Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

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

Mayer, B. P.; Valdez, C. A.; DeHope, A. J.

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduledmore » precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.« less

Peer-driven contraceptive choices and preferences for contraceptive methods among students of tertiary educational institutions in Enugu, Nigeria.

PubMed

Iyoke, Ca; Ezugwu, Fo; Lawani, Ol; Ugwu, Go; Ajah, Lo; Mba, Sg

2014-01-01

To describe the methods preferred for contraception, evaluate preferences and adherence to modern contraceptive methods, and determine the factors associated with contraceptive choices among tertiary students in South East Nigeria. A questionnaire-based cross-sectional study of sexual habits, knowledge of contraceptive methods, and patterns of contraceptive choices among a pooled sample of unmarried students from the three largest tertiary educational institutions in Enugu city, Nigeria was done. Statistical analysis involved descriptive and inferential statistics at the 95% level of confidence. A total of 313 unmarried students were studied (194 males; 119 females). Their mean age was 22.5±5.1 years. Over 98% of males and 85% of females made their contraceptive choices based on information from peers. Preferences for contraceptive methods among female students were 49.2% for traditional methods of contraception, 28% for modern methods, 10% for nonpharmacological agents, and 8% for off-label drugs. Adherence to modern contraceptives among female students was 35%. Among male students, the preference for the male condom was 45.2% and the adherence to condom use was 21.7%. Multivariate analysis showed that receiving information from health personnel/media/workshops (odds ratio 9.54, 95% confidence interval 3.5-26.3), health science-related course of study (odds ratio 3.5, 95% confidence interval 1.3-9.6), and previous sexual exposure prior to university admission (odds ratio 3.48, 95% confidence interval 1.5-8.0) all increased the likelihood of adherence to modern contraceptive methods. An overwhelming reliance on peers for contraceptive information in the context of poor knowledge of modern methods of contraception among young people could have contributed to the low preferences and adherence to modern contraceptive methods among students in tertiary educational institutions. Programs to reduce risky sexual behavior among these students may need to focus on increasing the content and adequacy of contraceptive information held by people through regular health worker-led, on-campus workshops.

Multivariate methods to visualise colour-space and colour discrimination data.

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

Despite most modern colour spaces treating colour as three-dimensional (3-D), colour data is usually not visualised in 3-D (and two-dimensional (2-D) projection-plane segments and multiple 2-D perspective views are used instead). The objectives of this article are firstly, to introduce a truly 3-D percept of colour space using stereo-pairs, secondly to view colour discrimination data using that platform, and thirdly to apply formal statistics and multivariate methods to analyse the data in 3-D. This is the first demonstration of the software that generated stereo-pairs of RGB colour space, as well as of a new computerised procedure that investigated colour discrimination by measuring colour just noticeable differences (JND). An initial pilot study and thorough investigation of instrument repeatability were performed. Thereafter, to demonstrate the capabilities of the software, five colour-normal and one colour-deficient subject were examined using the JND procedure and multivariate methods of data analysis. Scatter plots of responses were meaningfully examined in 3-D and were useful in evaluating multivariate normality as well as identifying outliers. The extent and direction of the difference between each JND response and the stimulus colour point was calculated and appreciated in 3-D. Ellipsoidal surfaces of constant probability density (distribution ellipsoids) were fitted to response data; the volumes of these ellipsoids appeared useful in differentiating the colour-deficient subject from the colour-normals. Hypothesis tests of variances and covariances showed many statistically significant differences between the results of the colour-deficient subject and those of the colour-normals, while far fewer differences were found when comparing within colour-normals. The 3-D visualisation of colour data using stereo-pairs, as well as the statistics and multivariate methods of analysis employed, were found to be unique and useful tools in the representation and study of colour. Many additional studies using these methods along with the JND and other procedures have been identified and will be reported in future publications. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Family Life and Developmental Idealism in Yazd, Iran

PubMed Central

Abbasi-Shavazi, Mohammad Jalal; Askari-Nodoushan, Abbas

2012-01-01

BACKGROUND This paper is motivated by the theory that developmental idealism has been disseminated globally and has become an international force for family and demographic change. Developmental idealism is a set of cultural beliefs and values about development and how development relates to family and demographic behavior. It holds that modern societies are causal forces producing modern families, that modern families help to produce modern societies, and that modern family change is to be expected. OBJECTIVE We examine the extent to which developmental idealism has been disseminated in Iran. We also investigate predictors of the dissemination of developmental idealism. METHODS We use survey data collected in 2007 from a sample of women in Yazd, a city in Iran. We examine the distribution of developmental idealism in the sample and the multivariate predictors of developmental idealism. RESULTS We find considerable support for the expectation that many elements of developmental idealism have been widely disseminated. Statistically significant majorities associate development with particular family attributes, believe that development causes change in families, believe that fertility reductions and age-at-marriage increases help foster development, and perceive family trends in Iran headed toward modernity. As predicted, parental education, respondent education, and income affect adherence to developmental idealism. CONCLUSIONS Developmental idealism has been widely disseminated in Yazd, Iran and is related to social and demographic factors in predicted ways. COMMENTS Although our data come from only one city, we expect that developmental idealism has been widely distributed in Iran, with important implications for family and demographic behavior. PMID:22942772

An introduction to real-time graphical techniques for analyzing multivariate data

NASA Astrophysics Data System (ADS)

Friedman, Jerome H.; McDonald, John Alan; Stuetzle, Werner

1987-08-01

Orion I is a graphics system used to study applications of computer graphics - especially interactive motion graphics - in statistics. Orion I is the newest of a family of "Prim" systems, whose most striking common feature is the use of real-time motion graphics to display three dimensional scatterplots. Orion I differs from earlier Prim systems through the use of modern and relatively inexpensive raster graphics and microprocessor technology. It also delivers more computing power to its user; Orion I can perform more sophisticated real-time computations than were possible on previous such systems. We demonstrate some of Orion I's capabilities in our film: "Exploring data with Orion I".

Predictors of modern contraceptive methods use among married women of reproductive age groups in Western Ethiopia: a community based cross-sectional study.

PubMed

Tekelab, Tesfalidet; Melka, Alemu Sufa; Wirtu, Desalegn

2015-07-17

In Ethiopia, the prevalence of modern contraceptive use is very low (27 %) and the percentage of those with unmet needs for family planning is 25 %. The current study identified factors associated with the utilization of modern contraceptive methods among married women in Western Ethiopia. A community based, cross-sectional study was employed from April 10 to April 25, 2014, among married women of reproductive age in Nekemte Town. A multi-stage sampling procedure was used to select 1003 study participants. A pretested structured questionnaire was used to collect data, and data collectors who had completed high school were involved in the data collection process. A bivariate, multivariable logistic regression model was fit, and statistical significance was determined with a 95% confidence level. The overall utilization rate of modern contraceptives in this study was 71.9%. The most common form of modern contraceptives used was injectable (60.3%). Age (AOR = 2.00, 95 % CI = 1.35-2.98), women's educational level (AOR = 2.50, 95 % CI = 1.62-3.84), monthly income (AOR = 2.26, 95 % CI = 1.24-4.10), respondent's fertility (AOR = 2.60, 95 % CI = 1.48-4.56), fertility-related decision (AOR = 3.70, 95 % CI = 2.45-5.58), and having radio (AOR = 1.93, 95 % CI = 1.37-2.71) showed significant positive associations with the utilization of modern contraceptive methods. The findings showed that women's empowerment, fertility-related discussions among couples, and the availability of the media were important factors that influenced the use of modern contraceptives. Thus, policymakers and implementers should work on those factors to increase the utilization of modern contraceptive methods.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

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

Mayer, B. P.; Mew, D. A.; DeHope, A.

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganicmore » species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.« less

Longitudinal flying qualities criteria for single-pilot instrument flight operations

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Bar-Gill, A.

1983-01-01

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Multivariate analysis techniques

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

Bendavid, Josh; Fisher, Wade C.; Junk, Thomas R.

2016-01-01

The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually bothmore » be improved by separating signal events from background events with higher efficiency and purity.« less

Postpartum modern contraceptive use in northern Ethiopia: prevalence and associated factors

PubMed Central

Teferra, Alemayehu Shimeka; Gelagay, Abebaw Addis

2017-01-01

OBJECTIVES The postpartum period is a critical period for addressing widespread unmet needs in family planning and for reducing the risks of closely spaced pregnancies. However, contraception during the extended postpartum period has been underemphasized in Ethiopia. Therefore, this study aimed to assess postpartum modern contraceptive use among women in northern Ethiopia and to identify factors associated with modern contraceptive use in the postpartum period. METHODS A community based cross-sectional study was conducted from March to April, 2015. Data were entered using Epi Info version 7 and then exported into Stata version 12 for analysis. Bivariate and multivariate logistic regression models were fitted to identify the determinants of postpartum modern contraceptive use. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were calculated, and p-values <0.05 were considered to indicate statistical significance. RESULTS Nearly half (48.0%) of women used modern contraceptives during the extended postpartum period. Postpartum modern contraceptive use was significantly associated with secondary and tertiary education levels (aOR, 4.25; 95% CI, 1.29 to 14.00; aOR, 5.36 ; 95% CI, 1.14 to 25.45, respectively), family planning counseling during prenatal and postnatal care (aOR, 5.72 ; 95% CI, 2.67, 12.28), having postnatal care (aOR, 2.36; 95% CI, 1.15 to 4.87), resuming sexual activity (aOR, 9.53; 95% CI, 3.74 to 24.27), and menses returning after birth (aOR, 6.35; 95% CI, 3.14 to 13.39). In addition, experiencing problems with previous contraceptive use was negatively associated with modern contraceptive use (aOR, 0.34; 95% CI, 0.16 to 0.72). CONCLUSIONS Low rate of postpartum modern contraceptive use were found in the study area. Therefore, strengthening family planning counseling during antenatal and postnatal care visits, improving utilization of postnatal care services and improving women’s educational status are crucial steps for to enhance modern contraceptive use among postpartum women. PMID:28330336

Impact of Different Surgeons on Dental Implant Failure.

PubMed

Chrcanovic, Bruno Ramos; Kisch, Jenö; Albrektsson, Tomas; Wennerberg, Ann

To assess the influence of several factors on the prevalence of dental implant failure, with special consideration of the placement of implants by different dental surgeons. This retrospective study is based on 2,670 patients who received 10,096 implants at one specialist clinic. Only the data of patients and implants treated by surgeons who had inserted a minimum of 200 implants at the clinic were included. Kaplan-Meier curves were stratified with respect to the individual surgeon. A generalized estimating equation (GEE) method was used to account for the fact that repeated observations (several implants) were placed in a single patient. The factors bone quantity, bone quality, implant location, implant surface, and implant system were analyzed with descriptive statistics separately for each individual surgeon. A total of 10 surgeons were eligible. The differences between the survival curves of each individual were statistically significant. The multivariate GEE model showed the following variables to be statistically significant: surgeon, bruxism, intake of antidepressants, location, implant length, and implant system. The surgeon with the highest absolute number of failures was also the one who inserted the most implants in sites of poor bone and used turned implants in most cases, whereas the surgeon with the lowest absolute number of failures used mainly modern implants. Separate survival analyses of turned and modern implants stratified for the individual surgeon showed statistically significant differences in cumulative survival. Different levels of failure incidence could be observed between the surgeons, occasionally reaching significant levels. Although a direct causal relationship could not be ascertained, the results of the present study suggest that the surgeons' technique, skills, and/or judgment may negatively influence implant survival rates.

The effect of single and repeated UVB radiation on rabbit cornea.

PubMed

Fris, Miroslav; Tessem, May-Britt; Cejková, Jitka; Midelfart, Anna

2006-12-01

Cumulative effect of ultraviolet radiation (UVR) is an important aspect of UV corneal damage. The purpose of this study was to apply high resolution magic angle spinning proton nuclear magnetic resonance (HR-MAS 1H NMR) spectroscopy to evaluate the effect of single and repeated UV radiation exposure of the same overall dose on the rabbit cornea. Corneal surfaces of 24 normal rabbit eyes were examined for the effects of UVB exposure (312 nm). In the first group (UVB1), animals were irradiated with a single dose (3.12 J/cm2; 21 min) of UVB radiation. The animals in the second group (UVB2) were irradiated three times for 7 min every other day (dose of 1.04 J/cm2; days 1, 3, 5) to give the same overall dose (3.12 J/cm2). The third group served as an untreated control group. One day after the last irradiation, the animals were sacrificed, and the corneas were removed and frozen. HR-MAS 1H NMR spectra from intact corneas were obtained. Special grouping patterns among the tissue samples and the relative percentage changes in particular metabolite concentrations were evaluated using modern statistical methods (multivariate analysis, one-way ANOVA). The metabolic profile of both groups of UVB-irradiated samples was significantly different from the control corneas. Substantial decreases in taurine, hypo-taurine and choline-derivatives concentrations and substantial elevation in glucose and betaine levels were observed following the UVR exposure. There was no significant difference between the effect of a single and repeated UVB irradiation of the same overall dose. For the first time, the effects of single and repeated UVR doses on the metabolic profile of the rabbit cornea were analysed and compared. The combination of HR-MAS 1H NMR spectroscopy and modern statistical methods (multivariate analysis, one-way ANOVA) proved suitable to assess the overall view of the metabolic alterations in the rabbit corneal tissue following UVB radiation exposure.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Health services utilization during terminal illness in Addis Ababa, Ethiopia

PubMed Central

Reniers, Georges; Tesfai, Rebbeca

2009-01-01

Objectives We describe modern and alternative health services use in terminal illness of adults, and assess whether utilization patterns of TB/AIDS patients are distinct from those of patients suffering from other illnesses. Methods Data are from post-mortem interviews with close relatives or caretakers of the deceased. We provide descriptive statistics of health care utilization in adults and discuss their covariates in multivariate analyses. Results Over 85% of terminally sick patients visited a modern medical facility, but less than 40% spent more than 24 hours in a medical facility and only 25% died in one. Traditional healer (11%) and holy water (46%) visits offer a common treatment and healing alternative, but these visits do not co-vary in any consistent manner with the utilization of modern medical services. In terms of the cause of death, we find a higher contact rate with both modern and alternative medical service providers among TB/AIDS patients compared with those suffering from other medical conditions. The duration of illness seems to account for a good share of that variability. Other covariates of health services utilization are socio-economic status, education and age. Conclusions The contact rate of adults with modern medical facilities in terminal illness is almost universal, but their usage intensity is rather low. Alternative curative options are less commonly used, and do not exclude modern health services use. This suggests that both types of services are considered complements rather than alternatives for each other. Because the contact rate with health service providers is greatest for TB/AIDS patients, it is unlikely that HIV/AIDS-related stigma is an impediment to seeking care. We cannot exclude, however, that it delays health-seeking behaviour. PMID:19372240

Health services utilization during terminal illness in Addis Ababa, Ethiopia.

PubMed

Reniers, Georges; Tesfai, Rebbeca

2009-07-01

OBJECTIVES We describe modern and alternative health services use in terminal illness of adults, and assess whether utilization patterns of TB/AIDS patients are distinct from those of patients suffering from other illnesses. METHODS Data are from post-mortem interviews with close relatives or caretakers of the deceased. We provide descriptive statistics of health care utilization in adults and discuss their covariates in multivariate analyses. RESULTS Over 85% of terminally sick patients visited a modern medical facility, but less than 40% spent more than 24 hours in a medical facility and only 25% died in one. Traditional healer (11%) and holy water (46%) visits offer a common treatment and healing alternative, but these visits do not co-vary in any consistent manner with the utilization of modern medical services. In terms of the cause of death, we find a higher contact rate with both modern and alternative medical service providers among TB/AIDS patients compared with those suffering from other medical conditions. The duration of illness seems to account for a good share of that variability. Other covariates of health services utilization are socio-economic status, education and age. CONCLUSIONS The contact rate of adults with modern medical facilities in terminal illness is almost universal, but their usage intensity is rather low. Alternative curative options are less commonly used, and do not exclude modern health services use. This suggests that both types of services are considered complements rather than alternatives for each other. Because the contact rate with health service providers is greatest for TB/AIDS patients, it is unlikely that HIV/AIDS-related stigma is an impediment to seeking care. We cannot exclude, however, that it delays health-seeking behaviour.

Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data

DOE PAGES

Mayer, Brian P.; DeHope, Alan J.; Mew, Daniel A.; ...

2016-03-24

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. Here, the results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinctmore » compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography–tandem mass spectrometry-time of-flight (LC–MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. Finally, this work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.« less

Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data

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

Mayer, Brian P.; DeHope, Alan J.; Mew, Daniel A.

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. Here, the results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinctmore » compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography–tandem mass spectrometry-time of-flight (LC–MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. Finally, this work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.« less

Trends of Modern Contraceptive Use among Young Married Women Based on the 2000, 2005, and 2011 Ethiopian Demographic and Health Surveys: A Multivariate Decomposition Analysis

PubMed Central

Worku, Abebaw Gebeyehu; Tessema, Gizachew Assefa; Zeleke, Atinkut Alamirrew

2015-01-01

Introduction Accessing family planning can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive methods is low but it is increasing. This study aimed to analyze the trends and determinants of changes in modern contraceptive use over time among young married women in Ethiopia. Methods The study used data from the three Demographic Health Surveys conducted in Ethiopia, in 2000, 2005, and 2011. Young married women age 15–24 years with sample sizes of 2,157 in 2000, 1,904 in 2005, and 2,146 in 2011 were included. Logit-based decomposition analysis technique was used for analysis of factors contributing to the recent changes. STATA 12 was employed for data management and analyses. All calculations presented in this paper were weighted for the sampling probabilities and non-response. Complex sampling procedures were also considered during testing of statistical significance. Results Among young married women, modern contraceptive prevalence increased from 6% in 2000 to 16% in 2005 and to 36% in 2011. The decomposition analysis indicated that 34% of the overall change in modern contraceptive use was due to difference in women’s characteristics. Changes in the composition of young women’s characteristics according to age, educational status, religion, couple concordance on family size, and fertility preference were the major sources of this increase. Two-thirds of the increase in modern contraceptive use was due to difference in coefficients. Most importantly, the increase was due to change in contraceptive use behavior among the rural population (33%) and among Orthodox Christians (16%) and Protestants (4%). Conclusions Modern contraceptive use among young married women has showed a remarkable increase over the last decade in Ethiopia. Programmatic interventions targeting poor, younger (adolescent), illiterate, and Muslim women would help to maintain the increasing trend in modern contraceptive use. PMID:25635389

Advances in analytical chemistry

NASA Technical Reports Server (NTRS)

Arendale, W. F.; Congo, Richard T.; Nielsen, Bruce J.

1991-01-01

Implementation of computer programs based on multivariate statistical algorithms makes possible obtaining reliable information from long data vectors that contain large amounts of extraneous information, for example, noise and/or analytes that we do not wish to control. Three examples are described. Each of these applications requires the use of techniques characteristic of modern analytical chemistry. The first example, using a quantitative or analytical model, describes the determination of the acid dissociation constant for 2,2'-pyridyl thiophene using archived data. The second example describes an investigation to determine the active biocidal species of iodine in aqueous solutions. The third example is taken from a research program directed toward advanced fiber-optic chemical sensors. The second and third examples require heuristic or empirical models.

Large Metal Heads and Vitamin E Polyethylene Increase Frictional Torque in Total Hip Arthroplasty.

PubMed

Meneghini, R Michael; Lovro, Luke R; Wallace, Joseph M; Ziemba-Davis, Mary

2016-03-01

Trunnionosis has reemerged in modern total hip arthroplasty for reasons that remain unclear. Bearing frictional torque transmits forces to the modular head-neck interface, which may contribute to taper corrosion. The purpose of this study is to compare frictional torque of modern bearing couples in total hip arthroplasty. Mechanical testing based on in vivo loading conditions was used to measure frictional torque. All bearing couples were lubricated and tested at 1 Hz for more than 2000 cycles. The bearing couples tested included conventional, highly crosslinked (XLPE) and vitamin E polyethylene, CoCr, and ceramic femoral heads and dual-mobility bearings. Statistical analysis was performed using Student t test for single-variable and analysis of variance for multivariant analysis. P ≤ .05 was considered statistically significant. Large CoCr metal heads (≥36 mm) substantially increased frictional torque against XLPE liners (P = .01), a finding not observed in ceramic heads. Vitamin E polyethylene substantially increased frictional torque compared with XLPE in CoCr and ceramic heads (P = .001), whereas a difference between conventional and XLPE was not observed (P = .69) with the numbers available. Dual-mobility bearing with ceramic inner head demonstrated the lowest mean frictional torque of all bearing couples. In this simulated in vivo model, large-diameter CoCr femoral heads and vitamin E polyethylene liners are associated with increased frictional torque compared with smaller metal heads and XLPE, respectively. The increased frictional torque of vitamin E polyethylene and larger-diameter femoral heads should be considered and further studied, along with reported benefits of these modern bearing couples. Copyright © 2016 Elsevier Inc. All rights reserved.

