known about portion sizes of home-cooked food. The aim of the study was to assess if the portion sizes measured in calories in Danish cookbook recipes have changed throughout the past 100 years. Portion size measured in calories was determined by content-analysis of 21 classic Danish recipes in 13 editions of the famous Danish cookbook "Food" from 1909 to 2009. Calorie content of the recipes was determined in standard nutritional software, and the changes in calories were examined by simple linear regression analyses. Mean portion size in calories increased significantly by 21% (β = 0.63; p < 0.01) over the past 100 years in the analyzed recipes. The mean portion size in calories from a composed homemade meal increased by 77% (β = 2.88; p < 0.01). The mean portion size in calories from meat increased by 27% (β = 0.85; p = 0.03), starchy products increased by 148% (β = 1.28; p < 0.01), vegetables increased by 37% (β = 0.21; p = 0.13) and sauce increased by 47% (β = 0.56; p = 0.02) throughout the years. Portion sizes measured in calories in classical Danish recipes have increased significantly in the past 100 years and can be an important factor in increased energy intake and the risk of developing overweight and obesity.

Are classic predictors of voltage valid in cardiac amyloidosis? A contemporary analysis of electrocardiographic findings.

PubMed

Sperry, Brett W; Vranian, Michael N; Hachamovitch, Rory; Joshi, Hariom; McCarthy, Meghann; Ikram, Asad; Hanna, Mazen

2016-07-01

Low voltage electrocardiography (ECG) coupled with increased ventricular wall thickness is the hallmark of cardiac amyloidosis. However, patient characteristics influencing voltage in the general population, including bundle branch block, have not been evaluated in amyloid heart disease. A retrospective analysis was performed of patients with newly diagnosed cardiac amyloidosis from 2002 to 2014. ECG voltage was calculated using limb (sum of QRS complex in leads I, II and III) and precordial (Sokolow: S in V1 plus R in V5-V6) criteria. The associations between voltage and clinical variables were tested using multivariable linear regression. A Cox model assessed the association of voltage with mortality. In 389 subjects (transthyretin ATTR 186, light chain AL 203), 30% had conduction delay (QRS >120ms). In those with narrow QRS, 68% met low limb, 72% low Sokolow and 57% both criteria, with lower voltages found in AL vs ATTR. LV mass index as well as other typical factors that impact voltage (age, sex, race, hypertension, BSA, and smoking) in the general population were not associated with voltage in this cardiac amyloidosis cohort. Patients with LBBB and IVCD had similar voltages when compared to those with narrow QRS. Voltage was significantly associated with mortality (p<0.001 for both criteria) after multivariable adjustment. Classic predictors of ECG voltage in the general population are not valid in cardiac amyloidosis. In this cohort, the prevalence estimates of ventricular conduction delay and low voltage are higher than previously reported. Voltage predicts mortality after multivariable adjustment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.

PubMed

Rutkowski, Tomasz M; Mori, Hiromu

2015-04-15

The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research. Copyright © 2014 Elsevier B.V. All rights reserved.

Quantum Max-flow/Min-cut

NASA Astrophysics Data System (ADS)

Cui, Shawn X.; Freedman, Michael H.; Sattath, Or; Stong, Richard; Minton, Greg

2016-06-01

The classical max-flow min-cut theorem describes transport through certain idealized classical networks. We consider the quantum analog for tensor networks. By associating an integral capacity to each edge and a tensor to each vertex in a flow network, we can also interpret it as a tensor network and, more specifically, as a linear map from the input space to the output space. The quantum max-flow is defined to be the maximal rank of this linear map over all choices of tensors. The quantum min-cut is defined to be the minimum product of the capacities of edges over all cuts of the tensor network. We show that unlike the classical case, the quantum max-flow=min-cut conjecture is not true in general. Under certain conditions, e.g., when the capacity on each edge is some power of a fixed integer, the quantum max-flow is proved to equal the quantum min-cut. However, concrete examples are also provided where the equality does not hold. We also found connections of quantum max-flow/min-cut with entropy of entanglement and the quantum satisfiability problem. We speculate that the phenomena revealed may be of interest both in spin systems in condensed matter and in quantum gravity.

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.

The Use of Alternative Regression Methods in Social Sciences and the Comparison of Least Squares and M Estimation Methods in Terms of the Determination of Coefficient

ERIC Educational Resources Information Center

Coskuntuncel, Orkun

2013-01-01

The purpose of this study is two-fold; the first aim being to show the effect of outliers on the widely used least squares regression estimator in social sciences. The second aim is to compare the classical method of least squares with the robust M-estimator using the "determination of coefficient" (R[superscript 2]). For this purpose,…

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

PubMed

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B M J

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship. Copyright © 2018. Published by Elsevier B.V.