Shape Ontogeny of the Distal Femur in the Hominidae with Implications for the Evolution of Bipedality

PubMed Central

Tallman, Melissa

2016-01-01

Heterochrony has been invoked to explain differences in the morphology of modern humans as compared to other great apes. The distal femur is one area where heterochrony has been hypothesized to explain morphological differentiation among Plio-Pleistocene hominins. This hypothesis is evaluated here using geometric morphometric data to describe the ontogenetic shape trajectories of extant hominine distal femora and place Plio-Pleistocene hominins within that context. Results of multivariate statistical analyses showed that in both Homo and Gorilla, the shape of the distal femur changes significantly over the course of development, whereas that of Pan changes very little. Development of the distal femur of Homo is characterized by an elongation of the condyles, and a greater degree of enlargement of the medial condyle relative to the lateral condyle, whereas Gorilla are characterized by a greater degree of enlargement of the lateral condyle, relative to the medial. Early Homo and Australopithecus africanus fossils fell on the modern human ontogenetic shape trajectory and were most similar to either adult or adolescent modern humans while specimens of Australopithecus afarensis were more similar to Gorilla/Pan. These results indicate that shape differences among the distal femora of Plio-Pleistocene hominins and humans cannot be accounted for by heterochrony alone; heterochrony could explain a transition from the distal femoral shape of early Homo/A. africanus to modern Homo, but not a transition from A. afarensis to Homo. That change could be the result of genetic or epigenetic factors. PMID:26886416

Anima: Modular Workflow System for Comprehensive Image Data Analysis

PubMed Central

Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

2014-01-01

Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

Level of modern health care seeking behaviors among mothers having under five children in Dangila town, north West Ethiopia, 2016: a cross sectional study.

PubMed

Dagnew, Amare Belachew; Tewabe, Tilahun; Murugan, Rajalakshmi

2018-05-29

Health seeking behavior is an action taken by an individual who perceive to have a health problem. In most developing countries including Ethiopia the health of the children is strongly dependant on maternal health care behavior. Most childhood morbidities and mortalities are associated with low level of mothers health care seeking behavior. Therefore, the objective of this study was to assess level of modern health care seeking behavior among mothers having under five children in Dangila town, North West Ethiopia. Community based quantitative cross-sectional study was conducted from April 15 to May 15, 2016. Systematic random sampling technique was used to select study participants. A total of273 mothers with children less than five years were included in this study. The data was collected from all five Kebeles using interviewer administered questionnaire. Descriptive and inferential statistics were used to present the data. Both bivariate and multivariate logistic regression analyses were used to identify factors associated with level of modern health care seeking behavior. Prevalence of modern health care seeking behavior was 82.1%. Age of mothers (AOR = 2.4(1.1, 5.3), age of the child (AOR = 6.7(2.8, 22.2), severity of illness (AOR = 5.2(1.2, 22.6) and family number (AOR = 6.4(2.1, 20.2) were predictors of modern health care seeking behavior among mothers. Majority of the mothers preferred to take their children to modern health care when they got illness. Age of children, age of mother, number of family and severity of illness were the determinant factors for modern health care seeking behavior. Therefore, health care services should be strengthened at community level through community integrated management of childhood illness, information, education communication / behavioral change communication strategies to improve mothers health care seeking behaviors.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Quality of reporting of modern randomized controlled trials in medical oncology: a systematic review.

PubMed

Péron, Julien; Pond, Gregory R; Gan, Hui K; Chen, Eric X; Almufti, Roula; Maillet, Denis; You, Benoit

2012-07-03

The Consolidated Standards of Reporting Trials (CONSORT) guidelines were developed in the mid-1990s for the explicit purpose of improving clinical trial reporting. However, there is little information regarding the adherence to CONSORT guidelines of recent publications of randomized controlled trials (RCTs) in oncology. All phase III RCTs published between 2005 and 2009 were reviewed using an 18-point overall quality score for reporting based on the 2001 CONSORT statement. Multivariable linear regression was used to identify features associated with improved reporting quality. To provide baseline data for future evaluations of reporting quality, RCTs were also assessed according to the 2010 revised CONSORT statement. All statistical tests were two-sided. A total of 357 RCTs were reviewed. The mean 2001 overall quality score was 13.4 on a scale of 0-18, whereas the mean 2010 overall quality score was 19.3 on a scale of 0-27. The overall RCT reporting quality score improved by 0.21 points per year from 2005 to 2009. Poorly reported items included method used to generate the random allocation (adequately reported in 29% of trials), whether and how blinding was applied (41%), method of allocation concealment (51%), and participant flow (59%). High impact factor (IF, P = .003), recent publication date (P = .008), and geographic origin of RCTs (P = .003) were independent factors statistically significantly associated with higher reporting quality in a multivariable regression model. Sample size, tumor type, and positivity of trial results were not associated with higher reporting quality, whereas funding source and treatment type had a borderline statistically significant impact. The results show that numerous items remained unreported for many trials. Thus, given the potential impact of poorly reported trials, oncology journals should require even stricter adherence to the CONSORT guidelines.

Forensic analysis of dyed textile fibers.

PubMed

Goodpaster, John V; Liszewski, Elisa A

2009-08-01

Textile fibers are a key form of trace evidence, and the ability to reliably associate or discriminate them is crucial for forensic scientists worldwide. While microscopic and instrumental analysis can be used to determine the composition of the fiber itself, additional specificity is gained by examining fiber color. This is particularly important when the bulk composition of the fiber is relatively uninformative, as it is with cotton, wool, or other natural fibers. Such analyses pose several problems, including extremely small sample sizes, the desire for nondestructive techniques, and the vast complexity of modern dye compositions. This review will focus on more recent methods for comparing fiber color by using chromatography, spectroscopy, and mass spectrometry. The increasing use of multivariate statistics and other data analysis techniques for the differentiation of spectra from dyed fibers will also be discussed.

Statistical Maps of Ground Magnetic Disturbance Derived from Global Geospace Models

NASA Astrophysics Data System (ADS)

Rigler, E. J.; Wiltberger, M. J.; Love, J. J.

2017-12-01

Electric currents in space are the principal driver of magnetic variations measured at Earth's surface. These in turn induce geoelectric fields that present a natural hazard for technological systems like high-voltage power distribution networks. Modern global geospace models can reasonably simulate large-scale geomagnetic response to solar wind variations, but they are less successful at deterministic predictions of intense localized geomagnetic activity that most impacts technological systems on the ground. Still, recent studies have shown that these models can accurately reproduce the spatial statistical distributions of geomagnetic activity, suggesting that their physics are largely correct. Since the magnetosphere is a largely externally driven system, most model-measurement discrepancies probably arise from uncertain boundary conditions. So, with realistic distributions of solar wind parameters to establish its boundary conditions, we use the Lyon-Fedder-Mobarry (LFM) geospace model to build a synthetic multivariate statistical model of gridded ground magnetic disturbance. From this, we analyze the spatial modes of geomagnetic response, regress on available measurements to fill in unsampled locations on the grid, and estimate the global probability distribution of extreme magnetic disturbance. The latter offers a prototype geomagnetic "hazard map", similar to those used to characterize better-known geophysical hazards like earthquakes and floods.

Statistics without Tears: Complex Statistics with Simple Arithmetic

ERIC Educational Resources Information Center

Smith, Brian

2011-01-01

One of the often overlooked aspects of modern statistics is the analysis of time series data. Modern introductory statistics courses tend to rush to probabilistic applications involving risk and confidence. Rarely does the first level course linger on such useful and fascinating topics as time series decomposition, with its practical applications…

Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

ERIC Educational Resources Information Center

Thompson, Bruce

Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

Psychological well-being and social support in chronic myeloid leukemia patients receiving lifelong targeted therapies.

PubMed

Efficace, Fabio; Breccia, Massimo; Cottone, Francesco; Okumura, Iris; Doro, Maribel; Riccardi, Francesca; Rosti, Gianantonio; Baccarani, Michele

2016-12-01

The main objective of this study was to investigate whether social support is independently associated with psychological well-being in chronic myeloid leukemia (CML) patients. Secondary objectives were to compare the psychological well-being profile of CML patients with that of their peers in general population and to examine possible age- and sex-related differences. Analysis was performed on 417 patients in treatment with lifelong molecularly targeted therapies. Mean age of patients analyzed was 56 years (range 19-87 years) and 247 (59 %) were male and 170 (41 %) were female. Social support was assessed with the Multidimensional Scale of Perceived Social Support and psychological well-being was evaluated with the short version of the Psychological General Well-Being Index. Descriptive statistics and multivariate logistic regression analyses were used. Multivariate logistic regression analysis revealed that a greater social support was independently associated with lower anxiety and depression, as well as with higher positive well-being, self-control, and vitality (p < 0.001). Female patients reported statistically significant worse outcomes in all dimensions of psychological well-being. Age- and sex-adjusted comparisons with population norms revealed that depression (ES = -0.42, p < 0.001) and self-control (ES = -0.48, p < 0.001) were the two main impaired psychological dimensions. This study indicates that social support is a critical factor associated with psychological well-being of CML patients treated with modern lifelong targeted therapies.

Analyzing Faculty Salaries When Statistics Fail.

ERIC Educational Resources Information Center

Simpson, William A.

The role played by nonstatistical procedures, in contrast to multivariant statistical approaches, in analyzing faculty salaries is discussed. Multivariant statistical methods are usually used to establish or defend against prima facia cases of gender and ethnic discrimination with respect to faculty salaries. These techniques are not applicable,…

The rise of Clostridium difficile infection in lung transplant recipients in the modern era.

PubMed

Lee, Janet T; Hertz, Marshall I; Dunitz, Jordan M; Kelly, Rosemary F; D'Cunha, Jonathan; Whitson, Bryan A; Shumway, Sara J

2013-01-01

Clostridium difficile infection (CDI) rates have been rising in recent years. We aimed to characterize CDI in lung transplant recipients in the modern era and hypothesized that CDI would increase the mortality risk. We performed a retrospective chart review of patients undergoing transplantation at our center from 1/2006 to 7/2011. Attributes of CDI+ and CDI- groups were compared using Student's t- and chi-square tests (α = 0.05). Multivariate Cox proportional hazard models were used to control for confounding factors. Overall CDI incidence was 22.5%. Seven of 151 patients (4.6%) developed CDI during the initial hospitalization after transplantation (mean time 10.6 ± 6 d) while 27 patients (19.7%) developed CDI after discharge (mean time 467 ± 471 d). Incidence rate was 224.6 cases/100 000 patient-days compared to 110 cases/100 000 patient-days (rate for entire hospital). CDI was not predictive of mortality (HR 2.06, 95% CI 0.94-4.52). CDI rates in lung transplant recipients are high in the modern era. No risk factors for CDI were identified. Although not statistically significant, CDI+ patients had a higher risk of death. The economic burden of CDI and trend toward worse outcomes for CDI patients have important implications for post-operative surveillance of CDI-related complications and need for CDI prophylaxis. © 2013 John Wiley & Sons A/S.

Facial Orientation and Facial Shape in Extant Great Apes: A Geometric Morphometric Analysis of Covariation

PubMed Central

Neaux, Dimitri; Guy, Franck; Gilissen, Emmanuel; Coudyzer, Walter; Vignaud, Patrick; Ducrocq, Stéphane

2013-01-01

The organization of the bony face is complex, its morphology being influenced in part by the rest of the cranium. Characterizing the facial morphological variation and craniofacial covariation patterns in extant hominids is fundamental to the understanding of their evolutionary history. Numerous studies on hominid facial shape have proposed hypotheses concerning the relationship between the anterior facial shape, facial block orientation and basicranial flexion. In this study we test these hypotheses in a sample of adult specimens belonging to three extant hominid genera (Homo, Pan and Gorilla). Intraspecific variation and covariation patterns are analyzed using geometric morphometric methods and multivariate statistics, such as partial least squared on three-dimensional landmarks coordinates. Our results indicate significant intraspecific covariation between facial shape, facial block orientation and basicranial flexion. Hominids share similar characteristics in the relationship between anterior facial shape and facial block orientation. Modern humans exhibit a specific pattern in the covariation between anterior facial shape and basicranial flexion. This peculiar feature underscores the role of modern humans' highly-flexed basicranium in the overall integration of the cranium. Furthermore, our results are consistent with the hypothesis of a relationship between the reduction of the value of the cranial base angle and a downward rotation of the facial block in modern humans, and to a lesser extent in chimpanzees. PMID:23441232

Multivariate Relationships between Statistics Anxiety and Motivational Beliefs

ERIC Educational Resources Information Center

Baloglu, Mustafa; Abbassi, Amir; Kesici, Sahin

2017-01-01

In general, anxiety has been found to be associated with motivational beliefs and the current study investigated multivariate relationships between statistics anxiety and motivational beliefs among 305 college students (60.0% women). The Statistical Anxiety Rating Scale, the Motivated Strategies for Learning Questionnaire, and a set of demographic…

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariable feedback design - Concepts for a classical/modern synthesis

NASA Technical Reports Server (NTRS)

Doyle, J. C.; Stein, G.

1981-01-01

This paper presents a practical design perspective on multivariable feedback control problems. It reviews the basic issue - feedback design in the face of uncertainties - and generalizes known single-input, single-output (SISO) statements and constraints of the design problem to multiinput, multioutput (MIMO) cases. Two major MIMO design approaches are then evaluated in the context of these results.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

Determinants of modern contraceptive utilization among married women of reproductive age group in North Shoa Zone, Amhara Region, Ethiopia.

PubMed

Mohammed, Abdurahman; Woldeyohannes, Desalegn; Feleke, Amsalu; Megabiaw, Berihun

2014-02-03

Ethiopia is the second most populous country in Africa with high fertility and fast population growth rate. It is also one of the countries with high maternal and child mortality rate in sub-Saharan Africa Family planning is a crucial strategy to halt the fast population growth, to reduce child mortality and improve maternal health (Millennium Development Goal 4 and 5). Therefore, this study aimed to assess the prevalence and determinants of modern contraceptive utilization among married women of reproductive age group. A community based cross-sectional study was conducted from August 15 to September 1, 2010 among married women aged 15-49 years in Debre Birhan District. Multistage sampling technique was used to select a total of 851 study participants. A pre-tested structured questionnaire was used for gathering data. Bivariate and multivariate logistic regression analyses were performed using SPSS version 16.0 statistical package. Modern contraceptive prevalence rate among currently married women was 46.9%. Injectable contraceptives were the most frequently used methods (62.9%), followed by intrauterine device (16.8%), pills (14%), norplant (4.3%), male condom (1.2%) and female sterilization (0.8%). Multiple logistic regression model revealed that the need for more children (AOR 9.27, 95% CI 5.43-15.84), husband approve (AOR 2.82, 95% CI 1.67-4.80), couple's discussion about family planning issues (AOR 7.32, 95% CI 3.60-14.86). Similarly, monthly family income and number of living children were significantly associated with the use of modern contraceptives. Modern contraceptive use was high in the district. Couple's discussion and husband approval of contraceptives use were significantly associated with the use of modern contraceptives. Therefore, district health office and concerned stakeholders should focus on couples to encourage communication and male involvement for family planning.

Determinants of modern contraceptive utilization among married women of reproductive age group in North Shoa Zone, Amhara Region, Ethiopia

PubMed Central

2014-01-01

Background Ethiopia is the second most populous country in Africa with high fertility and fast population growth rate. It is also one of the countries with high maternal and child mortality rate in sub-Saharan Africa Family planning is a crucial strategy to halt the fast population growth, to reduce child mortality and improve maternal health (Millennium Development Goal 4 and 5). Therefore, this study aimed to assess the prevalence and determinants of modern contraceptive utilization among married women of reproductive age group. Methods A community based cross-sectional study was conducted from August 15 to September 1, 2010 among married women aged 15–49 years in Debre Birhan District. Multistage sampling technique was used to select a total of 851 study participants. A pre-tested structured questionnaire was used for gathering data. Bivariate and multivariate logistic regression analyses were performed using SPSS version 16.0 statistical package. Results Modern contraceptive prevalence rate among currently married women was 46.9%. Injectable contraceptives were the most frequently used methods (62.9%), followed by intrauterine device (16.8%), pills (14%), norplant (4.3%), male condom (1.2%) and female sterilization (0.8%). Multiple logistic regression model revealed that the need for more children (AOR 9.27, 95% CI 5.43-15.84), husband approve (AOR 2.82, 95% CI 1.67-4.80), couple’s discussion about family planning issues (AOR 7.32, 95% CI 3.60-14.86). Similarly, monthly family income and number of living children were significantly associated with the use of modern contraceptives. Conclusion Modern contraceptive use was high in the district. Couple’s discussion and husband approval of contraceptives use were significantly associated with the use of modern contraceptives. Therefore, district health office and concerned stakeholders should focus on couples to encourage communication and male involvement for family planning. PMID:24490810

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

Trace Element Study of H Chondrites: Evidence for Meteoroid Streams.

NASA Astrophysics Data System (ADS)

Wolf, Stephen Frederic

1993-01-01

Multivariate statistical analyses, both linear discriminant analysis and logistic regression, of the volatile trace elemental concentrations in H4-6 chondrites reveal compositionally distinguishable subpopulations. Observed difference in volatile trace element composition between Antarctic and non-Antarctic H4-6 chondrites (Lipschutz and Samuels, 1991) can be explained by a compositionaily distinct subpopulation found in Victoria Land, Antarctica. This population of H4-6 chondrites is compositionally distinct from non-Antarctic H4-6 chondrites and from Antarctic H4 -6 chondrites from Queen Maud Land. Comparisons of Queen Maud Land H4-6 chondrites with non-Antarctic H4-6 chondrites do not give reason to believe that these two populations are distinguishable from each other on the basis of the ten volatile trace element concentrations measured. ANOVA indicates that these differences are not the result of trivial causes such as weathering and analytical bias. Thermoluminescence properties of these populations parallels the results of volatile trace element comparisons. Given the differences in terrestrial age between Victoria Land, Queen Maud Land, and modern H4-6 chondrite falls, these results are consistent with a variation in H4-6 chondrite flux on a 300 ky timescale. This conclusion requires the existence of co-orbital meteoroid streams. Statistical analyses of the volatile trace elemental concentrations in non-Antarctic modern falls of H4-6 chondrites also demonstrate that a group of 13 H4-6 chondrites, Cluster 1, selected exclusively for their distinct fall parameters (Dodd, 1992) is compositionally distinguishable from a control group of 45 non-Antarctic modern H4-6 chondrites on the basis of the ten volatile trace element concentrations measured. Model-independent randomization-simulations based on both linear discriminant analysis and logistic regression verify these results. While ANOVA identifies two possible causes for this difference, analytical bias and group classification, a test validation experiment verifies that group classification is the more significant cause of compositional difference between Cluster 1 and non-Cluster 1 modern H4-6 chondrite falls. Thermoluminescence properties of these populations parallels the results of volatile trace element comparisons. This suggests that these meteorites are fragments of a co-orbital meteorite stream derived from a single parent body.

Multivariate assessment of event-related potentials with the t-CWT method.

PubMed

Bostanov, Vladimir

2015-11-05

Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test. This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed. Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.

Multivariate causal attribution and cost-effectiveness of a national mass media campaign in the Philippines.

PubMed

Kincaid, D Lawrence; Do, Mai Phuong

2006-01-01

Cost-effectiveness analysis is based on a simple formula. A dollar estimate of the total cost to conduct a program is divided by the number of people estimated to have been affected by it in terms of some intended outcome. The direct, total costs of most communication campaigns are usually available. Estimating the amount of effect that can be attributed to the communication alone, however is problematical in full-coverage, mass media campaigns where the randomized control group design is not feasible. Single-equation, multiple regression analysis controls for confounding variables but does not adequately address the issue of causal attribution. In this article, multivariate causal attribution (MCA) methods are applied to data from a sample survey of 1,516 married women in the Philippines to obtain a valid measure of the number of new adopters of modern contraceptives that can be causally attributed to a national mass media campaign and to calculate its cost-effectiveness. The MCA analysis uses structural equation modeling to test the causal pathways and to test for endogeneity, biprobit analysis to test for direct effects of the campaign and endogeneity, and propensity score matching to create a statistically equivalent, matched control group that approximates the results that would have been obtained from a randomized control group design. The MCA results support the conclusion that the observed, 6.4 percentage point increase in modern contraceptive use can be attributed to the national mass media campaign and to its indirect effects on attitudes toward contraceptives. This net increase represented 348,695 new adopters in the population of married women at a cost of U.S. $1.57 per new adopter.

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

Predictors of compliance with tornado warnings issued in Joplin, Missouri, in 2011.

PubMed

Paul, Bimal Kanti; Stimers, Mitchel; Caldas, Marcellus

2015-01-01

Joplin, a city in the southwest corner of Missouri, United States, suffered an EF-5 tornado in the late afternoon of 22 May 2011. This event, which claimed the lives of 162 people, represents the deadliest single tornado to strike the US since modern record-keeping began in 1950. This study examines the factors associated with responses to tornado warnings. Based on a post-tornado survey of survivors in Joplin, it reveals that tornado warnings were adequate and timely. Multivariate logistic regression identified four statistically significant determinants of compliance with tornado warnings: number of warning sources, whether respondents were at home when the tornado struck, past tornado experience, and gender. The findings suggest several recommendations, the implementation of which will further improve responses to tornado warnings. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014.

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Resolving relationships between several Neolithic and Mesolithic populations in Northern Eurasia using geometric morphometrics.

PubMed

Stansfield Bulygina, Ekaterina; Rasskasova, Anna; Berezina, Natalia; Soficaru, Andrei D

2017-09-01

Remains from several Eastern European and Siberian Mesolithic and Neolithic sites are analysed to clarify their biological relationships. We assume that groups' geographical distances correlate with genetic and, therefore, morphological distances between them. Material includes complete male crania from several Mesolithic and Neolithic burial sites across Northern Eurasia and from several modern populations. Geometric morphometrics and multivariate statistical techniques are applied to explore morphological trends, group distances, and correlations with their geographical position, climate, and the time of origin. Despite an overlap in the morphology among the modern and archeological groups, some of them show significant morphological distances. Geographical parameters account for only a small proportion of cranial variation in the sample, with larger variance explained by geography and age together. Expectations of isolation by distance are met in some but not in all cases. Climate accounts for a large proportion of autocorrelation with geography. Nearest-neighbor joining trees demonstrate group relationships predicted by the regression on geography and on climate. The obtained results are discussed in application to relationships between particular groups. Unlike the Ukrainian Mesolithic, the Yuzhny Oleni Ostrov Mesolithic displays a high morphological affinity with several groups from Northern Eurasia of both European and Asian origin. A possibility of a common substrate for the Yuzhny Oleni Ostrov Mesolithic and Siberian Neolithic groups is reviewed. The Siberian Neolithic is shown to have morphological connection with both modern Siberian groups and the Native North Americans. © 2017 Wiley Periodicals, Inc.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Testing the equivalence of modern human cranial covariance structure: Implications for bioarchaeological applications.

PubMed

von Cramon-Taubadel, Noreen; Schroeder, Lauren

2016-10-01

Estimation of the variance-covariance (V/CV) structure of fragmentary bioarchaeological populations requires the use of proxy extant V/CV parameters. However, it is currently unclear whether extant human populations exhibit equivalent V/CV structures. Random skewers (RS) and hierarchical analyses of common principal components (CPC) were applied to a modern human cranial dataset. Cranial V/CV similarity was assessed globally for samples of individual populations (jackknifed method) and for pairwise population sample contrasts. The results were examined in light of potential explanatory factors for covariance difference, such as geographic region, among-group distance, and sample size. RS analyses showed that population samples exhibited highly correlated multivariate responses to selection, and that differences in RS results were primarily a consequence of differences in sample size. The CPC method yielded mixed results, depending upon the statistical criterion used to evaluate the hierarchy. The hypothesis-testing (step-up) approach was deemed problematic due to sensitivity to low statistical power and elevated Type I errors. In contrast, the model-fitting (lowest AIC) approach suggested that V/CV matrices were proportional and/or shared a large number of CPCs. Pairwise population sample CPC results were correlated with cranial distance, suggesting that population history explains some of the variability in V/CV structure among groups. The results indicate that patterns of covariance in human craniometric samples are broadly similar but not identical. These findings have important implications for choosing extant covariance matrices to use as proxy V/CV parameters in evolutionary analyses of past populations. © 2016 Wiley Periodicals, Inc.

F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

NASA Technical Reports Server (NTRS)

Soeder, J. F.

1983-01-01

As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

mvMapper: statistical and geographical data exploration and visualization of multivariate analysis of population structure

USDA-ARS?s Scientific Manuscript database

Characterizing population genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata is not always easily integrated into t...

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

NASA Technical Reports Server (NTRS)

Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

An Improved Method to Control the Critical Parameters of a Multivariable Control System

NASA Astrophysics Data System (ADS)

Subha Hency Jims, P.; Dharmalingam, S.; Wessley, G. Jims John

2017-10-01

The role of control systems is to cope with the process deficiencies and the undesirable effect of the external disturbances. Most of the multivariable processes are highly iterative and complex in nature. Aircraft systems, Modern Power Plants, Refineries, Robotic systems are few such complex systems that involve numerous critical parameters that need to be monitored and controlled. Control of these important parameters is not only tedious and cumbersome but also is crucial from environmental, safety and quality perspective. In this paper, one such multivariable system, namely, a utility boiler has been considered. A modern power plant is a complex arrangement of pipework and machineries with numerous interacting control loops and support systems. In this paper, the calculation of controller parameters based on classical tuning concepts has been presented. The controller parameters thus obtained and employed has controlled the critical parameters of a boiler during fuel switching disturbances. The proposed method can be applied to control the critical parameters like elevator, aileron, rudder, elevator trim rudder and aileron trim, flap control systems of aircraft systems.

A Civilian/Military Trauma Institute: National Trauma Coordinating Center

DTIC Science & Technology

2015-12-01

zip codes was used in “proximity to violence” analysis. Data were analyzed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Multivariable linear...number of adverse events and serious events was not statistically higher in one group, the incidence of deep venous thrombosis (DVT) was statistically ...subjects the lack of statistical difference on multivariate analysis may be related to an underpowered sample size. It was recommended that the

A new test of multivariate nonlinear causality

PubMed Central

Bai, Zhidong; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power. PMID:29304085

A new test of multivariate nonlinear causality.

PubMed

Bai, Zhidong; Hui, Yongchang; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Automating Geospatial Visualizations with Smart Default Renderers for Data Exploration Web Applications

NASA Astrophysics Data System (ADS)

Ekenes, K.

2017-12-01

This presentation will outline the process of creating a web application for exploring large amounts of scientific geospatial data using modern automated cartographic techniques. Traditional cartographic methods, including data classification, may inadvertently hide geospatial and statistical patterns in the underlying data. This presentation demonstrates how to use smart web APIs that quickly analyze the data when it loads, and provides suggestions for the most appropriate visualizations based on the statistics of the data. Since there are just a few ways to visualize any given dataset well, it is imperative to provide smart default color schemes tailored to the dataset as opposed to static defaults. Since many users don't go beyond default values, it is imperative that they are provided with smart default visualizations. Multiple functions for automating visualizations are available in the Smart APIs, along with UI elements allowing users to create more than one visualization for a dataset since there isn't a single best way to visualize a given dataset. Since bivariate and multivariate visualizations are particularly difficult to create effectively, this automated approach removes the guesswork out of the process and provides a number of ways to generate multivariate visualizations for the same variables. This allows the user to choose which visualization is most appropriate for their presentation. The methods used in these APIs and the renderers generated by them are not available elsewhere. The presentation will show how statistics can be used as the basis for automating default visualizations of data along continuous ramps, creating more refined visualizations while revealing the spread and outliers of the data. Adding interactive components to instantaneously alter visualizations allows users to unearth spatial patterns previously unknown among one or more variables. These applications may focus on a single dataset that is frequently updated, or configurable for a variety of datasets from multiple sources.

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Postoperative Radiotherapy Patterns of Care and Survival Implications for Medulloblastoma in Young Children.

PubMed

Kann, Benjamin H; Park, Henry S; Lester-Coll, Nataniel H; Yeboa, Debra N; Benitez, Viviana; Khan, Atif J; Bindra, Ranjit S; Marks, Asher M; Roberts, Kenneth B

2016-12-01

Postoperative radiotherapy to the craniospinal axis is standard-of-care for pediatric medulloblastoma but is associated with long-term morbidity, particularly in young children. With the advent of modern adjuvant chemotherapy strategies, postoperative radiotherapy deferral has gained acceptance in children younger than 3 years, although it remains controversial in older children. To analyze recent postoperative radiotherapy national treatment patterns and implications for overall survival in patients with medulloblastoma ages 3 to 8 years. Using the National Cancer Data Base, patients ages 3 to 8 years diagnosed as having histologically confirmed medulloblastoma in 2004 to 2012, without distant metastases, who underwent surgery and adjuvant chemotherapy with or without postoperative radiotherapy at facilities nationwide accredited by the Commission on Cancer were identified. Patients were designated as having "postoperative radiotherapy upfront" if they received radiotherapy within 90 days of surgery or "postoperative radiotherapy deferred" otherwise. Factors associated with postoperative radiotherapy deferral were identified using multivariable logistic regression. Overall survival (OS) was compared using Kaplan-Meier analysis with log-rank tests and multivariable Cox regression. Statistical tests were 2-sided. Postoperative radiotherapy utilization and overall survival. Among 816 patients, 123 (15.1%) had postoperative radiotherapy deferred, and 693 (84.9%) had postoperative radiotherapy upfront; 36.8% of 3-year-olds and 4.1% of 8-year-olds had postoperative radiotherapy deferred (P < .001). On multivariable logistic regression, variables associated with postoperative radiotherapy deferral were age (odds ratio [OR], 0.57 per year; 95% CI, 0.49-0.67 per year) and year of diagnosis (OR, 1.18 per year; 95% CI, 1.08-1.29 per year). On survival analysis, with median follow-up of 4.8 years, OS was improved for those receiving postoperative radiotherapy upfront vs postoperative radiotherapy deferred (5-year OS: 82.0% vs 63.4%; P < .001). On multivariable analysis, variables associated with poorer OS were postoperative radiotherapy deferral (hazards ratio [HR], 1.95; 95% CI, 1.15-3.31); stage M1-3 disease (HR, 1.86; 95% CI, 1.10-3.16), and low facility volume (HR, 1.75; 95% CI, 1.04-2.94). Our national database analysis reveals a higher-than-expected and increasing rate of postoperative radiotherapy deferral in children with medulloblastoma ages 3 to 8 years. The analysis suggests that postoperative radiotherapy deferral is associated with worse survival in this age group, even in the modern era of chemotherapy.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Integration of ecological indices in the multivariate evaluation of an urban inventory of street trees

Treesearch

J. Grabinsky; A. Aldama; A. Chacalo; H. J. Vazquez

2000-01-01

Inventory data of Mexico City's street trees were studied using classical statistical arboricultural and ecological statistical approaches. Multivariate techniques were applied to both. Results did not differ substantially and were complementary. It was possible to reduce inventory data and to group species, boroughs, blocks, and variables.

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation.

PubMed

Lee, Kyu Ha; Tadesse, Mahlet G; Baccarelli, Andrea A; Schwartz, Joel; Coull, Brent A

2017-03-01

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants. © 2016, The International Biometric Society.

The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

ERIC Educational Resources Information Center

Kirisci, Levent; Hsu, Tse-Chi

Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Atrial Electrogram Fractionation Distribution before and after Pulmonary Vein Isolation in Human Persistent Atrial Fibrillation-A Retrospective Multivariate Statistical Analysis.

PubMed

Almeida, Tiago P; Chu, Gavin S; Li, Xin; Dastagir, Nawshin; Tuan, Jiun H; Stafford, Peter J; Schlindwein, Fernando S; Ng, G André

2017-01-01

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model. Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs. Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination ( P < 0.0001). Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure. PMID:26786791

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Consuming sex: the association between modern goods, lifestyles and sexual behaviour among youth in Madagascar

PubMed Central

2013-01-01

Background Ethnographic evidence suggests that transactional sex is sometimes motivated by youth’s interest in the consumption of modern goods as much as it is in basic survival. There are very few quantitative studies that examine the association between young people’s interests in the consumption of modern goods and their sexual behaviour. We examined this association in two regions and four residence zones of Madagascar: urban, peri-urban and rural Antananarivo, and urban Antsiranana. We expected risky sexual behaviour would be associated with interests in consuming modern goods or lifestyles; urban residence; and socio-cultural characteristics. Methods We administered a population-based survey to 2, 255 youth ages 15–24 in all four residence zones. Focus group discussions guided the survey instrument which assessed socio-demographic and economic characteristics, consumption of modern goods, preferred activities and sexual behaviour. Our outcomes measures included: multiple sexual partners in the last year (for men and women); and ever practicing transactional sex (for women). Results Overall, 7.3% of women and 30.7% of men reported having had multiple partners in the last year; and 5.9% of women reported ever practicing transactional sex. Bivariate results suggested that for both men and women having multiple partners was associated with perceptions concerning the importance of fashion and a series of activities associated with modern lifestyles. A subset of lifestyle characteristics remained significant in multivariate models. For transactional sex bivariate results suggested perceptions around fashion, nightclub attendance, and getting to know a foreigner were key determinants; and all remained significant in multivariate analysis. We found peri-urban residence more associated with transactional sex than urban residence; and ethnic origin was the strongest predictor of both outcomes for women. Conclusions While we found indication of an association between sexual behaviour and interest in modern goods, or modern lifestyles, such processes did not single-handedly explain risky sexual behaviour among youth; these behaviours were also shaped by culture and conditions of economic uncertainty. These determinants must all be accounted for when developing interventions to reduce risky transactional sex and vulnerability to HIV. PMID:23510104

Consuming sex: the association between modern goods, lifestyles and sexual behaviour among youth in Madagascar.

PubMed

Stoebenau, Kirsten; Nair, Rama C; Rambeloson, Valérie; Rakotoarison, Paul Ghislain; Razafintsalama, Violette; Labonté, Ronald

2013-03-19

Ethnographic evidence suggests that transactional sex is sometimes motivated by youth's interest in the consumption of modern goods as much as it is in basic survival. There are very few quantitative studies that examine the association between young people's interests in the consumption of modern goods and their sexual behaviour. We examined this association in two regions and four residence zones of Madagascar: urban, peri-urban and rural Antananarivo, and urban Antsiranana. We expected risky sexual behaviour would be associated with interests in consuming modern goods or lifestyles; urban residence; and socio-cultural characteristics. We administered a population-based survey to 2, 255 youth ages 15-24 in all four residence zones. Focus group discussions guided the survey instrument which assessed socio-demographic and economic characteristics, consumption of modern goods, preferred activities and sexual behaviour. Our outcomes measures included: multiple sexual partners in the last year (for men and women); and ever practicing transactional sex (for women). Overall, 7.3% of women and 30.7% of men reported having had multiple partners in the last year; and 5.9% of women reported ever practicing transactional sex. Bivariate results suggested that for both men and women having multiple partners was associated with perceptions concerning the importance of fashion and a series of activities associated with modern lifestyles. A subset of lifestyle characteristics remained significant in multivariate models. For transactional sex bivariate results suggested perceptions around fashion, nightclub attendance, and getting to know a foreigner were key determinants; and all remained significant in multivariate analysis. We found peri-urban residence more associated with transactional sex than urban residence; and ethnic origin was the strongest predictor of both outcomes for women. While we found indication of an association between sexual behaviour and interest in modern goods, or modern lifestyles, such processes did not single-handedly explain risky sexual behaviour among youth; these behaviours were also shaped by culture and conditions of economic uncertainty. These determinants must all be accounted for when developing interventions to reduce risky transactional sex and vulnerability to HIV.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

PubMed

Adams, Dean C

2014-09-01

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Shape and mechanics in thalattosuchian (Crocodylomorpha) skulls: implications for feeding behaviour and niche partitioning

PubMed Central

Pierce, S E; Angielczyk, K D; Rayfield, E J

2009-01-01

Variation in modern crocodilian and extinct thalattosuchian crocodylomorph skull morphology is only weakly correlated with phylogeny, implying that factors other than evolutionary proximity play important roles in determining crocodile skull shape. To further explore factors potentially influencing morphological differentiation within the Thalattosuchia, we examine teleosaurid and metriorhynchid skull shape variation within a mechanical and dietary context using a combination of finite element modelling and multivariate statistics. Patterns of stress distribution through the skull were found to be very similar in teleosaurid and metriorhynchid species, with stress peaking at the posterior constriction of the snout and around the enlarged supratemporal fenestrae. However, the magnitudes of stresses differ, with metriorhynchids having generally stronger skulls. As with modern crocodilians, a strong linear relationship between skull length and skull strength exists, with short-snouted morphotypes experiencing less stress through the skull than long-snouted morphotypes under equivalent loads. Selection on snout shape related to dietary preference was found to work in orthogonal directions in the two families: diet is associated with snout length in teleosaurids and with snout width in metriorhynchids, suggesting that teleosaurid skulls were adapted for speed of attack and metriorhynchid skulls for force production. Evidence also indicates that morphological and functional differentiation of the skull occurred as a result of dietary preference, allowing closely related sympatric species to exploit a limited environment. Comparisons of the mechanical performance of the thalattosuchian skull with extant crocodilians show that teleosaurids and long-snouted metriorhynchids exhibit stress magnitudes similar to or greater than those of long-snouted modern forms, whereas short-snouted metriorhynchids display stress magnitudes converging on those found in short-snouted modern species. As a result, teleosaurids and long-snouted metriorhynchids were probably restricted to lateral attacks of the head and neck, but short-snouted metriorhynchids may have been able to employ the grasp and shake and/or ‘death roll’ feeding and foraging behaviours. PMID:19702868

Risk perception and sexual behavior in HPV-vaccinated and unvaccinated young Colombian women.

PubMed

Ruiz-Sternberg, Angela M; Pinzón-Rondón, Ángela M

2014-09-01

To compare sexual behaviors and risk perception between young women vaccinated for HPV and unvaccinated Colombian women. In a cross-sectional design study, 1436 women (231 adolescents, <18 years; 1205 young women, 18-26 years) completed a self-administered questionnaire between May 2011 and March 2012 in Bogotá, Colombia. Data from vaccinated and unvaccinated women were compared by descriptive statistics and multivariate models. Sexual risk behaviors were not associated with vaccination after adjustment for risk perception, age, educational level, and HPV knowledge. By contrast, vaccination was associated with higher routine Pap smear screening (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.69-3.28), use of modern contraceptives (OR, 2.02; 95% CI, 1.26-3.22), and consistent use of condoms (OR, 1.49; 95% CI, 1.11-2.01). Vaccinated young women were more likely to have had sex (OR, 2.08; 95% CI, 1.56-2.78), but sexual debut among adolescents was not associated with vaccination. In bivariate and multivariate analyses, vaccination status was negatively associated with perceived risk of HPV infection, warts, and cervical cancer. There was no association between vaccination and perceived risk of sexually transmitted infections in any model. No association was found between changes in risk perception after HPV vaccination and sexual risk behaviors. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Association of elevated pretransplant sCD30 levels with graft loss in 206 patients treated with modern immunosuppressive therapies after renal transplantation.

PubMed

Heinemann, Falko M; Rebmann, Vera; Witzke, Oliver; Philipp, Thomas; Broelsch, Christoph E; Grosse-Wilde, Hans

2007-03-27

Recent reports suggest that high pretransplant serum levels of soluble CD30 (sCD30) are a risk factor for rejections after kidney transplantation. The aim of our study was to elucidate the predictive value of pretransplant sCD30 levels for kidney transplantation outcome in a single-center patient cohort that has been treated with modern immunosuppressive therapies after transplantation. We retrospectively analyzed sCD30 in multiple pretransplant sera from 206 patients, of whom 174 were transplanted with a cadaveric kidney and 32 patients received an allograft from a living donor. Renal function after transplantation was estimated by measuring serum creatinine and by rejection diagnosis. We could demonstrate a statistically significant association between increased pretransplant sCD30 values and graft failures (P=0.005). Receiver operating curve analysis revealed a cutoff value of 124 U/mL pretransplant sCD30. A multivariate analysis confirmed pretransplant sCD30 values >124 U/mL (P=0.011) and rejection episodes (P<0.0001) as independent risk factors for graft loss. Our study revealed a strong correlation between pretransplant sCD30 levels and the incidence of graft failure, but we could not confirm that the development of rejection episodes is correlated with pretransplant sCD30 values.

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Matching pollution with adaptive changes in mangrove plants by multivariate statistics. A case study, Rhizophora mangle from four neotropical mangroves in Brazil.

PubMed

Souza, Iara da Costa; Morozesk, Mariana; Duarte, Ian Drumond; Bonomo, Marina Marques; Rocha, Lívia Dorsch; Furlan, Larissa Maria; Arrivabene, Hiulana Pereira; Monferrán, Magdalena Victoria; Matsumoto, Silvia Tamie; Milanez, Camilla Rozindo Dias; Wunderlin, Daniel Alberto; Fernandes, Marisa Narciso

2014-08-01

Roots of mangrove trees have an important role in depurating water and sediments by retaining metals that may accumulate in different plant tissues, affecting physiological processes and anatomy. The present study aimed to evaluate adaptive changes in root of Rhizophora mangle in response to different levels of chemical elements (metals/metalloids) in interstitial water and sediments from four neotropical mangroves in Brazil. What sets this study apart from other studies is that we not only investigate adaptive modifications in R. mangle but also changes in environments where this plant grows, evaluating correspondence between physical, chemical and biological issues by a combined set of multivariate statistical methods (pattern recognition). Thus, we looked to match changes in the environment with adaptations in plants. Multivariate statistics highlighted that the lignified periderm and the air gaps are directly related to the environmental contamination. Current results provide new evidences of root anatomical strategies to deal with contaminated environments. Multivariate statistics greatly contributes to extrapolate results from complex data matrixes obtained when analyzing environmental issues, pointing out parameters involved in environmental changes and also evidencing the adaptive response of the exposed biota. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

The "Diagnostic and Statistical Manual of Mental Disorders" as a Major Form of Dehumanization in the Modern World

ERIC Educational Resources Information Center

Gambrill, Eileen

2014-01-01

The "Diagnostic and Statistical Manual of Mental Disorders" (DSM) is one of the most successful technologies in modern times. In spite of well-argued critiques, the DSM and the idea of "mental illness" on which it is based flourish, with ever more (mis)behaviors labeled as brain diseases. Problems in living and related distress…

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Asymptotic Distribution of the Likelihood Ratio Test Statistic for Sphericity of Complex Multivariate Normal Distribution.

DTIC Science & Technology

1981-08-01

RATIO TEST STATISTIC FOR SPHERICITY OF COMPLEX MULTIVARIATE NORMAL DISTRIBUTION* C. Fang P. R. Krishnaiah B. N. Nagarsenker** August 1981 Technical...and their applications in time sEries, the reader is referred to Krishnaiah (1976). Motivated by the applications in the area of inference on multiple...for practical purposes. Here, we note that Krishnaiah , Lee and Chang (1976) approxi- mated the null distribution of certain power of the likeli

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

GAISE 2016 Promotes Statistical Literacy

ERIC Educational Resources Information Center

Schield, Milo

2017-01-01

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and--more importantly--confounding as recommended…

Avoid lost discoveries, because of violations of standard assumptions, by using modern robust statistical methods.