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.

Revealing a quantum feature of dimensionless uncertainty in linear and quadratic potentials by changing potential intervals

NASA Astrophysics Data System (ADS)

Kheiri, R.

2016-09-01

As an undergraduate exercise, in an article (2012 Am. J. Phys. 80 780-14), quantum and classical uncertainties for dimensionless variables of position and momentum were evaluated in three potentials: infinite well, bouncing ball, and harmonic oscillator. While original quantum uncertainty products depend on {{\\hslash }} and the number of states (n), a dimensionless approach makes the comparison between quantum uncertainty and classical dispersion possible by excluding {{\\hslash }}. But the question is whether the uncertainty still remains dependent on quantum number n. In the above-mentioned article, there lies this contrast; on the one hand, the dimensionless quantum uncertainty of the potential box approaches classical dispersion only in the limit of large quantum numbers (n\\to ∞ )—consistent with the correspondence principle. On the other hand, similar evaluations for bouncing ball and harmonic oscillator potentials are equal to their classical counterparts independent of n. This equality may hide the quantum feature of low energy levels. In the current study, we change the potential intervals in order to make them symmetric for the linear potential and non-symmetric for the quadratic potential. As a result, it is shown in this paper that the dimensionless quantum uncertainty of these potentials in the new potential intervals is expressed in terms of quantum number n. In other words, the uncertainty requires the correspondence principle in order to approach the classical limit. Therefore, it can be concluded that the dimensionless analysis, as a useful pedagogical method, does not take away the quantum feature of the n-dependence of quantum uncertainty in general. Moreover, our numerical calculations include the higher powers of the position for the potentials.

Serum aminotransferase levels are associated with markers of hypoxia in patients with obstructive sleep apnea.

PubMed

Norman, Daniel; Bardwell, Wayne A; Arosemena, Farah; Nelesen, Richard; Mills, Paul J; Loredo, Jose S; Lavine, Joel E; Dimsdale, Joel E

2008-01-01

Nonalcoholic fatty liver disease (NAFLD) is a disorder that often presents with elevated serum aminotransferase levels. Although it has classically been linked with the metabolic syndrome, recent studies suggest NAFLD may also be associated with obstructive sleep apnea (OSA). This study evaluates the association between serum aminotransferase levels and factors connected with: either the metabolic syndrome (elevated body mass index [BMI], lipid profile, blood pressure, fasting glucose), or with OSA severity (apnea hypopnea index, lowest oxygen saturation level, oxygen desaturation index, percent of time below 90% saturation [%T<90]). Retrospective case series. 109 adult patients with OSA at a university hospital general clinical research center. Markers of hypoxia (lowest oxygen saturation level and %T<90), correlated significantly with aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels (Pearson's r = -0.31 to -0.38, P <0.003), while apnea hypopnea index, body mass index, blood pressure, fasting glucose, triglyceride, and cholesterol levels did not. Hierarchical linear regression was then done to determine the best predictors of aminotransferase levels. Markers of metabolic syndrome were entered as one block and markers of sleep apnea as another. Regression analyses explained 16.3% of the variance in AST and 18.9% of the variance in ALT, with %T<90 playing the largest role. In patients with obstructive sleep apnea, serum aminotransferase levels are better predicted by markers of oxygen desaturation than by factors traditionally associated with the metabolic syndrome.

Regularized linearization for quantum nonlinear optical cavities: application to degenerate optical parametric oscillators.

PubMed

Navarrete-Benlloch, Carlos; Roldán, Eugenio; Chang, Yue; Shi, Tao

2014-10-06

Nonlinear optical cavities are crucial both in classical and quantum optics; in particular, nowadays optical parametric oscillators are one of the most versatile and tunable sources of coherent light, as well as the sources of the highest quality quantum-correlated light in the continuous variable regime. Being nonlinear systems, they can be driven through critical points in which a solution ceases to exist in favour of a new one, and it is close to these points where quantum correlations are the strongest. The simplest description of such systems consists in writing the quantum fields as the classical part plus some quantum fluctuations, linearizing then the dynamical equations with respect to the latter; however, such an approach breaks down close to critical points, where it provides unphysical predictions such as infinite photon numbers. On the other hand, techniques going beyond the simple linear description become too complicated especially regarding the evaluation of two-time correlators, which are of major importance to compute observables outside the cavity. In this article we provide a regularized linear description of nonlinear cavities, that is, a linearization procedure yielding physical results, taking the degenerate optical parametric oscillator as the guiding example. The method, which we call self-consistent linearization, is shown to be equivalent to a general Gaussian ansatz for the state of the system, and we compare its predictions with those obtained with available exact (or quasi-exact) methods. Apart from its operational value, we believe that our work is valuable also from a fundamental point of view, especially in connection to the question of how far linearized or Gaussian theories can be pushed to describe nonlinear dissipative systems which have access to non-Gaussian states.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

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

Information transport in classical statistical systems

NASA Astrophysics Data System (ADS)

Wetterich, C.