PubMed

Wilcox, Rand; Carlson, Mike; Azen, Stan; Clark, Florence

2013-03-01

Recently, there have been major advances in statistical techniques for assessing central tendency and measures of association. The practical utility of modern methods has been documented extensively in the statistics literature, but they remain underused and relatively unknown in clinical trials. Our objective was to address this issue. STUDY DESIGN AND PURPOSE: The first purpose was to review common problems associated with standard methodologies (low power, lack of control over type I errors, and incorrect assessments of the strength of the association). The second purpose was to summarize some modern methods that can be used to circumvent such problems. The third purpose was to illustrate the practical utility of modern robust methods using data from the Well Elderly 2 randomized controlled trial. In multiple instances, robust methods uncovered differences among groups and associations among variables that were not detected by classic techniques. In particular, the results demonstrated that details of the nature and strength of the association were sometimes overlooked when using ordinary least squares regression and Pearson correlation. Modern robust methods can make a practical difference in detecting and describing differences between groups and associations between variables. Such procedures should be applied more frequently when analyzing trial-based data. Copyright © 2013 Elsevier Inc. All rights reserved.

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Trends in Citations to Books on Epidemiological and Statistical Methods in the Biomedical Literature

PubMed Central

Porta, Miquel; Vandenbroucke, Jan P.; Ioannidis, John P. A.; Sanz, Sergio; Fernandez, Esteve; Bhopal, Raj; Morabia, Alfredo; Victora, Cesar; Lopez, Tomàs

2013-01-01

Background There are no analyses of citations to books on epidemiological and statistical methods in the biomedical literature. Such analyses may shed light on how concepts and methods changed while biomedical research evolved. Our aim was to analyze the number and time trends of citations received from biomedical articles by books on epidemiological and statistical methods, and related disciplines. Methods and Findings The data source was the Web of Science. The study books were published between 1957 and 2010. The first year of publication of the citing articles was 1945. We identified 125 books that received at least 25 citations. Books first published in 1980–1989 had the highest total and median number of citations per year. Nine of the 10 most cited texts focused on statistical methods. Hosmer & Lemeshow's Applied logistic regression received the highest number of citations and highest average annual rate. It was followed by books by Fleiss, Armitage, et al., Rothman, et al., and Kalbfleisch and Prentice. Fifth in citations per year was Sackett, et al., Evidence-based medicine. The rise of multivariate methods, clinical epidemiology, or nutritional epidemiology was reflected in the citation trends. Educational textbooks, practice-oriented books, books on epidemiological substantive knowledge, and on theory and health policies were much less cited. None of the 25 top-cited books had the theoretical or sociopolitical scope of works by Cochrane, McKeown, Rose, or Morris. Conclusions Books were mainly cited to reference methods. Books first published in the 1980s continue to be most influential. Older books on theory and policies were rooted in societal and general medical concerns, while the most modern books are almost purely on methods. PMID:23667447

Trends in citations to books on epidemiological and statistical methods in the biomedical literature.

PubMed

Porta, Miquel; Vandenbroucke, Jan P; Ioannidis, John P A; Sanz, Sergio; Fernandez, Esteve; Bhopal, Raj; Morabia, Alfredo; Victora, Cesar; Lopez, Tomàs

2013-01-01

There are no analyses of citations to books on epidemiological and statistical methods in the biomedical literature. Such analyses may shed light on how concepts and methods changed while biomedical research evolved. Our aim was to analyze the number and time trends of citations received from biomedical articles by books on epidemiological and statistical methods, and related disciplines. The data source was the Web of Science. The study books were published between 1957 and 2010. The first year of publication of the citing articles was 1945. We identified 125 books that received at least 25 citations. Books first published in 1980-1989 had the highest total and median number of citations per year. Nine of the 10 most cited texts focused on statistical methods. Hosmer & Lemeshow's Applied logistic regression received the highest number of citations and highest average annual rate. It was followed by books by Fleiss, Armitage, et al., Rothman, et al., and Kalbfleisch and Prentice. Fifth in citations per year was Sackett, et al., Evidence-based medicine. The rise of multivariate methods, clinical epidemiology, or nutritional epidemiology was reflected in the citation trends. Educational textbooks, practice-oriented books, books on epidemiological substantive knowledge, and on theory and health policies were much less cited. None of the 25 top-cited books had the theoretical or sociopolitical scope of works by Cochrane, McKeown, Rose, or Morris. Books were mainly cited to reference methods. Books first published in the 1980s continue to be most influential. Older books on theory and policies were rooted in societal and general medical concerns, while the most modern books are almost purely on methods.

Multivariate statistical analysis of low-voltage EDS spectrum images

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

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

Quick Overview Scout 2008 Version 1.0

EPA Science Inventory

The Scout 2008 version 1.0 statistical software package has been updated from past DOS and Windows versions to provide classical and robust univariate and multivariate graphical and statistical methods that are not typically available in commercial or freeware statistical softwar...

The Galileo scan platform pointing control system - A modern control theoretic viewpoint

NASA Technical Reports Server (NTRS)

Sevaston, G. E.; Macala, G. A.; Man, G. K.

1985-01-01

The current Galileo scan platform pointing control system (SPPCS) is described, and ways in which modern control concepts could serve to enhance it are considered. Of particular interest are: the multi-variable design model and overall control system architecture, command input filtering, feedback compensator and command input design, stability robustness constraint for both continuous time control systems and for sampled data control systems, and digital implementation of the control system. The proposed approach leads to the design of a system that is similar to current Galileo SPPCS configuration, but promises to be more systematic.

Woods and Russell, Hill, and the emergence of medical statistics

PubMed Central

Farewell, Vern; Johnson, Tony

2010-01-01

In 1937, Austin Bradford Hill wrote Principles of Medical Statistics (Lancet: London, 1937) that became renowned throughout the world and is widely associated with the birth of modern medical statistics. Some 6 years earlier Hilda Mary Woods and William Thomas Russell, colleagues of Hill at the London School of Hygiene and Tropical Medicine, wrote a similar book An Introduction to Medical Statistics (PS King and Son: London, 1931) that is little known today. We trace the origins of these two books from the foundations of early demography and vital statistics, and make a detailed examination of some of their chapters. It is clear that these texts mark a watershed in the history of medical statistics that demarcates the vital statistics of the nineteenth and early twentieth centuries from the modern discipline. Moreover, we consider that the book by Woods and Russell is of some importance in the development of medical statistics and we describe and acknowledge their place in the history of this discipline. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20535761

Woods and Russell, Hill, and the emergence of medical statistics.

PubMed

Farewell, Vern; Johnson, Tony

2010-06-30

In 1937, Austin Bradford Hill wrote Principles of Medical Statistics (Lancet: London, 1937) that became renowned throughout the world and is widely associated with the birth of modern medical statistics. Some 6 years earlier Hilda Mary Woods and William Thomas Russell, colleagues of Hill at the London School of Hygiene and Tropical Medicine, wrote a similar book An Introduction to Medical Statistics (PS King and Son: London, 1931) that is little known today. We trace the origins of these two books from the foundations of early demography and vital statistics, and make a detailed examination of some of their chapters. It is clear that these texts mark a watershed in the history of medical statistics that demarcates the vital statistics of the nineteenth and early twentieth centuries from the modern discipline. Moreover, we consider that the book by Woods and Russell is of some importance in the development of medical statistics and we describe and acknowledge their place in the history of this discipline. (c) 2010 John Wiley & Sons, Ltd.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

This report contains edited and reviewed versions of papers presented at a workshop held at the University of Vermont in April 1980. Topics include sampling avian habitats, multivariate methods, applications, examples, and new approaches to analysis and interpretation.

Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

Is Household Wealth Associated With Use of Long-Acting Reversible and Permanent Methods of Contraception? A Multi-Country Analysis

PubMed Central

Ugaz, Jorge I; Chatterji, Minki; Gribble, James N; Banke, Kathryn

2016-01-01

Abstract As programs continue to expand access to family planning information, services, and products, it is critical that these efforts be undertaken with an equity lens, ensuring that regardless of socioeconomic status, all women and couples can use the method that meets their needs. This study explores the relationship between household wealth and the use of long-acting and permanent methods (LAPMs) versus short-acting methods of contraception among modern method users, using multivariate analyses based on Demographic Health Survey data from 30 developing countries conducted between 2006 and 2013. Overall, and controlling for relevant individual and household characteristics including age, number of living children, education, and urban/rural residence, we found that wealthier women were more likely than poorer women to use LAPMs instead of short-acting methods: 20 of the 30 countries showed a positive and statistically significant association between wealth and LAPM use. For 10 of those countries, however, LAPM use was significantly higher only for the top (1 or 2) wealthiest quintiles. Eight countries showed no broad pattern of association, while in 2 countries—Bangladesh and India—poorer women were more likely to use LAPMs than wealthier women. The positive association between wealth and LAPM use was found most consistently in the Latin American and the Caribbean countries in our sample. These findings can help program implementers respond better to women’s needs for modern contraception, especially in reaching women from lower- and middle-income households. PMID:27016543

Is Household Wealth Associated With Use of Long-Acting Reversible and Permanent Methods of Contraception? A Multi-Country Analysis.

PubMed

Ugaz, Jorge I; Chatterji, Minki; Gribble, James N; Banke, Kathryn

2016-03-01

As programs continue to expand access to family planning information, services, and products, it is critical that these efforts be undertaken with an equity lens, ensuring that regardless of socioeconomic status, all women and couples can use the method that meets their needs. This study explores the relationship between household wealth and the use of long-acting and permanent methods (LAPMs) versus short-acting methods of contraception among modern method users, using multivariate analyses based on Demographic Health Survey data from 30 developing countries conducted between 2006 and 2013. Overall, and controlling for relevant individual and household characteristics including age, number of living children, education, and urban/rural residence, we found that wealthier women were more likely than poorer women to use LAPMs instead of short-acting methods: 20 of the 30 countries showed a positive and statistically significant association between wealth and LAPM use. For 10 of those countries, however, LAPM use was significantly higher only for the top (1 or 2) wealthiest quintiles. Eight countries showed no broad pattern of association, while in 2 countries-Bangladesh and India-poorer women were more likely to use LAPMs than wealthier women. The positive association between wealth and LAPM use was found most consistently in the Latin American and the Caribbean countries in our sample. These findings can help program implementers respond better to women's needs for modern contraception, especially in reaching women from lower- and middle-income households. © Ugaz et al.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Problematizing Statistical Literacy: An Intersection of Critical and Statistical Literacies

ERIC Educational Resources Information Center

Weiland, Travis

2017-01-01

In this paper, I problematize traditional notions of statistical literacy by juxtaposing it with critical literacy. At the school level statistical literacy is vitally important for students who are preparing to become citizens in modern societies that are increasingly shaped and driven by data based arguments. The teaching of statistics, which is…

Teaching Statistics Online: A Decade's Review of the Literature about What Works

ERIC Educational Resources Information Center

Mills, Jamie D.; Raju, Dheeraj

2011-01-01

A statistics course can be a very challenging subject to teach. To enhance learning, today's modern course in statistics might incorporate many different aspects of technology. Due to advances in technology, teaching statistics online has also become a popular course option. Although researchers are studying how to deliver statistics courses in…

Fundamental investigation of the tribological and mechanical responses of materials and nanostructures

NASA Astrophysics Data System (ADS)

Bucholz, Eric W.

In the field of tribology, the ability to predict, and ultimately control, frictional performance is of critical importance for the optimization of tribological systems. As such, understanding the specific mechanisms involved in the lubrication processes for different materials is a fundamental step in tribological system design. In this work, a combination of computational and experimental methods that include classical molecular dynamics (MD) simulations, atomic force microscopy (AFM) experiments, and multivariate statistical analyses provides fundamental insight into the tribological and mechanical properties of carbon-based and inorganic nanostructures, lamellar materials, and inorganic ceramic compounds. One class of materials of modern interest for tribological applications is nanoparticles, which can be employed either as solid lubricating films or as lubricant additives. In experimental systems, however, it is often challenging to attain the in situ observation of tribological interfaces necessary to identify the atomic-level mechanisms involved during lubrication and response to mechanical deformation. Here, classical MD simulations establish the mechanisms occurring during the friction and compression of several types of nanoparticles including carbon nano-onions, amorphous carbon nanoparticles, and inorganic fullerene-like MoS2 nanoparticles. Specifically, the effect of a nanoparticle's structural properties on the lubrication mechanisms of rolling, sliding, and lamellar exfoliation is indicated; the findings quantify the relative impact of each mechanism on the tribological and mechanical properties of these nanoparticles. Beyond identifying the lubrication mechanisms of known lubricating materials, the continual advancement of modern technology necessitates the identification of new candidate materials for use in tribological applications. To this effect, atomic-scale AFM friction experiments on the aluminosilicate mineral pyrophyllite demonstrate that pyrophyllite provides a low friction coefficient and low shear stresses as well as a high threshold to interfacial wear; this suggests the potential for use of pyrophyllite as a lubricious material under specific conditions. Also, a robust and accurate model for estimating the friction coefficients of inorganic ceramic materials that is based on the fundamental relationships between material properties is presented, which was developed using multivariate data mining algorithms. These findings provide the tribological community with a new means of quickly identifying candidate materials that may provide specific frictional properties for desired applications.

Research on an innovative design model

NASA Astrophysics Data System (ADS)

Fu, Y.; Fang, H.

2018-03-01

The design methods of furniture are different from east to west; it has been the hotspot of the scholars. However, in terms of the theory of modern design innovation, neither the early creation theory, the modern design theory, nor the widely applied TRIZ theory can fully fit the modern furniture design innovation, so it is urgent to study the modern furniture design theory. This paper is based on the idea of TRIZ theory, using lots of literatures as data, and uses the method of statistical stratification to analyze and sort out the research of modern sitting equipment, and finally put forward the modern furniture design model, which provides new ideas and perspectives for the modern design of Chinese furniture.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Commentary on A General Curriculum in Mathematics for Colleges.

ERIC Educational Resources Information Center

Committee on the Undergraduate Program in Mathematics, Berkeley, CA.

This document constitutes a complete revision of the report of the same name first published in 1965. A new list of basic courses is described, consisting of Calculus I, Calculus II, Elementary Linear Algebra, Multivariable Calculus I, Linear Algebra, and Introductory Modern Algebra. Commentaries outline the content and spirit of these courses in…

Development of a robust framework for controlling high performance turbofan engines

NASA Astrophysics Data System (ADS)

Miklosovic, Robert

This research involves the development of a robust framework for controlling complex and uncertain multivariable systems. Where mathematical modeling is often tedious or inaccurate, the new method uses an extended state observer (ESO) to estimate and cancel dynamic information in real time and dynamically decouple the system. As a result, controller design and tuning become transparent as the number of required model parameters is reduced. Much research has been devoted towards the application of modern multivariable control techniques on aircraft engines. However, few, if any, have been implemented on an operational aircraft, partially due to the difficulty in tuning the controller for satisfactory performance. The new technique is applied to a modern two-spool, high-pressure ratio, low-bypass turbofan with mixed-flow afterburning. A realistic Modular Aero-Propulsion System Simulation (MAPSS) package, developed by NASA, is used to demonstrate the new design process and compare its performance with that of a supplied nominal controller. This approach is expected to reduce gain scheduling over the full operating envelope of the engine and allow a controller to be tuned for engine-to-engine variations.

Statistics: Can We Get beyond Terminal?

ERIC Educational Resources Information Center

Green, Suzy; Carney, JoLynn V.

Recent articles in behavioral sciences statistics literature address the need for modernizing graduate statistics programs and courses. This paper describes the development of one such course and evaluates student background for a class designed to provide a more consumer-oriented type of statistics instruction by focusing on the needs of students…

Accuracy of metric sex analysis of skeletal remains using Fordisc based on a recent skull collection.

PubMed

Ramsthaler, F; Kreutz, K; Verhoff, M A

2007-11-01

It has been generally accepted in skeletal sex determination that the use of metric methods is limited due to the population dependence of the multivariate algorithms. The aim of the study was to verify the applicability of software-based sex estimations outside the reference population group for which discriminant equations have been developed. We examined 98 skulls from recent forensic cases of known age, sex, and Caucasian ancestry from cranium collections in Frankfurt and Mainz (Germany) to determine the accuracy of sex determination using the statistical software solution Fordisc which derives its database and functions from the US American Forensic Database. In a comparison between metric analysis using Fordisc and morphological determination of sex, average accuracy for both sexes was 86 vs 94%, respectively, and males were identified more accurately than females. The ratio of the true test result rate to the false test result rate was not statistically different for the two methodological approaches at a significance level of 0.05 but was statistically different at a level of 0.10 (p=0.06). Possible explanations for this difference comprise different ancestry, age distribution, and socio-economic status compared to the Fordisc reference sample. It is likely that a discriminant function analysis on the basis of more similar European reference samples will lead to more valid and reliable sexing results. The use of Fordisc as a single method for the estimation of sex of recent skeletal remains in Europe cannot be recommended without additional morphological assessment and without a built-in software update based on modern European reference samples.

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies. PMID:28081206

Global vegetation distribution and terrestrial climate evolution at the Eocene-Oligocene transition

NASA Astrophysics Data System (ADS)

Pound, Matthew; Salzmann, Ulrich

2016-04-01

The Eocene - Oligocene transition (EOT; ca. 34-33.5 Ma) is widely considered to be the biggest step in Cenozoic climate evolution. Geochemical marine records show both surface and bottom water cooling, associated with the expansion of Antarctic glaciers and a reduction in the atmospheric CO2 concentration. However, the global response of the terrestrial biosphere to the EOT is less well understood and not uniform when comparing different regions. We present new global vegetation and terrestrial climate reconstructions of the Priabonian (late Eocene; 38-33.9 Ma) and Rupelian (early Oligocene; 33.9-28.45 Ma) by synthesising 215 pollen and spore localities. Using presence/absence data of pollen and spores with multivariate statistics has allowed the reconstruction of palaeo-biomes without relying on modern analogues. The reconstructed palaeo-biomes do not show the equator-ward shift at the EOT, which would be expected from a global cooling. Reconstructions of mean annual temperature, cold month mean temperature and warm month mean temperature do not show a global cooling of terrestrial climate across the EOT. Our new reconstructions differ from previous global syntheses by being based on an internally consistent statistically defined classification of palaeo-biomes and our terrestrial based climate reconstructions are in stark contrast to some marine based climate estimates. Our results raise new questions on the nature and extent of terrestrial global climate change at the EOT.

Human choices and environmental constraints: deciphering the variability of large game procurement from Mousterian to Aurignacian times (MIS 5-3) in southwestern France

NASA Astrophysics Data System (ADS)

Discamps, Emmanuel; Jaubert, Jacques; Bachellerie, François

2011-09-01

The evolution in the selection of prey made by past humans, especially the Neandertals and the first anatomically modern humans, has been widely debated. Between Marine Isotope Stages (MIS) 5 and 3, the accuracy of absolute dating is still insufficient to precisely correlate paleoclimatic and archaeological data. It is often difficult, therefore, to estimate to what extent changes in species procurement are correlated with either climate fluctuations or deliberate cultural choices in terms of subsistence behavior. Here, the full development of archeostratigraphy and Bayesian statistical analysis of absolute dates allows the archeological and paleoclimatic chronologies to be compared. The variability in hunted fauna is investigated using multivariate statistical analysis of quantitative faunal lists of 148 assemblages from 39 archeological sequences from MIS 5 through MIS 3. Despite significant intra-technocomplex variability, it is possible to identify major shifts in the human diet during these stages. The integration of archeological data, paleoclimatic proxies and the ecological characteristics of the different species of prey shows that the shifts in large game hunting can be explained by an adaptation of the human groups to climatic fluctuations. However, even if Middle and Early Upper Paleolithic men adapted to changes in their environment and to contrasting landscapes, they ultimately belonged to the ecosystems of the past and were limited by environmental constraints.

Permanent tooth mineralization in bonobos (Pan paniscus) and chimpanzees (P. troglodytes).

PubMed

Boughner, Julia C; Dean, M Christopher; Wilgenbusch, Chelsea S

2012-12-01

The timing of tooth mineralization in bonobos (Pan paniscus) is virtually uncharacterized. Analysis of these developmental features in bonobos and the possible differences with its sister species, the chimpanzee (P. troglodytes), is important to properly quantify the normal ranges of dental growth variation in closely related primate species. Understanding this variation among bonobo, chimpanzee and modern human dental development is necessary to better contextualize the life histories of extinct hominins. This study tests whether bonobos and chimpanzees are distinguished from each other by covariance among the relative timing and sequences of tooth crown initiation, mineralization, root extension, and completion. Using multivariate statistical analyses, we compared the relative timing of permanent tooth crypt formation, crown mineralization, and root extension between 34 P. paniscus and 80 P. troglodytes mandibles radiographed in lateral and occlusal views. Covariance among our 12 assigned dental scores failed to statistically distinguish between bonobos and chimpanzees. Rather than clustering by species, individuals clustered by age group (infant, younger or older juvenile, and adult). Dental scores covaried similarly between the incisors, as well as between both premolars. Conversely, covariance among dental scores distinguished the canine and each of the three molars not only from each other, but also from the rest of the anterior teeth. Our study showed no significant differences in the relative timing of permanent tooth crown and root formation between bonobos and chimpanzees. Copyright © 2012 Wiley Periodicals, Inc.

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Applying Sociocultural Theory to Teaching Statistics for Doctoral Social Work Students

ERIC Educational Resources Information Center

Mogro-Wilson, Cristina; Reeves, Michael G.; Charter, Mollie Lazar

2015-01-01

This article describes the development of two doctoral-level multivariate statistics courses utilizing sociocultural theory, an integrative pedagogical framework. In the first course, the implementation of sociocultural theory helps to support the students through a rigorous introduction to statistics. The second course involves students…

A review on the multivariate statistical methods for dimensional reduction studies

NASA Astrophysics Data System (ADS)

Aik, Lim Eng; Kiang, Lam Chee; Mohamed, Zulkifley Bin; Hong, Tan Wei

2017-05-01

In this research study we have discussed multivariate statistical methods for dimensional reduction, which has been done by various researchers. The reduction of dimensionality is valuable to accelerate algorithm progression, as well as really may offer assistance with the last grouping/clustering precision. A lot of boisterous or even flawed info information regularly prompts a not exactly alluring algorithm progression. Expelling un-useful or dis-instructive information segments may for sure help the algorithm discover more broad grouping locales and principles and generally speaking accomplish better exhibitions on new data set.

Generating an Empirical Probability Distribution for the Andrews-Pregibon Statistic.

ERIC Educational Resources Information Center

Jarrell, Michele G.

A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…

Modernization and medicinal plant knowledge in a Caribbean horticultural village.

PubMed

Quinlan, Marsha B; Quinlan, Robert J

2007-06-01

Herbal medicine is the first response to illness in rural Dominica. Every adult knows several "bush" medicines, and knowledge varies from person to person. Anthropological convention suggests that modernization generally weakens traditional knowledge. We examine the effects of commercial occupation, consumerism, education, parenthood, age, and gender on the number of medicinal plants freelisted by individuals. All six predictors are associated with bush medical knowledge in bivariate analyses. Contrary to predictions, commercial occupation and consumerism are positively associated with herbal knowledge. Gender, age, occupation, and education are significant predictors in multivariate analysis. Women tend to recall more plants than do men. Education is negatively associated with plants listed; age positively associates with number of species listed. There are significant interactions among commercial occupation, education, age, and parenthood, suggesting that modernization has complex effects on knowledge of traditional medicine in Dominica.

Northern Russian chironomid-based modern summer temperature data set and inference models

NASA Astrophysics Data System (ADS)

Nazarova, Larisa; Self, Angela E.; Brooks, Stephen J.; van Hardenbroek, Maarten; Herzschuh, Ulrike; Diekmann, Bernhard

2015-11-01

West and East Siberian data sets and 55 new sites were merged based on the high taxonomic similarity, and the strong relationship between mean July air temperature and the distribution of chironomid taxa in both data sets compared with other environmental parameters. Multivariate statistical analysis of chironomid and environmental data from the combined data set consisting of 268 lakes, located in northern Russia, suggests that mean July air temperature explains the greatest amount of variance in chironomid distribution compared with other measured variables (latitude, longitude, altitude, water depth, lake surface area, pH, conductivity, mean January air temperature, mean July air temperature, and continentality). We established two robust inference models to reconstruct mean summer air temperatures from subfossil chironomids based on ecological and geographical approaches. The North Russian 2-component WA-PLS model (RMSEPJack = 1.35 °C, rJack2 = 0.87) can be recommended for application in palaeoclimatic studies in northern Russia. Based on distinctive chironomid fauna and climatic regimes of Kamchatka the Far East 2-component WAPLS model (RMSEPJack = 1.3 °C, rJack2 = 0.81) has potentially better applicability in Kamchatka.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

A Multivariate Solution of the Multivariate Ranking and Selection Problem

DTIC Science & Technology

1980-02-01

Taneja (1972)), a ’a for a vector of constants c (Krishnaiah and Rizvi (1966)), the generalized variance ( Gnanadesikan and Gupta (1970)), iegier (1976...Olk-in, I. and Sobel, M. (1977). Selecting and Ordering Populations: A New Statistical Methodology, John Wiley & Sons, Inc., New York. Gnanadesikan

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

The Impact of Global Warming and Anoxia on Marine Benthic Community Dynamics: an Example from the Toarcian (Early Jurassic)

PubMed Central

Danise, Silvia; Twitchett, Richard J.; Little, Crispin T. S.; Clémence, Marie-Emilie

2013-01-01

The Pliensbachian-Toarcian (Early Jurassic) fossil record is an archive of natural data of benthic community response to global warming and marine long-term hypoxia and anoxia. In the early Toarcian mean temperatures increased by the same order of magnitude as that predicted for the near future; laminated, organic-rich, black shales were deposited in many shallow water epicontinental basins; and a biotic crisis occurred in the marine realm, with the extinction of approximately 5% of families and 26% of genera. High-resolution quantitative abundance data of benthic invertebrates were collected from the Cleveland Basin (North Yorkshire, UK), and analysed with multivariate statistical methods to detect how the fauna responded to environmental changes during the early Toarcian. Twelve biofacies were identified. Their changes through time closely resemble the pattern of faunal degradation and recovery observed in modern habitats affected by anoxia. All four successional stages of community structure recorded in modern studies are recognised in the fossil data (i.e. Stage III: climax; II: transitional; I: pioneer; 0: highly disturbed). Two main faunal turnover events occurred: (i) at the onset of anoxia, with the extinction of most benthic species and the survival of a few adapted to thrive in low-oxygen conditions (Stages I to 0) and (ii) in the recovery, when newly evolved species colonized the re-oxygenated soft sediments and the path of recovery did not retrace of pattern of ecological degradation (Stages I to II). The ordination of samples coupled with sedimentological and palaeotemperature proxy data indicate that the onset of anoxia and the extinction horizon coincide with both a rise in temperature and sea level. Our study of how faunal associations co-vary with long and short term sea level and temperature changes has implications for predicting the long-term effects of “dead zones” in modern oceans. PMID:23457537

The impact of global warming and anoxia on marine benthic community dynamics: an example from the Toarcian (Early Jurassic).

PubMed

Danise, Silvia; Twitchett, Richard J; Little, Crispin T S; Clémence, Marie-Emilie

2013-01-01

The Pliensbachian-Toarcian (Early Jurassic) fossil record is an archive of natural data of benthic community response to global warming and marine long-term hypoxia and anoxia. In the early Toarcian mean temperatures increased by the same order of magnitude as that predicted for the near future; laminated, organic-rich, black shales were deposited in many shallow water epicontinental basins; and a biotic crisis occurred in the marine realm, with the extinction of approximately 5% of families and 26% of genera. High-resolution quantitative abundance data of benthic invertebrates were collected from the Cleveland Basin (North Yorkshire, UK), and analysed with multivariate statistical methods to detect how the fauna responded to environmental changes during the early Toarcian. Twelve biofacies were identified. Their changes through time closely resemble the pattern of faunal degradation and recovery observed in modern habitats affected by anoxia. All four successional stages of community structure recorded in modern studies are recognised in the fossil data (i.e. Stage III: climax; II: transitional; I: pioneer; 0: highly disturbed). Two main faunal turnover events occurred: (i) at the onset of anoxia, with the extinction of most benthic species and the survival of a few adapted to thrive in low-oxygen conditions (Stages I to 0) and (ii) in the recovery, when newly evolved species colonized the re-oxygenated soft sediments and the path of recovery did not retrace of pattern of ecological degradation (Stages I to II). The ordination of samples coupled with sedimentological and palaeotemperature proxy data indicate that the onset of anoxia and the extinction horizon coincide with both a rise in temperature and sea level. Our study of how faunal associations co-vary with long and short term sea level and temperature changes has implications for predicting the long-term effects of "dead zones" in modern oceans.

Pollen-based temperature and precipitation inferences for the montane forest of Mt. Kilimanjaro during the last Glacial and the Holocene

NASA Astrophysics Data System (ADS)

Schüler, L.; Hemp, A.; Behling, H.

2014-01-01

The relationship between modern pollen-rain taxa and measured climate variables was explored along the elevational gradient of the southern slope of Mt. Kilimanjaro, Tanzania. Pollen assemblages in 28 pollen traps positioned on 14 montane forest vegetation plots were identified and their relationship with climate variables was examined using multivariate statistical methods. Canonical correspondence analysis revealed that the mean annual temperature, mean annual precipitation and minimum temperature each account for significant fractions of the variation in pollen taxa. A training set of 107 modern pollen taxa was used to derive temperature and precipitation transfer functions based on pollen subsets using weighted-averaging-partial-least-squares (WA-PLS) techniques. The transfer functions were then applied to a fossil pollen record from the montane forest of Mt. Kilimanjaro and the climate parameter estimates for the Late Glacial and the Holocene on Mt. Kilimanjaro were inferred. Our results present the first quantitatively reconstructed temperature and precipitation estimates for Mt Kilimanjaro and give highly interesting insights into the past 45 000 yr of climate dynamics in tropical East Africa. The climate reconstructions are consistent with the interpretation of pollen data in terms of vegetation and climate history of afro-montane forest in East Africa. Minimum temperatures above the frostline as well as increased precipitation turn out to be crucial for the development and expansion of montane forest during the Holocene. In contrast, consistently low minimum temperatures as well as about 25% drier climate conditions prevailed during the pre LGM, which kept the montane vegetation composition in a stable state. In prospective studies, the quantitative climate reconstruction will be improved by additional modern pollen rain data, especially from lower elevations with submontane dry forests and colline savanna vegetation in order to extend the reference climate gradient.

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

PubMed

Del Giudice, G; Padulano, R; Siciliano, D

2016-01-01

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from homogeneous groundwaters in the Great Artesian Basin, Australia

NASA Astrophysics Data System (ADS)

O'Shea, Bethany; Jankowski, Jerzy

2006-12-01

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO2 input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright

Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.

PubMed

Harrington, Peter de Boves

2018-01-02

Validation of multivariate models is of current importance for a wide range of chemical applications. Although important, it is neglected. The common practice is to use a single external validation set for evaluation. This approach is deficient and may mislead investigators with results that are specific to the single validation set of data. In addition, no statistics are available regarding the precision of a derived figure of merit (FOM). A statistical approach using bootstrapped Latin partitions is advocated. This validation method makes an efficient use of the data because each object is used once for validation. It was reviewed a decade earlier but primarily for the optimization of chemometric models this review presents the reasons it should be used for generalized statistical validation. Average FOMs with confidence intervals are reported and powerful, matched-sample statistics may be applied for comparing models and methods. Examples demonstrate the problems with single validation sets.

[Applications of the hospital statistics management system].

PubMed

Zhai, Hong; Ren, Yong; Liu, Jing; Li, You-Zhang; Ma, Xiao-Long; Jiao, Tao-Tao

2008-01-01

The Hospital Statistics Management System is built on an Office Automation Platform of Shandong provincial hospital system. Its workflow, role and popedom technologies are used to standardize and optimize the management program of statistics in the total quality control of hospital statistics. The system's applications have combined the office automation platform with the statistics management in a hospital and this provides a practical example of a modern hospital statistics management model.

Write-Skewed: Writing in an Introductory Statistics Course

ERIC Educational Resources Information Center

Delcham, Hendrick; Sezer, Renan

2010-01-01

Statistics is used in almost every facet of our daily lives: crime reports, election results, environmental/climate change, advances in business, financial planning, and progress in multifarious research. Although understanding statistics is essential for efficient functioning in the modern world (Cerrito 1996), students often do not grasp…

[Current status and trends in the health of the Moscow population].

PubMed

Tishuk, E A; Plavunov, N F; Soboleva, N P

1997-01-01

Based on vast comprehensive medical statistical database, the authors analyze the health status of the population and the efficacy of public health service in Moscow. The pre-crisis tendencies and the modern status of public health under modern socioeconomic conditions are noted.

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

A Descriptive Study of Individual and Cross-Cultural Differences in Statistics Anxiety

ERIC Educational Resources Information Center

Baloglu, Mustafa; Deniz, M. Engin; Kesici, Sahin

2011-01-01

The present study investigated individual and cross-cultural differences in statistics anxiety among 223 Turkish and 237 American college students. A 2 x 2 between-subjects factorial multivariate analysis of covariance (MANCOVA) was performed on the six dependent variables which are the six subscales of the Statistical Anxiety Rating Scale.…

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Robust Statistics: What They Are, and Why They Are So Important

ERIC Educational Resources Information Center

Corlu, Sencer M.

2009-01-01

The problem with "classical" statistics all invoking the mean is that these estimates are notoriously influenced by atypical scores (outliers), partly because the mean itself is differentially influenced by outliers. In theory, "modern" statistics may generate more replicable characterizations of data, because at least in some…

Statistical Diversions

ERIC Educational Resources Information Center

Petocz, Peter; Sowey, Eric

2008-01-01

In this article, the authors focus on hypothesis testing--that peculiarly statistical way of deciding things. Statistical methods for testing hypotheses were developed in the 1920s and 1930s by some of the most famous statisticians, in particular Ronald Fisher, Jerzy Neyman and Egon Pearson, who laid the foundations of almost all modern methods of…

Facilitating the Transition from Bright to Dim Environments

DTIC Science & Technology

2016-03-04

For the parametric data, a multivariate ANOVA was used in determining the systematic presence of any statistically significant performance differences...performed. All significance levels were p < 0.05, and statistical analyses were performed with the Statistical Package for Social Sciences ( SPSS ...1950. Age changes in rate and level of visual dark adaptation. Journal of Applied Physiology, 2, 407–411. Field, A. 2009. Discovering statistics

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design

PubMed Central

Lu, Tsui-Shan; Longnecker, Matthew P.; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data and the general ODS design for a continuous response. While substantial work has been done for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome dependent sampling (Multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the Multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the Multivariate-ODS or the estimator from a simple random sample with the same sample size. The Multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of PCB exposure to hearing loss in children born to the Collaborative Perinatal Study. PMID:27966260

Socio-Economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model

ERIC Educational Resources Information Center

Mittal, Surabhi; Mehar, Mamta

2016-01-01

Purpose: The paper analyzes factors that affect the likelihood of adoption of different agriculture-related information sources by farmers. Design/Methodology/Approach: The paper links the theoretical understanding of the existing multiple sources of information that farmers use, with the empirical model to analyze the factors that affect the…

A Multivariate Examination of Explanations for the Occurrence of Elder Abuse

ERIC Educational Resources Information Center

Litwin, Howard; Zoabi, Sameer

2004-01-01

The goal of the study was to determine the relative strength of four major explanations for the rise of elder abuse among a population in transition from traditional to modern culture. The study compared a sample of 120 abused elderly Arab Israelis with a control group of 120 nonabused older adults from the same background. The abuse status…

An application of modern control theory to jet propulsion systems. [considering onboard computer

NASA Technical Reports Server (NTRS)

Merrill, W. C.

1975-01-01

The control of an airbreathing turbojet engine by an onboard digital computer is studied. The approach taken is to model the turbojet engine as a linear, multivariable system whose parameters vary with engine operating environment. From this model adaptive closed-loop or feedback control laws are designed and applied to the acceleration of the turbojet engine.

Wind Tunnel Database Development using Modern Experiment Design and Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2003-01-01

A wind tunnel experiment for characterizing the aerodynamic and propulsion forces and moments acting on a research model airplane is described. The model airplane called the Free-flying Airplane for Sub-scale Experimental Research (FASER), is a modified off-the-shelf radio-controlled model airplane, with 7 ft wingspan, a tractor propeller driven by an electric motor, and aerobatic capability. FASER was tested in the NASA Langley 12-foot Low-Speed Wind Tunnel, using a combination of traditional sweeps and modern experiment design. Power level was included as an independent variable in the wind tunnel test, to allow characterization of power effects on aerodynamic forces and moments. A modeling technique that employs multivariate orthogonal functions was used to develop accurate analytic models for the aerodynamic and propulsion force and moment coefficient dependencies from the wind tunnel data. Efficient methods for generating orthogonal modeling functions, expanding the orthogonal modeling functions in terms of ordinary polynomial functions, and analytical orthogonal blocking were developed and discussed. The resulting models comprise a set of smooth, differentiable functions for the non-dimensional aerodynamic force and moment coefficients in terms of ordinary polynomials in the independent variables, suitable for nonlinear aircraft simulation.

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

Differences in the bioenergetic potential of athletes participating in team sports.

PubMed

Malacko, Julijan; Doder, Dragan; Djurdjević, Slavisa; Savić, Biljana; Doder, Radoslava

2013-07-01

In modern training technology, assessment of aerobic bioenergetic potential in athletes is commonly performed by standard laboratory procedures to determine basic or specific functional abilities for specific sport activity or discipline. The aim of study was to assess the aerobic bioenergetic potential of athletes participating in basketball, football and handball. The study included 87 athletes (29 basketball players, 29 football players, and 29 handball players) aged 21-24. Evaluation of the aerobic bioenergetic potential of athletes participating in basketball, football and handball was performed followed by both univariate (ANOVA) and multivariate (MANOVA) statistical methods to determine differences among the athletes in relative (VO2 mL/kg/min) and absolute oxygen consumption (VO2 L/min). Statistically significant differences between absolute and relative oxygen consumption were found in basketball players (Mb), football players (Mf), and handball players (Mh) (MANOVA, p = 0.00). ANOVA also revealed significant differences in relative oxygen consumption (VO2 mL/kg/min) (p = 0.00). The football players (55.32 mL/kg/min) had the highest relative oxygen consumption, followed by the handball players (51.84 mL/kg/min) and basketball players (47.00 mL/kg/min). The highest absolute oxygen consumption was recorded in the basketball players (4.47 L/min), followed by the handball players (4.40 L/min) and footballers (4.16 L/min). Statistically significant differences in the aerobic bioenergetic potential, expressed by the relative oxygen consumption were found among atletes participating in different team sports. It can be assumed that the player from the sports in which it is necessary to cross greater distance in total during the match have a greater need for aerobic capacity.

NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

PubMed Central

He, Fanyin; Mazumdar, Sati; Tang, Gong; Bhatia, Triptish; Anderson, Stewart J.; Dew, Mary Amanda; Krafty, Robert; Nimgaonkar, Vishwajit; Deshpande, Smita; Hall, Martica; Reynolds, Charles F.

2017-01-01

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the nonparametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially-observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete-case analyses. PMID:29416225

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Attitudes toward Advanced and Multivariate Statistics When Using Computers.

ERIC Educational Resources Information Center

Kennedy, Robert L.; McCallister, Corliss Jean

This study investigated the attitudes toward statistics of graduate students who studied advanced statistics in a course in which the focus of instruction was the use of a computer program in class. The use of the program made it possible to provide an individualized, self-paced, student-centered, and activity-based course. The three sections…

Statistics Anxiety and Worry: The Roles of Worry Beliefs, Negative Problem Orientation, and Cognitive Avoidance

ERIC Educational Resources Information Center

Williams, Amanda S.

2015-01-01

Statistics anxiety is a common problem for graduate students. This study explores the multivariate relationship between a set of worry-related variables and six types of statistics anxiety. Canonical correlation analysis indicates a significant relationship between the two sets of variables. Findings suggest that students who are more intolerant…

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Interfaces between statistical analysis packages and the ESRI geographic information system

NASA Technical Reports Server (NTRS)

Masuoka, E.

1980-01-01

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

PubMed

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Alejandro Q; Musolf, Anthony; Matise, Tara C; Finch, Stephen J; Gordon, Derek

2012-01-01

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci. Copyright © 2013 S. Karger AG, Basel.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

PubMed Central

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Andrew; Musolf, Anthony; Matise, Tara C.; Finch, Stephen J.; Gordon, Derek

2013-01-01

As with any new technology, next generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model, based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to that data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p-value, no matter how many loci. PMID:23594495

Statistical Research of Investment Development of Russian Regions

ERIC Educational Resources Information Center

Burtseva, Tatiana A.; Aleshnikova, Vera I.; Dubovik, Mayya V.; Naidenkova, Ksenya V.; Kovalchuk, Nadezda B.; Repetskaya, Natalia V.; Kuzmina, Oksana G.; Surkov, Anton A.; Bershadskaya, Olga I.; Smirennikova, Anna V.

2016-01-01

This article the article is concerned with a substantiation of procedures ensuring the implementation of statistical research and monitoring of investment development of the Russian regions, which would be pertinent for modern development of the state statistics. The aim of the study is to develop the methodological framework in order to estimate…

Teaching Engineering Statistics with Technology, Group Learning, Contextual Projects, Simulation Models and Student Presentations

ERIC Educational Resources Information Center

Romeu, Jorge Luis

2008-01-01

This article discusses our teaching approach in graduate level Engineering Statistics. It is based on the use of modern technology, learning groups, contextual projects, simulation models, and statistical and simulation software to entice student motivation. The use of technology to facilitate group projects and presentations, and to generate,…

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Departure from Normality in Multivariate Normative Comparison: The Cramer Alternative for Hotelling's "T[squared]"

ERIC Educational Resources Information Center

Grasman, Raoul P. P. P.; Huizenga, Hilde M.; Geurts, Hilde M.

2010-01-01

Crawford and Howell (1998) have pointed out that the common practice of z-score inference on cognitive disability is inappropriate if a patient's performance on a task is compared with relatively few typical control individuals. Appropriate univariate and multivariate statistical tests have been proposed for these studies, but these are only valid…

Determinants of Modern Contraceptive Uptake among Nigerian Women: Evidence from the National Demographic and Health Survey.

PubMed

Johnson, Ofonime E

2017-09-01

Family planning is a key strategy in the control of fertility among women. This study sought to determine various factors that influence modern family planning use in Nigeria. The study used data from Nigeria Demographic and Health Survey (NDHS) 2013. Analysis was carried out using Stata version 12.1. Multivariate logistic regression was used to determine association between various factors and use of modern family planning methods. A total of 119,386 women aged 15-49 years participated in the study. The mean age of respondents was 35.9 ± 8.1 years. Overall, contraceptive prevalence rate of modern methods was 10.3%. The highest proportion of use was 26.7% in the South West, while the lowest was 2.7% in the North West. Predictors of modern family planning use were higher education (OR=4.49, 95% CI: 4.10-4.92), richest wealth quintile (OR=3.71 CI: 3.29-4.19), being from South West (OR=3.42, 95% CI: 3.15-3.70), age 25-49 years (OR=1.55, 95% CI: 1.42-1.69) and urban residence (OR=1.08, 95% CI: 1.03-1.13) (p <0.001). The highest predictors of modern contraceptive use were education and wealth index. These indices were poorest in North Western Nigeria. Measures should be taken to improve female literacy and employment as these will most likely improve uptake of modern contraceptives among women in Nigeria.