2018-02-01

For "static memory materials" the bulk properties depend on boundary conditions. Such materials can be realized by classical statistical systems which admit no unique equilibrium state. We describe the propagation of information from the boundary to the bulk by classical wave functions. The dependence of wave functions on the location of hypersurfaces in the bulk is governed by a linear evolution equation that can be viewed as a generalized Schrödinger equation. Classical wave functions obey the superposition principle, with local probabilities realized as bilinears of wave functions. For static memory materials the evolution within a subsector is unitary, as characteristic for the time evolution in quantum mechanics. The space-dependence in static memory materials can be used as an analogue representation of the time evolution in quantum mechanics - such materials are "quantum simulators". For example, an asymmetric Ising model on a Euclidean two-dimensional lattice represents the time evolution of free relativistic fermions in two-dimensional Minkowski space.

General integrable n-level, many-mode Janes-Cummings-Dicke models and classical r-matrices with spectral parameters

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

Skrypnyk, T., E-mail: taras.skrypnyk@unimib.it, E-mail: tskrypnyk@imath.kiev.ua

Using the technique of classical r-matrices and quantum Lax operators, we construct the most general form of the quantum integrable “n-level, many-mode” spin-boson Jaynes-Cummings-Dicke-type hamiltonians describing an interaction of a molecule of N n-level atoms with many modes of electromagnetic field and containing, in general, additional non-linear interaction terms. We explicitly obtain the corresponding quantum Lax operators and spin-boson analogs of the generalized Gaudin hamiltonians and prove their quantum commutativity. We investigate symmetries of the obtained models that are associated with the geometric symmetries of the classical r-matrices and construct the corresponding algebra of quantum integrals. We consider in detailmore » three classes of non-skew-symmetric classical r-matrices with spectral parameters and explicitly obtain the corresponding quantum Lax operators and Jaynes-Cummings-Dicke-type hamiltonians depending on the considered r-matrix.« less

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%.

Semi-automatic assessment of skin capillary density: proof of principle and validation.

PubMed

Gronenschild, E H B M; Muris, D M J; Schram, M T; Karaca, U; Stehouwer, C D A; Houben, A J H M

2013-11-01

Skin capillary density and recruitment have been proven to be relevant measures of microvascular function. Unfortunately, the assessment of skin capillary density from movie files is very time-consuming, since this is done manually. This impedes the use of this technique in large-scale studies. We aimed to develop a (semi-) automated assessment of skin capillary density. CapiAna (Capillary Analysis) is a newly developed semi-automatic image analysis application. The technique involves four steps: 1) movement correction, 2) selection of the frame range and positioning of the region of interest (ROI), 3) automatic detection of capillaries, and 4) manual correction of detected capillaries. To gain insight into the performance of the technique, skin capillary density was measured in twenty participants (ten women; mean age 56.2 [42-72] years). To investigate the agreement between CapiAna and the classic manual counting procedure, we used weighted Deming regression and Bland-Altman analyses. In addition, intra- and inter-observer coefficients of variation (CVs), and differences in analysis time were assessed. We found a good agreement between CapiAna and the classic manual method, with a Pearson's correlation coefficient (r) of 0.95 (P<0.001) and a Deming regression coefficient of 1.01 (95%CI: 0.91; 1.10). In addition, we found no significant differences between the two methods, with an intercept of the Deming regression of 1.75 (-6.04; 9.54), while the Bland-Altman analysis showed a mean difference (bias) of 2.0 (-13.5; 18.4) capillaries/mm(2). The intra- and inter-observer CVs of CapiAna were 2.5% and 5.6% respectively, while for the classic manual counting procedure these were 3.2% and 7.2%, respectively. Finally, the analysis time for CapiAna ranged between 25 and 35min versus 80 and 95min for the manual counting procedure. We have developed a semi-automatic image analysis application (CapiAna) for the assessment of skin capillary density, which agrees well with the classic manual counting procedure, is time-saving, and has a better reproducibility as compared to the classic manual counting procedure. As a result, the use of skin capillaroscopy is feasible in large-scale studies, which importantly extends the possibilities to perform microcirculation research in humans. © 2013.

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.