Applied statistics in agricultural, biological, and environmental sciences.

USDA-ARS?s Scientific Manuscript database

Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or s...

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

2016-04-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

Geographical variation and factors influencing modern contraceptive use among married women in Ethiopia: evidence from a national population based survey.

PubMed

Lakew, Yihunie; Reda, Ayalu A; Tamene, Habtamu; Benedict, Susan; Deribe, Kebede

2013-09-26

Modern contraceptive use persists to be low in most African countries where fertility, population growth, and unmet need for family planning are high. Though there is an evidence of increased overall contraceptive prevalence, a substantial effort remains behind in Ethiopia. This study aimed to identify factors associated with modern contraceptive use and to examine its geographical variations among 15-49 married women in Ethiopia. We conducted secondary analysis of 10,204 reproductive age women included in the 2011 Ethiopia Demographic and Health Survey (DHS). The survey sample was designed to provide national, urban/rural, and regional representative estimates for key health and demographic indicators. The sample was selected using a two-stage stratified sampling process. Bivariate and multivariate logistic regressions were applied to determine the prevalence of modern contraceptive use and associated factors in Ethiopia. Being wealthy, more educated, being employed, higher number of living children, being in a monogamous relationship, attending community conversation, being visited by health worker at home strongly predicted use of modern contraception. While living in rural areas, older age, being in polygamous relationship, and witnessing one's own child's death were found negatively influence modern contraceptive use. The spatial analysis of contraceptive use revealed that the central and southwestern parts of the country had higher prevalence of modern contraceptive use than that of the eastern and western parts. The findings indicate significant socio-economic, urban-rural and regional variation in modern contraceptive use among reproductive age women in Ethiopia. Strengthening community conversation programs and female education should be given top priority.

Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

PubMed

Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

2010-10-01

We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

Multivariate model of female black bear habitat use for a Geographic Information System

USGS Publications Warehouse

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

1993-01-01

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

NASA Astrophysics Data System (ADS)

Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

2017-10-01

The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

Teaching Business Statistics with Real Data to Undergraduates and the Use of Technology in the Class Room

ERIC Educational Resources Information Center

Singamsetti, Rao

2007-01-01

In this paper an attempt is made to highlight some issues of interpretation of statistical concepts and interpretation of results as taught in undergraduate Business statistics courses. The use of modern technology in the class room is shown to have increased the efficiency and the ease of learning and teaching in statistics. The importance of…

Modern Empirical Statistical Spectral Analysis.

DTIC Science & Technology

1980-05-01

716-723. Akaike, H. (1977). On entropy maximization principle, Applications of Statistics, P.R. Krishnaiah , ed., North-Holland, Amsterdam, 27-41...by P. Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, E. (1979). Forecasting and whitening filter estimation, TIMS Studies in the Management

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

PubMed

Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P; Gonzalez-Zacarias, Clio; Zhao, Lu; D'Arcy, Mike; Kesselman, Carl; Herting, Megan M; Dinov, Ivo D; Toga, Arthur W; Clark, Kristi A

2018-05-15

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases). Copyright © 2018 Elsevier Inc. All rights reserved.

Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

Tsvetkov, Ch; Gorchev, G; Tomov, S; Nikolova, M; Genchev, G

2016-01-01

The aim of the research was to evaluate and analyse prognosis and prognostic factors in patients with squamous cell vulvar carcinoma after primary surgery with individual approach applied during the course of treatment. In the period between January 2000 and July 2010, 113 patients with squamous cell carcinoma of the vulva were diagnosed and operated on at Gynecologic Oncology Clinic of Medical University, Pleven. All the patients were monitored at the same clinic. Individual approach was applied to each patient and whenever it was possible, more conservative operative techniques were applied. The probable clinicopathological characteristics influencing the overall survival and recurrence free survival were analyzed. Univariate statistical analysis and Cox regression analysis were made in order to evaluate the characteristics, which were statistically significant for overall survival and survival without recurrence. A multivariate logistic regression analysis (Forward Wald procedure) was applied to evaluate the combined influence of the significant factors. While performing the multivariate analysis, the synergic effect of the independent prognostic factors of both kinds of survivals was also evaluated. Approaching individually each patient, we applied the following operative techniques: 1. Deep total radical vulvectomy with separate incisions for lymph dissection (LD) or without dissection--68 (60.18 %) patients. 2. En-bloc vulvectomy with bilateral LD without vulva reconstruction--10 (8.85%) 3. Modified radical vulvactomy (hemivulvectomy, patial vulvactomy)--25 (22.02%). 4. wide-local excision--3 (2.65%). 5. Simple (total /partial) vulvectomy--5 (4.43%) patients. 6. En-bloc resection with reconstruction--2 (1.77%) After a thorough analysis of the overall survival and recurrence free survival, we made the conclusion that the relapse occurrence and clinical stage of FIGO were independent prognostic factors for overall survival and the independent prognostic factors for recurrence free survival were: metastatic inguinal nodes (unilateral or bilateral), tumor size (above or below 3 cm) and lymphovascular space invasion. On the basis of these results we created two prognostic models: 1. A prognostic model of overall survival 2. A prognostic model for survival without recurrence. Following the surgical staging of the disease, were able to gather and analyse important clinicopathological indexes, which gave us the opportunity to form prognostic groups for overall survival and recurrence-free survival.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

ERIC Educational Resources Information Center

Yuan, Ke-Hai

2008-01-01

In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

Some Tests of Randomness with Applications

DTIC Science & Technology

1981-02-01

freedom. For further details, the reader is referred to Gnanadesikan (1977, p. 169) wherein other relevant tests are also given, Graphical tests, as...sample from a gamma distri- bution. J. Am. Statist. Assoc. 71, 480-7. Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

Statistical polarization in greenhouse gas emissions: Theory and evidence.

PubMed

Remuzgo, Lorena; Trueba, Carmen

2017-11-01

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990-2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design.

PubMed

Lu, Tsui-Shan; Longnecker, Matthew P; Zhou, Haibo

2017-03-15

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data, and the general ODS design for a continuous response. While substantial work has been carried out for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome-dependent sampling (multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the multivariate-ODS or the estimator from a simple random sample with the same sample size. The multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of polychlorinated biphenyl exposure to hearing loss in children born to the Collaborative Perinatal Study. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © 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.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.

Critical Views of 8th Grade Students toward Statistical Data in Newspaper Articles: Analysis in Light of Statistical Literacy

ERIC Educational Resources Information Center

Guler, Mustafa; Gursoy, Kadir; Guven, Bulent

2016-01-01

Understanding and interpreting biased data, decision-making in accordance with the data, and critically evaluating situations involving data are among the fundamental skills necessary in the modern world. To develop these required skills, emphasis on statistical literacy in school mathematics has been gradually increased in recent years. The…

Multivariable polynomial fitting of controlled single-phase nonlinear load of input current total harmonic distortion

NASA Astrophysics Data System (ADS)

Sikora, Roman; Markiewicz, Przemysław; Pabjańczyk, Wiesława

2018-04-01

The power systems usually include a number of nonlinear receivers. Nonlinear receivers are the source of disturbances generated to the power system in the form of higher harmonics. The level of these disturbances describes the total harmonic distortion coefficient THD. Its value depends on many factors. One of them are the deformation and change in RMS value of supply voltage. A modern LED luminaire is a nonlinear receiver as well. The paper presents the results of the analysis of the influence of change in RMS value of supply voltage and the level of dimming of the tested luminaire on the value of the current THD. The analysis was made using a mathematical model based on multivariable polynomial fitting.

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Identification of Differential Item Functioning in Multiple-Group Settings: A Multivariate Outlier Detection Approach

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

We focus on the identification of differential item functioning (DIF) when more than two groups of examinees are considered. We propose to consider items as elements of a multivariate space, where DIF items are outlying elements. Following this approach, the situation of multiple groups is a quite natural case. A robust statistics technique is…

Uses of Multivariate Analytical Techniques in Online and Blended Business Education: An Assessment of Current Practice and Recommendations for Future Research

ERIC Educational Resources Information Center

Arbaugh, J. B.; Hwang, Alvin

2013-01-01

Seeking to assess the analytical rigor of empirical research in management education, this article reviews the use of multivariate statistical techniques in 85 studies of online and blended management education over the past decade and compares them with prescriptions offered by both the organization studies and educational research communities.…

On Some Multiple Decision Problems

DTIC Science & Technology

1976-08-01

parameter space. Some recent results in the area of subset selection formulation are Gnanadesikan and Gupta [28], Gupta and Studden [43], Gupta and...York, pp. 363-376. [27) Gnanadesikan , M. (1966). Some Selection and Ranking Procedures for Multivariate Normal Populations. Ph.D. Thesis. Dept. of...Statist., Purdue Univ., West Lafayette, Indiana 47907. [28) Gnanadesikan , M. and Gupta, S. S. (1970). Selection procedures for multivariate normal

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

Yang, James J; Williams, L Keoki; Buu, Anne

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

Preisser, John S.; Sen, Pranab K.; Offenbacher, Steven

2011-01-01

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined. PMID:21984957

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet.

PubMed

Potyrailo, Radislav A

2016-10-12

Modern gas monitoring scenarios for medical diagnostics, environmental surveillance, industrial safety, and other applications demand new sensing capabilities. This Review provides analysis of development of new generation of gas sensors based on the multivariable response principles. Design criteria of these individual sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with independent outputs to recognize these different gas responses. These new sensors quantify individual components in mixtures, reject interferences, and offer more stable response over sensor arrays. Such performance is attractive when selectivity advantages of classic gas chromatography, ion mobility, and mass spectrometry instruments are canceled by requirements for no consumables, low power, low cost, and unobtrusive form factors for Internet of Things, Industrial Internet, and other applications. This Review is concluded with a perspective for future needs in fundamental and applied aspects of gas sensing and with the 2025 roadmap for ubiquitous gas monitoring.

Detecting taxonomic and phylogenetic signals in equid cheek teeth: towards new palaeontological and archaeological proxies

NASA Astrophysics Data System (ADS)

Cucchi, T.; Mohaseb, A.; Peigné, S.; Debue, K.; Orlando, L.; Mashkour, M.

2017-04-01

The Plio-Pleistocene evolution of Equus and the subsequent domestication of horses and donkeys remains poorly understood, due to the lack of phenotypic markers capable of tracing this evolutionary process in the palaeontological/archaeological record. Using images from 345 specimens, encompassing 15 extant taxa of equids, we quantified the occlusal enamel folding pattern in four mandibular cheek teeth with a single geometric morphometric protocol. We initially investigated the protocol accuracy by assigning each tooth to its correct anatomical position and taxonomic group. We then contrasted the phylogenetic signal present in each tooth shape with an exome-wide phylogeny from 10 extant equine species. We estimated the strength of the phylogenetic signal using a Brownian motion model of evolution with multivariate K statistic, and mapped the dental shape along the molecular phylogeny using an approach based on squared-change parsimony. We found clear evidence for the relevance of dental phenotypes to accurately discriminate all modern members of the genus Equus and capture their phylogenetic relationships. These results are valuable for both palaeontologists and zooarchaeologists exploring the spatial and temporal dynamics of the evolutionary history of the horse family, up to the latest domestication trajectories of horses and donkeys.

Detecting taxonomic and phylogenetic signals in equid cheek teeth: towards new palaeontological and archaeological proxies

PubMed Central

Mohaseb, A.; Peigné, S.; Debue, K.; Orlando, L.; Mashkour, M.

2017-01-01

The Plio–Pleistocene evolution of Equus and the subsequent domestication of horses and donkeys remains poorly understood, due to the lack of phenotypic markers capable of tracing this evolutionary process in the palaeontological/archaeological record. Using images from 345 specimens, encompassing 15 extant taxa of equids, we quantified the occlusal enamel folding pattern in four mandibular cheek teeth with a single geometric morphometric protocol. We initially investigated the protocol accuracy by assigning each tooth to its correct anatomical position and taxonomic group. We then contrasted the phylogenetic signal present in each tooth shape with an exome-wide phylogeny from 10 extant equine species. We estimated the strength of the phylogenetic signal using a Brownian motion model of evolution with multivariate K statistic, and mapped the dental shape along the molecular phylogeny using an approach based on squared-change parsimony. We found clear evidence for the relevance of dental phenotypes to accurately discriminate all modern members of the genus Equus and capture their phylogenetic relationships. These results are valuable for both palaeontologists and zooarchaeologists exploring the spatial and temporal dynamics of the evolutionary history of the horse family, up to the latest domestication trajectories of horses and donkeys. PMID:28484618

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Hypothesis Testing, "p" Values, Confidence Intervals, Measures of Effect Size, and Bayesian Methods in Light of Modern Robust Techniques

ERIC Educational Resources Information Center

Wilcox, Rand R.; Serang, Sarfaraz

2017-01-01

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and "p" values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have…

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

NASA Astrophysics Data System (ADS)

Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

2018-03-01

The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

Increasing family planning in Myanmar: the role of the private sector and social franchise programs.

PubMed

Aung, Tin; Hom, Nang Mo; Sudhinaraset, May

2017-07-01

This study examines the influence of clinical social franchise program on modern contraceptive use. This was a cross-sectional survey of contraceptive use among 2390 currently married women across 25 townships in Myanmar in 2014. Social franchise program measures were from programmatic records. Multivariable models show that women who lived in communities with at least 1-5 years of a clinical social franchise intrauterine device (IUD) program had 4.770 higher odds of using a modern contraceptive method compared to women living in communities with no IUD program [CI: 3.739-6.084]. Townships where the reproductive health program had existed for at least 10 years had 1.428 higher odds of reporting modern method use compared to women living in townships where the programs had existed for less than 10 years [CI: 1.016-2.008]. This study found consistent and robust evidence for an increase in family planning methods over program duration as well as intensity of social franchise programs.

Comparative Research of Navy Voluntary Education at Operational Commands

DTIC Science & Technology

2017-03-01

return on investment, ROI, logistic regression, multivariate analysis, descriptive statistics, Markov, time-series, linear programming 15. NUMBER...21  B.  DESCRIPTIVE STATISTICS TABLES ...............................................25  C.  PRIVACY CONSIDERATIONS...THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1.  Variables and Descriptions . Adapted from NETC (2016). .......................21

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

Establishing Benchmarks for Outcome Indicators: A Statistical Approach to Developing Performance Standards.

ERIC Educational Resources Information Center

Henry, Gary T.; And Others

1992-01-01

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Challenging Conventional Wisdom for Multivariate Statistical Models with Small Samples

ERIC Educational Resources Information Center

McNeish, Daniel

2017-01-01

In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…

Belief in Family Planning Myths at the Individual and Community Levels and Modern Contraceptive Use in Urban Africa.

PubMed

Gueye, Abdou; Speizer, Ilene S; Corroon, Meghan; Okigbo, Chinelo C

2015-12-01

Negative myths and misconceptions about family planning are a barrier to modern contraceptive use. Most research on the subject has focused on individual beliefs about contraception; however, given that myths spread easily within communities, it is also important to examine how the prevalence of negative myths in a community affects the aggregate level of method use. Baseline data collected in 2010-2011 by the Measurement, Learning & Evaluation project on women aged 15-49 living in selected cities in Kenya, Nigeria and Senegal were used. Multivariate analyses examined associations between modern contraceptive use and belief in negative myths for individuals and communities. In each country, the family planning myths most prevalent at the individual and community levels were that "people who use contraceptives end up with health problems," "contraceptives are dangerous to women's health" and "contraceptives can harm your womb." On average, women in Nigeria and Kenya believed 2.7 and 4.6 out of eight selected myths, respectively, and women in Senegal believed 2.6 out of seven. Women's individual-level belief in myths was negatively associated with their modern contraceptive use in all three countries (odds ratios, 0.2-0.7). In Nigeria, the women's community-level myth variable was positively associated with modern contraceptive use (1.6), whereas the men's community-level myth variable was negatively associated with use (0.6); neither community-level variable was associated with modern contraceptive use in Kenya or Senegal. Education programs are needed to dispel common myths and misconceptions about modern contraceptives. In Nigeria, programs that encourage community-level discussions may be effective at reducing myths and increasing modern contraceptive use.

Relationships between antenatal and postnatal care and post-partum modern contraceptive use: evidence from population surveys in Kenya and Zambia.

PubMed

Do, Mai; Hotchkiss, David

2013-01-04

It is often assumed, with little supportive, empirical evidence, that women who use maternal health care are more likely than those who do not to use modern contraceptives. This study aims to add to the existing literature on associations between the use of antenatal (ANC) and post-natal care (PNC) and post-partum modern contraceptives. Data come from the most recent Demographic and Health Surveys (DHS) in Kenya (2008-09) and Zambia (2007). Study samples include women who had a live birth within five years before the survey (3,667 in Kenya and 3,587 in Zambia). Multivariate proportional hazard models were used to examine the associations between the intensity of ANC and PNC service use and a woman's adoption of modern contraceptives after a recent live birth. Tests of exogeneity confirmed that the intensity of ANC and PNC service use and post-partum modern contraceptive practice were not influenced by common unobserved factors. Cox proportional hazard models showed significant associations between the service intensity of ANC and PNC and post-partum modern contraceptive use in both countries. This relationship is largely due to ANC services; no significant associations were observed between PNC service intensity and post-partum FP practice. While the lack of associations between PNC and post-partum FP use may be due to the limited measure of PNC service intensity, the study highlights a window of opportunity to promote the use of modern contraceptives after childbirth through ANC service delivery. Depending on the availability of data, further research should take into account community- and facility-level factors that may influence modern contraceptive use in examining associations between ANC and PNC use and post-partum FP practice.

Geographical variation and factors influencing modern contraceptive use among married women in Ethiopia: evidence from a national population based survey

PubMed Central

2013-01-01

Background Modern contraceptive use persists to be low in most African countries where fertility, population growth, and unmet need for family planning are high. Though there is an evidence of increased overall contraceptive prevalence, a substantial effort remains behind in Ethiopia. This study aimed to identify factors associated with modern contraceptive use and to examine its geographical variations among 15–49 married women in Ethiopia. Methods We conducted secondary analysis of 10,204 reproductive age women included in the 2011 Ethiopia Demographic and Health Survey (DHS). The survey sample was designed to provide national, urban/rural, and regional representative estimates for key health and demographic indicators. The sample was selected using a two-stage stratified sampling process. Bivariate and multivariate logistic regressions were applied to determine the prevalence of modern contraceptive use and associated factors in Ethiopia. Results Being wealthy, more educated, being employed, higher number of living children, being in a monogamous relationship, attending community conversation, being visited by health worker at home strongly predicted use of modern contraception. While living in rural areas, older age, being in polygamous relationship, and witnessing one’s own child’s death were found negatively influence modern contraceptive use. The spatial analysis of contraceptive use revealed that the central and southwestern parts of the country had higher prevalence of modern contraceptive use than that of the eastern and western parts. Conclusion The findings indicate significant socio-economic, urban–rural and regional variation in modern contraceptive use among reproductive age women in Ethiopia. Strengthening community conversation programs and female education should be given top priority. PMID:24067083

Belief in Family Planning Myths at the Individual And Community Levels and Modern Contraceptive Use in Urban Africa

PubMed Central

Gueye, Abdou; Speizer, Ilene S.; Corroon, Meghan; Okigbo, Chinelo C.

2016-01-01

Context Negative myths and misconceptions about family planning are a barrier to modern contraceptive use. Most research on the subject has focused on individual beliefs about contraception; however, given that myths spread easily within communities, it is also important to examine how the prevalence of negative myths in a community affects the aggregate level of method use. Methods Baseline data collected in 2010–2011 by the Measurement, Learning & Evaluation project on women aged 15–49 living in selected cities in Kenya, Nigeria and Senegal were used. Multivariate analyses examined associations between modern contraceptive use and belief in negative myths for individuals and communities. Results In each country, the family planning myths most prevalent at the individual and community levels were that “people who use contraceptives end up with health problems,” “contraceptives are dangerous to women's health” and “contraceptives can harm your womb.” On average, women in Nigeria and Kenya believed 2.7 and 4.6 out of eight selected myths, respectively, and women in Senegal believed 2.6 out of seven. Women's individual-level belief in myths was negatively associated with their modern contraceptive use in all three countries (odds ratios, 0.2–0.7). In Nigeria, the women's community-level myth variable was positively associated with modern contraceptive use (1.6), whereas the men's community-level myth variable was negatively associated with use (0.6); neither community-level variable was associated with modern contraceptive use in Kenya or Senegal. Conclusion Education programs are needed to dispel common myths and misconceptions about modern contraceptives. In Nigeria, programs that encourage community-level discussions may be effective at reducing myths and increasing modern contraceptive use. PMID:26871727

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential for astronomical discovery is equally large, and so the data-oriented research methods, algorithms, and techniques that are presented here will enable the greatest discovery potential from the ever-growing data and information resources in astronomy.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

[Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

PubMed

Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

2017-10-01

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Improving Quality in Teaching Statistics Concepts Using Modern Visualization: The Design and Use of the Flash Application on Pocket PCs

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Pei-Yu

2009-01-01

The emergence of technology has led to numerous changes in mathematical and statistical teaching and learning which has improved the quality of instruction and teacher/student interactions. The teaching of statistics, for example, has shifted from mathematical calculations to higher level cognitive abilities such as reasoning, interpretation, and…

Measuring an Effect Size from Dichotomized Data: Contrasted Results Whether Using a Correlation or an Odds Ratio

ERIC Educational Resources Information Center

Rousson, Valentin

2014-01-01

It is well known that dichotomizing continuous data has the effect to decrease statistical power when the goal is to test for a statistical association between two variables. Modern researchers however are focusing not only on statistical significance but also on an estimation of the "effect size" (i.e., the strength of association…

Trends and patterns of modern contraceptive use and relationships with high-risk births and child mortality in Burkina Faso.

PubMed

Maïga, Abdoulaye; Hounton, Sennen; Amouzou, Agbessi; Akinyemi, Akanni; Shiferaw, Solomon; Baya, Banza; Bahan, Dalomi; Barros, Aluisio J D; Walker, Neff; Friedman, Howard

2015-01-01

In sub-Saharan Africa, few studies have stressed the importance of spatial heterogeneity analysis in modern contraceptive use and the relationships with high-risk births. This paper aims to analyse the association between modern contraceptive use, distribution of birth risk, and under-five child mortality at both national and regional levels in Burkina Faso. The last three Demographic and Health Surveys - conducted in Burkina Faso in 1998, 2003, and 2010 - enabled descriptions of differentials, trends, and associations between modern contraceptive use, total fertility rates (TFR), and factors associated with high-risk births and under-five child mortality. Multivariate models, adjusted by covariates of cultural and socio-economic background and contact with health system, were used to investigate the relationship between birth risk factors and modern contraceptive prevalence rates (mCPR). Overall, Burkina Faso's modern contraception level remains low (15.4% in 2010), despite significant increases during the last decade. However, there are substantial variations in mCPR by region, and health facility contact was positively associated with mCPR increase. Women's fertility history and cultural and socio-economic background were also significant factors in predicting use of modern contraception. Low modern contraceptive use is associated with higher birth risks and increased child mortality. This association is stronger in the Sahel, Est, and Sud-Ouest regions. Even though all factors in high-risk births were associated with under-five mortality, it should be stressed that short birth spacing ranked as the highest risk in relation to mortality of children. Programmes that target sub-national differentials and leverage women's health system contacts to inform women about family planning opportunities may be effective in improving coverage, quality, and equity of modern contraceptive use. Improving the demand satisfied for modern contraception may result in a reduction in the percentage of women experiencing high-risk births and may also reduce child mortality.

Trends and patterns of modern contraceptive use and relationships with high-risk births and child mortality in Burkina Faso

PubMed Central

Maïga, Abdoulaye; Hounton, Sennen; Amouzou, Agbessi; Akinyemi, Akanni; Shiferaw, Solomon; Baya, Banza; Bahan, Dalomi; Barros, Aluisio J. D.; Walker, Neff; Friedman, Howard

2015-01-01

Background In sub-Saharan Africa, few studies have stressed the importance of spatial heterogeneity analysis in modern contraceptive use and the relationships with high-risk births. Objective This paper aims to analyse the association between modern contraceptive use, distribution of birth risk, and under-five child mortality at both national and regional levels in Burkina Faso. Design The last three Demographic and Health Surveys – conducted in Burkina Faso in 1998, 2003, and 2010 – enabled descriptions of differentials, trends, and associations between modern contraceptive use, total fertility rates (TFR), and factors associated with high-risk births and under-five child mortality. Multivariate models, adjusted by covariates of cultural and socio-economic background and contact with health system, were used to investigate the relationship between birth risk factors and modern contraceptive prevalence rates (mCPR). Results Overall, Burkina Faso's modern contraception level remains low (15.4% in 2010), despite significant increases during the last decade. However, there are substantial variations in mCPR by region, and health facility contact was positively associated with mCPR increase. Women's fertility history and cultural and socio-economic background were also significant factors in predicting use of modern contraception. Low modern contraceptive use is associated with higher birth risks and increased child mortality. This association is stronger in the Sahel, Est, and Sud-Ouest regions. Even though all factors in high-risk births were associated with under-five mortality, it should be stressed that short birth spacing ranked as the highest risk in relation to mortality of children. Conclusions Programmes that target sub-national differentials and leverage women's health system contacts to inform women about family planning opportunities may be effective in improving coverage, quality, and equity of modern contraceptive use. Improving the demand satisfied for modern contraception may result in a reduction in the percentage of women experiencing high-risk births and may also reduce child mortality. PMID:26562142

Physical access to health facilities and contraceptive use in Kenya: evidence from the 2008-2009 Kenya Demographic and Health Survey.

PubMed

Ettarh, Remare R; Kyobutungi, Catherine

2012-09-01

The objective of the study was to determine the spatial variation in modern contraceptive use and unmet need for family planning across the counties of Kenya and to examine whether the spatial patterns were associated with inequalities in physical access to health facilities. Data were obtained from the 2008-2009 Kenya Demographic and Health Survey and linked to the location of health facilities in the country. Multivariate logistic regression was used to examine the influence of distance to the nearest health facility and health facility density, in addition to other covariates, on modern contraceptive use and unmet need. Overall, the prevalence of modern contraceptive use and unmet need among women aged 15-49 in Kenya was 42.1% and 19.7% respectively. Among the respondents who lived more than 5 km from the nearest health facility modern contraceptive use was significantly less likely compared to women resident 5 km or less from the nearest health facility. Women from counties with higher health facility density were 53% more likely to use modern contraceptives compared to women in counties with low health facility density. Distance and health facility density in the county were not significantly associated with unmet need. Physical access to health facilities is an important determinant of modern contraceptive use and unmet need in Kenya. Strategies should be developed in underserved counties to mitigate the challenge of distance to health facilities, such as delivering services by outreach and mobile facilities.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Modern contraceptive use among women in the Asuogyaman district of Ghana: is reliability more important than health concerns?

PubMed

Teye, Joseph Kofi

2013-06-01

This study examines the socio-demographic determinants of modern contraceptive use among women in the Asuogyaman district of Ghana. The results reveal that although 97% of the survey respondents knew of at least one modern method of contraception, only 16% of them were using modern contraceptives. Statistical tests show that level of education, place of residence, and work status significantly influence modern contraceptive use among women in the study area. Fear of side effects, desire for more children, and partner's disapproval were the main barriers to modern contraceptive use in the study area. The use of traditional methods of contraception was very high because of the perception that they are safer. Based on these findings, it has been suggested that in addition to making family planning services available and accessible, health workers must address attitudinal factors such as fear of side effects and high fertility preferences.

Mathematical background and attitudes toward statistics in a sample of Spanish college students.

PubMed

Carmona, José; Martínez, Rafael J; Sánchez, Manuel

2005-08-01

To examine the relation of mathematical background and initial attitudes toward statistics of Spanish college students in social sciences the Survey of Attitudes Toward Statistics was given to 827 students. Multivariate analyses tested the effects of two indicators of mathematical background (amount of exposure and achievement in previous courses) on the four subscales. Analysis suggested grades in previous courses are more related to initial attitudes toward statistics than the number of mathematics courses taken. Mathematical background was related with students' affective responses to statistics but not with their valuing of statistics. Implications of possible research are discussed.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characterizations of linear sufficient statistics

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Reoner, R.; Decell, H. P., Jr.

1977-01-01

A surjective bounded linear operator T from a Banach space X to a Banach space Y must be a sufficient statistic for a dominated family of probability measures defined on the Borel sets of X. These results were applied, so that they characterize linear sufficient statistics for families of the exponential type, including as special cases the Wishart and multivariate normal distributions. The latter result was used to establish precisely which procedures for sampling from a normal population had the property that the sample mean was a sufficient statistic.

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

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

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Statistical Knowledge for Teaching: Exploring it in the Classroom

ERIC Educational Resources Information Center

Burgess, Tim

2009-01-01

This paper first reports on the methodology of a study of teacher knowledge for statistics, conducted in a classroom at the primary school level. The methodology included videotaping of a sequence of lessons that involved students in investigating multivariate data sets, followed up by audiotaped interviews with each teacher. These stimulated…

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

ERIC Educational Resources Information Center

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

Spatial Statistical Model and Optimal Survey Design for Rapid Geophysical Characterization of UXO Sites

DTIC Science & Technology

2003-07-01

4, Gnanadesikan , 1977). An entity whose measured features fall into one of the regions is classified accordingly. For the approaches we discuss here... Gnanadesikan , R. 1977. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons, New York. Hassig, N. L., O’Brien, R. F

Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes

Treesearch

Jorge F. Perez-Quezada; Nicanor Z. Saliendra; William E. Emmerich; Emilio A. Laca

2007-01-01

The process of quality control of micrometeorological and carbon dioxide (CO2) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO2 flux...

The Evolution of Random Number Generation in MUVES

DTIC Science & Technology

2017-01-01

mathematical basis and statistical justification for algorithms used in the code. The working code provided produces results identical to the current...MUVES, includ- ing the mathematical basis and statistical justification for algorithms used in the code. The working code provided produces results...questionable numerical and statistical properties. The development of the modern system is traced through software change requests, resulting in a random number

FT. Sam 91 Whiskey Combat Medic Medical Simulation Training Quantitative Integration Enhancement Program

DTIC Science & Technology

2011-07-01

joined the project team in the statistical and research coordination role. Dr. Collin is an employee at the University of Pittsburgh. A successful...3. Submit to Ft. Detrick Completed Milestone: Statistical analysis planning 1. Review planned data metrics and data gathering tools...approach to performance assessment for continuous quality improvement.  Analyzing data with modern statistical techniques to determine the

Conceptual and statistical problems associated with the use of diversity indices in ecology.

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. In addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value.

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Exploratory Multivariate Analysis. A Graphical Approach.

DTIC Science & Technology

1981-01-01

Gnanadesikan , 1977) but we feel that these should be used with great caution unless one really has good reason to believe that the data came from such a...are referred to Gnanadesikan (1977). The present author hopes that the convenience of a single summary or significance level will not deter his readers...fit of a harmonic model to meteorological data. (In preparation). Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

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.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Indulging our gendered selves? Sex segregation by field of study in 44 countries.

PubMed

Charles, Maria; Bradley, Karen

2009-01-01

Data from 44 societies are used to explore sex segregation by field of study. Contrary to accounts linking socioeconomic modernization to a "degendering" of public-sphere institutions, sex typing of curricular fields is stronger in more economically developed contexts. The authors argue that two cultural forces combine in advanced industrial societies to create a new sort of sex segregation regime. The first is gender-essentialist ideology, which has proven to be extremely resilient even in the most liberal-egalitarian of contexts; the second is self-expressive value systems, which create opportunities and incentives for the expression of "gendered selves." Multivariate analyses suggest that structural features of postindustrial labor markets and modern educational systems support the cultivation, realization, and display of gender-specific curricular affinities.

Computer simulation of a single pilot flying a modern high-performance helicopter

NASA Technical Reports Server (NTRS)

Zipf, Mark E.; Vogt, William G.; Mickle, Marlin H.; Hoelzeman, Ronald G.; Kai, Fei; Mihaloew, James R.

1988-01-01

Presented is a computer simulation of a human response pilot model able to execute operational flight maneuvers and vehicle stabilization of a modern high-performance helicopter. Low-order, single-variable, human response mechanisms, integrated to form a multivariable pilot structure, provide a comprehensive operational control over the vehicle. Evaluations of the integrated pilot were performed by direct insertion into a nonlinear, total-force simulation environment provided by NASA Lewis. Comparisons between the integrated pilot structure and single-variable pilot mechanisms are presented. Static and dynamically alterable configurations of the pilot structure are introduced to simulate pilot activities during vehicle maneuvers. These configurations, in conjunction with higher level, decision-making processes, are considered for use where guidance and navigational procedures, operational mode transfers, and resource sharing are required.

Development of modern human subadult age and sex estimation standards using multi-slice computed tomography images from medical examiner's offices

NASA Astrophysics Data System (ADS)

Stock, Michala K.; Stull, Kyra E.; Garvin, Heather M.; Klales, Alexandra R.

2016-10-01

Forensic anthropologists are routinely asked to estimate a biological profile (i.e., age, sex, ancestry and stature) from a set of unidentified remains. In contrast to the abundance of collections and techniques associated with adult skeletons, there is a paucity of modern, documented subadult skeletal material, which limits the creation and validation of appropriate forensic standards. Many are forced to use antiquated methods derived from small sample sizes, which given documented secular changes in the growth and development of children, are not appropriate for application in the medico-legal setting. Therefore, the aim of this project is to use multi-slice computed tomography (MSCT) data from a large, diverse sample of modern subadults to develop new methods to estimate subadult age and sex for practical forensic applications. The research sample will consist of over 1,500 full-body MSCT scans of modern subadult individuals (aged birth to 20 years) obtained from two U.S. medical examiner's offices. Statistical analysis of epiphyseal union scores, long bone osteometrics, and os coxae landmark data will be used to develop modern subadult age and sex estimation standards. This project will result in a database of information gathered from the MSCT scans, as well as the creation of modern, statistically rigorous standards for skeletal age and sex estimation in subadults. Furthermore, the research and methods developed in this project will be applicable to dry bone specimens, MSCT scans, and radiographic images, thus providing both tools and continued access to data for forensic practitioners in a variety of settings.

Hepatic Arterial Infusion in Combination with Modern Systemic Chemotherapy is Associated with Improved Survival Compared with Modern Systemic Chemotherapy Alone in Patients with Isolated Unresectable Colorectal Liver Metastases: A Case-Control Study.

PubMed

Dhir, Mashaal; Jones, Heather L; Shuai, Yongli; Clifford, Amber K; Perkins, Samantha; Steve, Jennifer; Hogg, Melissa E; Choudry, M Haroon A; Pingpank, James F; Holtzman, Matthew P; Zeh, Herbert J; Bahary, Nathan; Bartlett, David L; Zureikat, Amer H

2017-01-01

In the era of effective modern systemic chemotherapy (CT), the role of hepatic arterial infusion of fluoxuridine (HAI-FUDR) in the treatment of isolated unresectable colorectal liver metastasis (IU-CRCLM) remains controversial. This study aimed to compare the overall survival (OS) of HAI-FUDR in combination with modern systemic CT versus modern systemic CT alone in patients with IU-CRCLM. This was a case-control study of IU-CRCLM patients who underwent HAI + modern systemic CT or modern systemic CT alone. Modern systemic CT was defined as the use of multidrug regimens containing oxaliplatin and/or irinotecan ± biologics. Overall, 86 patients met the inclusion criteria (n = 40 for the HAI + CT group, and n = 46 for the CT-alone group). Both groups were similar in demographics, primary and stage IV tumor characteristics, and treatment-related variables (carcinoembryonic antigen, use of biologic agents, total number of lines of systemic CT administered) (all p > 0.05). Additionally, both groups were comparable with respect to liver tumor burden [median number of lesions (13.5 vs. 15), percentage of liver tumor replacement (37.5 vs. 40 %), and size of largest lesion] (all p > 0.05). Median OS in the HAI + CT group was 32.8 months compared with 15.3 months in the CT-alone group (p < 0.0001). Multivariate analysis revealed HAI + CT (hazard ratio 0.4, 95 % confidence interval 0.21-0.72; p = 0.003), Eastern Cooperative Oncology Group status, and receipt of increasing number of lines of systemic CT to be independent predictors of survival. In this case-control study of patients with IU-CRCLM, HAI in combination with CT was associated with improved OS when compared with modern systemic CT alone.

Are the correct herbal claims by Hildegard von Bingen only lucky strikes? A new statistical approach.

PubMed

Uehleke, Bernhard; Hopfenmueller, Werner; Stange, Rainer; Saller, Reinhard

2012-01-01

Ancient and medieval herbal books are often believed to describe the same claims still in use today. Medieval herbal books, however, provide long lists of claims for each herb, most of which are not approved today, while the herb's modern use is often missing. So the hypothesis arises that a medieval author could have randomly hit on 'correct' claims among his many 'wrong' ones. We developed a statistical procedure based on a simple probability model. We applied our procedure to the herbal books of Hildegard von Bingen (1098- 1179) as an example for its usefulness. Claim attributions for a certain herb were classified as 'correct' if approximately the same as indicated in actual monographs. The number of 'correct' claim attributions was significantly higher than it could have been by pure chance, even though the vast majority of Hildegard von Bingen's claims were not 'correct'. The hypothesis that Hildegard would have achieved her 'correct' claims purely by chance can be clearly rejected. The finding that medical claims provided by a medieval author are significantly related to modern herbal use supports the importance of traditional medicinal systems as an empirical source. However, since many traditional claims are not in accordance with modern applications, they should be used carefully and analyzed in a systematic, statistics-based manner. Our statistical approach can be used for further systematic comparison of herbal claims of traditional sources as well as in the fields of ethnobotany and ethnopharmacology. Copyright © 2012 S. Karger AG, Basel.

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria

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

Gunn, Andrew J., E-mail: agunn@uabmc.edu; Sheth, Rahul A.; Luber, Brandon

2017-01-15

PurposeThe purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC).Materials and methodsHospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which,more » if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP).Results75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6–24.8) and 9.8 months (95 % CI 7.1–21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51–0.58 in the univariate model; range: 0.54–0.58 in the multivariate model) or TTP (C-statistic range: 0.55–0.59 in the univariate model; range: 0.57–0.61 in the multivariate model).ConclusionCurrent response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.« less

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria.

PubMed

Gunn, Andrew J; Sheth, Rahul A; Luber, Brandon; Huynh, Minh-Huy; Rachamreddy, Niranjan R; Kalva, Sanjeeva P

2017-01-01

The purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC). Hospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which, if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP). 75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6-24.8) and 9.8 months (95 % CI 7.1-21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51-0.58 in the univariate model; range: 0.54-0.58 in the multivariate model) or TTP (C-statistic range: 0.55-0.59 in the univariate model; range: 0.57-0.61 in the multivariate model). Current response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

A simple rapid approach using coupled multivariate statistical methods, GIS and trajectory models to delineate areas of common oil spill risk

NASA Astrophysics Data System (ADS)

Guillen, George; Rainey, Gail; Morin, Michelle

2004-04-01

Currently, the Minerals Management Service uses the Oil Spill Risk Analysis model (OSRAM) to predict the movement of potential oil spills greater than 1000 bbl originating from offshore oil and gas facilities. OSRAM generates oil spill trajectories using meteorological and hydrological data input from either actual physical measurements or estimates generated from other hydrological models. OSRAM and many other models produce output matrices of average, maximum and minimum contact probabilities to specific landfall or target segments (columns) from oil spills at specific points (rows). Analysts and managers are often interested in identifying geographic areas or groups of facilities that pose similar risks to specific targets or groups of targets if a spill occurred. Unfortunately, due to the potentially large matrix generated by many spill models, this question is difficult to answer without the use of data reduction and visualization methods. In our study we utilized a multivariate statistical method called cluster analysis to group areas of similar risk based on potential distribution of landfall target trajectory probabilities. We also utilized ArcView™ GIS to display spill launch point groupings. The combination of GIS and multivariate statistical techniques in the post-processing of trajectory model output is a powerful tool for identifying and delineating areas of similar risk from multiple spill sources. We strongly encourage modelers, statistical and GIS software programmers to closely collaborate to produce a more seamless integration of these technologies and approaches to analyzing data. They are complimentary methods that strengthen the overall assessment of spill risks.

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

Publication of statistically significant research findings in prosthodontics & implant dentistry in the context of other dental specialties.

PubMed

Papageorgiou, Spyridon N; Kloukos, Dimitrios; Petridis, Haralampos; Pandis, Nikolaos

2015-10-01

To assess the hypothesis that there is excessive reporting of statistically significant studies published in prosthodontic and implantology journals, which could indicate selective publication. The last 30 issues of 9 journals in prosthodontics and implant dentistry were hand-searched for articles with statistical analyses. The percentages of significant and non-significant results were tabulated by parameter of interest. Univariable/multivariable logistic regression analyses were applied to identify possible predictors of reporting statistically significance findings. The results of this study were compared with similar studies in dentistry with random-effects meta-analyses. From the 2323 included studies 71% of them reported statistically significant results, with the significant results ranging from 47% to 86%. Multivariable modeling identified that geographical area and involvement of statistician were predictors of statistically significant results. Compared to interventional studies, the odds that in vitro and observational studies would report statistically significant results was increased by 1.20 times (OR: 2.20, 95% CI: 1.66-2.92) and 0.35 times (OR: 1.35, 95% CI: 1.05-1.73), respectively. The probability of statistically significant results from randomized controlled trials was significantly lower compared to various study designs (difference: 30%, 95% CI: 11-49%). Likewise the probability of statistically significant results in prosthodontics and implant dentistry was lower compared to other dental specialties, but this result did not reach statistical significant (P>0.05). The majority of studies identified in the fields of prosthodontics and implant dentistry presented statistically significant results. The same trend existed in publications of other specialties in dentistry. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Teaching Biology through Statistics: Application of Statistical Methods in Genetics and Zoology Courses

PubMed Central

Colon-Berlingeri, Migdalisel; Burrowes, Patricia A.

2011-01-01

Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math–biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology. PMID:21885822

Teaching biology through statistics: application of statistical methods in genetics and zoology courses.

PubMed

Colon-Berlingeri, Migdalisel; Burrowes, Patricia A

2011-01-01

Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math-biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology.

Edward J. Wolfrum | NREL

Science.gov Websites

. Another project used multivariate statistics to develop a novel device to non-invasively measure hydrogen Cellulosic Ethanol Production due to Experimental Measurement Uncertainty," Biotechnology for Biofuels

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

A Note on Asymptotic Joint Distribution of the Eigenvalues of a Noncentral Multivariate F Matrix.

DTIC Science & Technology

1984-11-01

Krishnaiah (1982). Now, let us consider the samples drawn from the k multivariate normal popuiejons. Let (Xlt....Xpt) denote the mean vector of the t...to maltivariate problems. Sankh-ya, 4, 381-39(s. (71 KRISHNAIAH , P. R. (1982). Selection of variables in discrimlnant analysis. In Handbook of...Statistics, Volume 2 (P. R. Krishnaiah , editor), 805-820. North-Holland Publishing Company. 6. Unclassifie INSTRUCTIONS REPORT DOCUMENTATION PAGE

LIKELIHOOD RATIO TESTS OF HYPOTHESES ON MULTIVARIATE POPULATIONS, VOLUME II, TEST OF HYPOTHESIS--STATISTICAL MODELS FOR THE EVALUATION AND INTERPRETATION OF EDUCATIONAL CRITERIA. PART 4.

ERIC Educational Resources Information Center

SAW, J.G.

THIS PAPER DEALS WITH SOME TESTS OF HYPOTHESIS FREQUENTLY ENCOUNTERED IN THE ANALYSIS OF MULTIVARIATE DATA. THE TYPE OF HYPOTHESIS CONSIDERED IS THAT WHICH THE STATISTICIAN CAN ANSWER IN THE NEGATIVE OR AFFIRMATIVE. THE DOOLITTLE METHOD MAKES IT POSSIBLE TO EVALUATE THE DETERMINANT OF A MATRIX OF HIGH ORDER, TO SOLVE A MATRIX EQUATION, OR TO…

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

What is a good index? Problems with statistically based indicators and the Malmquist index as alternative

USDA-ARS?s Scientific Manuscript database

Conventional multivariate statistical methods have been used for decades to calculate environmental indicators. These methods generally work fine if they are used in a situation where the method can be tailored to the data. But there is some skepticism that the methods might fail in the context of s...

A revised burial dose estimation procedure for optical dating of youngand modern-age sediments

USGS Publications Warehouse

Arnold, L.J.; Roberts, R.G.; Galbraith, R.F.; DeLong, S.B.

2009-01-01

The presence of genuinely zero-age or near-zero-age grains in modern-age and very young samples poses a problem for many existing burial dose estimation procedures used in optical (optically stimulated luminescence, OSL) dating. This difficulty currently necessitates consideration of relatively simplistic and statistically inferior age models. In this study, we investigate the potential for using modified versions of the statistical age models of Galbraith et??al. [Galbraith, R.F., Roberts, R.G., Laslett, G.M., Yoshida, H., Olley, J.M., 1999. Optical dating of single and multiple grains of quartz from Jinmium rock shelter, northern Australia: Part I, experimental design and statistical models. Archaeometry 41, 339-364.] to provide reliable equivalent dose (De) estimates for young and modern-age samples that display negative, zero or near-zero De estimates. For this purpose, we have revised the original versions of the central and minimum age models, which are based on log-transformed De values, so that they can be applied to un-logged De estimates and their associated absolute standard errors. The suitability of these 'un-logged' age models is tested using a series of known-age fluvial samples deposited within two arroyo systems from the American Southwest. The un-logged age models provide accurate burial doses and final OSL ages for roughly three-quarters of the total number of samples considered in this study. Sensitivity tests reveal that the un-logged versions of the central and minimum age models are capable of producing accurate burial dose estimates for modern-age and very young (<350??yr) fluvial samples that contain (i) more than 20% of well-bleached grains in their De distributions, or (ii) smaller sub-populations of well-bleached grains for which the De values are known with high precision. Our results indicate that the original (log-transformed) versions of the central and minimum age models are still preferable for most routine dating applications, since these age models are better suited to the statistical properties of typical single-grain and multi-grain single-aliquot De datasets. However, the unique error properties of modern-age samples, combined with the problems of calculating natural logarithms of negative or zero-Gy De values, mean that the un-logged versions of the central and minimum age models currently offer the most suitable means of deriving accurate burial dose estimates for very young and modern-age samples. ?? 2009 Elsevier Ltd. All rights reserved.

Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

Here, we show the effects of sexual steroids, progesterone, testosterone, or estradiol on motility parameters of boar sperm. Sixteen commercial seminal doses, four each of four adult boars, were analyzed using computer assisted sperm analysis (CASA). Mean values of motility parameters were analyzed by bivariate and multivariate statistics. Principal component analysis (PCA), followed by hierarchical clustering, was applied on data of motility parameters, provided automatically as intervals by the CASA system. Effects of sexual steroids were described in the kinematic subpopulations identified from multivariate statistics. Mean values of motility parameters were not significantly changed after addition of sexual steroids. Multivariate graphics showed that sperm subpopulations were not sensitive to the addition of either testosterone or estradiol, but sperm subpopulations responsive to progesterone were found. Distribution of motility parameters were wide in controls but sharpened at distinct concentrations of progesterone. We conclude that kinematic sperm subpopulations responsive to progesterone are present in boar semen, and these subpopulations are masked in evaluations of mean values of motility parameters. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

PubMed

Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

2009-06-01

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

Extracting chemical information from high-resolution Kβ X-ray emission spectroscopy

NASA Astrophysics Data System (ADS)

Limandri, S.; Robledo, J.; Tirao, G.

2018-06-01

High-resolution X-ray emission spectroscopy allows studying the chemical environment of a wide variety of materials. Chemical information can be obtained by fitting the X-ray spectra and observing the behavior of some spectral features. Spectral changes can also be quantified by means of statistical parameters calculated by considering the spectrum as a probability distribution. Another possibility is to perform statistical multivariate analysis, such as principal component analysis. In this work the performance of these procedures for extracting chemical information in X-ray emission spectroscopy spectra for mixtures of Mn2+ and Mn4+ oxides are studied. A detail analysis of the parameters obtained, as well as the associated uncertainties is shown. The methodologies are also applied for Mn oxidation state characterization of double perovskite oxides Ba1+xLa1-xMnSbO6 (with 0 ≤ x ≤ 0.7). The results show that statistical parameters and multivariate analysis are the most suitable for the analysis of this kind of spectra.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

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

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Descriptive Statistics for Modern Test Score Distributions: Skewness, Kurtosis, Discreteness, and Ceiling Effects

ERIC Educational Resources Information Center

Ho, Andrew D.; Yu, Carol C.

2015-01-01

Many statistical analyses benefit from the assumption that unconditional or conditional distributions are continuous and normal. More than 50 years ago in this journal, Lord and Cook chronicled departures from normality in educational tests, and Micerri similarly showed that the normality assumption is met rarely in educational and psychological…

Running R Statistical Computing Environment Software on the Peregrine

Science.gov Websites

for the development of new statistical methodologies and enjoys a large user base. Please consult the distribution details. Natural language support but running in an English locale R is a collaborative project programming paradigms to better leverage modern HPC systems. The CRAN task view for High Performance Computing

Teaching Biology through Statistics: Application of Statistical Methods in Genetics and Zoology Courses

ERIC Educational Resources Information Center

Colon-Berlingeri, Migdalisel; Burrowes, Patricia A.

2011-01-01

Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the…

[Artificial neural networks for decision making in urologic oncology].

PubMed

Remzi, M; Djavan, B

2007-06-01

This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

The Statistical Interpretation of Entropy: An Activity

ERIC Educational Resources Information Center

Timmberlake, Todd

2010-01-01

The second law of thermodynamics, which states that the entropy of an isolated macroscopic system can increase but will not decrease, is a cornerstone of modern physics. Ludwig Boltzmann argued that the second law arises from the motion of the atoms that compose the system. Boltzmann's statistical mechanics provides deep insight into the…

Harnessing Multivariate Statistics for Ellipsoidal Data in Structural Geology

NASA Astrophysics Data System (ADS)

Roberts, N.; Davis, J. R.; Titus, S.; Tikoff, B.

2015-12-01

Most structural geology articles do not state significance levels, report confidence intervals, or perform regressions to find trends. This is, in part, because structural data tend to include directions, orientations, ellipsoids, and tensors, which are not treatable by elementary statistics. We describe a full procedural methodology for the statistical treatment of ellipsoidal data. We use a reconstructed dataset of deformed ooids in Maryland from Cloos (1947) to illustrate the process. Normalized ellipsoids have five degrees of freedom and can be represented by a second order tensor. This tensor can be permuted into a five dimensional vector that belongs to a vector space and can be treated with standard multivariate statistics. Cloos made several claims about the distribution of deformation in the South Mountain fold, Maryland, and we reexamine two particular claims using hypothesis testing: 1) octahedral shear strain increases towards the axial plane of the fold; 2) finite strain orientation varies systematically along the trend of the axial trace as it bends with the Appalachian orogen. We then test the null hypothesis that the southern segment of South Mountain is the same as the northern segment. This test illustrates the application of ellipsoidal statistics, which combine both orientation and shape. We report confidence intervals for each test, and graphically display our results with novel plots. This poster illustrates the importance of statistics in structural geology, especially when working with noisy or small datasets.

Modern dust aerosol availability in northwestern China.

PubMed

Wang, Xunming; Cheng, Hong; Che, Huizheng; Sun, Jimin; Lu, Huayu; Qiang, Mingrui; Hua, Ting; Zhu, Bingqi; Li, Hui; Ma, Wenyong; Lang, Lili; Jiao, Linlin; Li, Danfeng

2017-08-18

The sources of modern dust aerosols and their emission magnitudes are fundamental for linking dust with climate and environment. Using field sample data, wind tunnel experiments and statistical analysis, we determined the contributions of wadis, gobi (stony desert), lakebeds, riverbeds, and interdunes to modern dust aerosol availability in the three important potential dust sources including the Tarim Basin, Qaidam Basin, and Ala Shan Plateau of China. The results show that riverbeds are the dominant landscape for modern dust aerosol availabilities in the Qaidam Basin, while wadis, gobi, and interdunes are the main landscapes over the Ala Shan Plateau and Tarim Basin. The Ala Shan Plateau and Tarim Basin are potential dust sources in northwestern China, while the Qaidam Basin is not a major source of the modern dust aerosols nowadays, and it is not acting in a significant way to the Loess Plateau presently. Moreover, most of modern dust aerosol emissions from China originated from aeolian processes with low intensities rather than from major dust events.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Computerized design of controllers using data models

NASA Technical Reports Server (NTRS)

Irwin, Dennis; Mitchell, Jerrel; Medina, Enrique; Allwine, Dan; Frazier, Garth; Duncan, Mark

1995-01-01

The major contributions of the grant effort have been the enhancement of the Compensator Improvement Program (CIP), which resulted in the Ohio University CIP (OUCIP) package, and the development of the Model and Data-Oriented Computer Aided Design System (MADCADS). Incorporation of direct z-domain designs into CIP was tested and determined to be numerically ill-conditioned for the type of lightly damped problems for which the development was intended. Therefore, it was decided to pursue the development of z-plane designs in the w-plane, and to make this conversion transparent to the user. The analytical development needed for this feature, as well as that needed for including compensator damping ratios and DC gain specifications, closed loop stability requirements, and closed loop disturbance rejection specifications into OUCIP are all contained in Section 3. OUCIP was successfully tested with several example systems to verify proper operation of existing and new features. The extension of the CIP philosophy and algorithmic approach to handle modern multivariable controller design criteria was implemented and tested. Several new algorithms for implementing the search approach to modern multivariable control system design were developed and tested. This analytical development, most of which was incorporated into the MADCADS software package, is described in Section 4, which also includes results of the application of MADCADS to the MSFC ACES facility and the Hubble Space Telescope.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Testing for significance of phase synchronisation dynamics in the EEG.

PubMed

Daly, Ian; Sweeney-Reed, Catherine M; Nasuto, Slawomir J

2013-06-01

A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

Processes and subdivisions in diogenites, a multivariate statistical analysis

NASA Technical Reports Server (NTRS)

Harriott, T. A.; Hewins, R. H.

1984-01-01

Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations

PubMed Central

Jack, John; Havener, Tammy M; McLeod, Howard L; Motsinger-Reif, Alison A; Foster, Matthew

2015-01-01

Aim: We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies. Methods: Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy. Results & Conclusion: For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses. PMID:26314407

Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers.

PubMed

Genser, Bernd; Fischer, Joachim E; Figueiredo, Camila A; Alcântara-Neves, Neuza; Barreto, Mauricio L; Cooper, Philip J; Amorim, Leila D; Saemann, Marcus D; Weichhart, Thomas; Rodrigues, Laura C

2016-05-20

Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists' hypotheses about the underlying biological mechanisms to be integrated. We present an analytical approach for statistical analysis of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes.

Modern contraceptive utilization and associated factors among married pastoralist women in Bale eco-region, Bale Zone, South East Ethiopia.

PubMed

Belda, Semere Sileshi; Haile, Mekonnen Tegegne; Melku, Abulie Takele; Tololu, Abdurehaman Kalu

2017-03-14

Women who live in remote rural areas encounter different challenges against contraception and often deny the use of modern contraceptive methods. The predictors of modern contraceptive utilization by pastoralist women in the Bale eco-region could be specific and are not well known. Therefore, this study aims to assess modern contraceptive utilization and its determinants among married pastoralist women in Bale eco-region, Oromia regional state, South East Ethiopia. A community-based cross-sectional study was conducted from 20th November 2015 to 30th February 2016. A structured questionnaire was used to interview 549 married pastoralist women who were selected by multistage sampling technique. The data were analyzed by SPSS - 21 software, multivariate logistic regression analysis was used to identify predictors of modern contraceptive use at (P-value <0.05), and odds ratios with 95% confidence interval were used to assess the strength of associations between variables. The current modern contraceptive method use by married pastoralist women was (20.8%). Among the total users, (78.1%) use the injectable method. The common reasons for non-use of modern contraceptive methods includes: religious-opposition (55.9%), desire for more children (28.3%), fear of side effects (25.5%), and husband's opposition (17.5%). Couple discussion (AOR = 4.63, 95%CI: 2.15, 9.98), perceived husband's approval (AOR = 8.00, 95% CI: 3.52, 18.19), discussion with health extension worker (AOR = 5.99, 95% CI: 1.81, 19.85), and perceived cultural acceptability (AOR = 2.10, 95% CI: 1.09, 4.03) were the independent predictors of modern contraceptive use by married pastoralist women in Bale eco-region. The study identified lower modern contraceptive method utilization by pastoralist women, and the majority of the contraceptive users rely on short- acting contraceptive methods. The uncomplimentary perceptions towards religious and cultural acceptability of modern contraceptive method were among the major reasons for lesser utilization of the methods. Family planning programs should be tailored to actively involve pastoralist women, husbands, and religious leaders in pastoralist communities.

The role of modern control theory in the design of controls for aircraft turbine engines

NASA Technical Reports Server (NTRS)

Zeller, J.; Lehtinen, B.; Merrill, W.

1982-01-01

The development, applications, and current research in modern control theory (MCT) are reviewed, noting the importance for fuel-efficient operation of turbines with variable inlet guide vanes, compressor stators, and exhaust nozzle area. The evolution of multivariable propulsion control design is examined, noting a basis in a matrix formulation of the differential equations defining the process, leading to state space formulations. Reports and papers which appeared from 1970-1982 which dealt with problems in MCT applications to turbine engine control design are outlined, including works on linear quadratic regulator methods, frequency domain methods, identification, estimation, and model reduction, detection, isolation, and accommodation, and state space control, adaptive control, and optimization approaches. Finally, NASA programs in frequency domain design, sensor failure detection, computer-aided control design, and plant modeling are explored

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

Intra-individual metameric variation expressed at the enamel-dentine junction of lower post-canine dentition of South African fossil hominins and modern humans.

PubMed

Pan, Lei; Thackeray, John Francis; Dumoncel, Jean; Zanolli, Clément; Oettlé, Anna; de Beer, Frikkie; Hoffman, Jakobus; Duployer, Benjamin; Tenailleau, Christophe; Braga, José

2017-08-01

The aim of this study is to compare the degree and patterning of inter- and intra-individual metameric variation in South African australopiths, early Homo and modern humans. Metameric variation likely reflects developmental and taxonomical issues, and could also be used to infer ecological and functional adaptations. However, its patterning along the early hominin postcanine dentition, particularly among South African fossil hominins, remains unexplored. Using microfocus X-ray computed tomography (µXCT) and geometric morphometric tools, we studied the enamel-dentine junction (EDJ) morphology and we investigated the intra- and inter-individual EDJ metameric variation among eight australopiths and two early Homo specimens from South Africa, as well as 32 modern humans. Along post-canine dentition, shape changes between metameres represented by relative positions and height of dentine horns, outlines of the EDJ occlusal table are reported in modern and fossil taxa. Comparisons of EDJ mean shapes and multivariate analyses reveal substantial variation in the direction and magnitude of metameric shape changes among taxa, but some common trends can be found. In modern humans, both the direction and magnitude of metameric shape change show increased variability in M 2 -M 3 compared to M 1 -M 2 . Fossil specimens are clustered together showing similar magnitudes of shape change. Along M 2 -M 3 , the lengths of their metameric vectors are not as variable as those of modern humans, but they display considerable variability in the direction of shape change. The distalward increase of metameric variation along the modern human molar row is consistent with the odontogenetic models of molar row structure (inhibitory cascade model). Though much remains to be tested, the variable trends and magnitudes in metamerism in fossil hominins reported here, together with differences in the scale of shape change between modern humans and fossil hominins may provide valuable information regarding functional morphology and developmental processes in fossil species. © 2017 Wiley Periodicals, Inc.

A 3D quantitative comparison of trapezium and trapezoid relative articular and nonarticular surface areas in modern humans and great apes.

PubMed

Tocheri, M W; Razdan, A; Williams, R C; Marzke, M W

2005-11-01

The structure and functions of the modern human hand are critical components of what distinguishes Homo sapiens from the great apes (Gorilla, Pan, and Pongo). In this study, attention is focused on the trapezium and trapezoid, the two most lateral bones of the distal carpal row, in the four extant hominid genera, representing the first time they have been quantified and analyzed together as a morphological-functional complex. Our objective is to quantify the relative articular and nonarticular surface areas of these two bones and to test whether modern humans exhibit significant shape differences from the great apes, as predicted by previous qualitative analyses and the functional demands of differing manipulative and locomotor strategies. Modern humans were predicted to show larger relative first metacarpal and scaphoid surfaces on the trapezium because of the regular recruitment of the thumb during manipulative behaviors; alternatively, great apes were predicted to show larger relative second metacarpal and scaphoid surfaces on the trapezoid because of the functional demands on the hands during locomotor behaviors. Modern humans were also expected to exhibit larger relative mutual joint surfaces between the trapezoid and adjacent carpals than do the great apes because of assumed transverse loads generated by the functional demands of the modern human power grip. Using 3D bone models acquired through laser digitizing, the relative articular and nonarticular areas on each bone are quantified and compared. Multivariate analyses of these data clearly distinguish modern humans from the great apes. In total, the observed differences between modern humans and the great apes support morphological predictions based on the fact that this region of the human wrist is no longer involved in weight-bearing during locomotor behavior and is instead recruited solely for manipulative behaviors. The results provide the beginnings of a 3D comparative standard against which further extant and fossil primate wrist bones can be compared within the contexts of manipulative and locomotor behaviors.

Differentials of modern contraceptive methods use by food security status among married women of reproductive age in Wolaita Zone, South Ethiopia.

PubMed

Feyisso, Mohammed; Belachew, Tefera; Tesfay, Amanuel; Addisu, Yohannes

2015-01-01

In spite of the massive spending and extensive family-planning promotion, many poor people in the third world remain reluctant to use modern contraceptive method. Mostly when they use modern contraceptives, their continuation rates are often low. Reproductive health can improve women's nutrition; in return better nutrition can improve reproductive health. Thus addressing the connection between nutrition and reproductive health is critical to ensure population growth that does not overwhelm world resources. A community based cross-sectional study was conducted from March 15-30, 2014 in Soddo Zuria Woreda, Southern Ethiopia. A total of 651 currently married women of reproductive age group were selected using multistage sampling. Probability proportional to the size allocation method was employed to determine the number of households. Multivariable logistic regression was used to assess the association between family planning use and food security status after adjusting for other covariates. Use of modern contraceptive method was significantly low among food insecure women (29.7 %) compared to those who were food secure (52.0 %), (P < 0.001). Women from food secure households were nearly twice likely to use modern contraceptive methods (AOR: 1.69 (CI: 1.03, 2.66)). Similarly, those who had antenatal care (ANC) visit (AOR: 4.56 (CI: 2.45, 7.05)); exposure to media (AOR: 4.92 (CI: 1.84, 13.79)) and those who discussed about contraceptive methods with their partner (AOR: 3.07 (CI: 1.86, 5.22)) were more likely to use modern contraceptive methods. Conversely, women who delivered their last child at home were less likely to use modern contraceptive methods (AOR: 0.08 (CI: 0.03, 0.13)). Food insecurity is negatively associated with modern contraceptive method use. Thus food insecurity should be considered as one of the barriers in designing family planning services and needs special arrangement.

Sexual autonomy and contraceptive use among women in Nigeria: findings from the Demographic and Health Survey data

PubMed Central

Viswan, Saritha P; Ravindran, T K Sundari; Kandala, Ngianga-Bakwin; Petzold, Max G; Fonn, Sharon

2017-01-01

Context The persistent low contraceptive use and high fertility in Nigeria despite improvements in educational achievements calls for an examination of the role of factors, which may moderate the use of modern contraception. This article explores the influence of sexual autonomy on the use of modern contraceptive methods among women and its relative importance compared with other, more traditional, indicators of women’s autonomy such as education and occupation. Data and methods Data from two Demographic and Health Surveys (DHS), 2008 and 2013, were used in this study. An index of sexual autonomy was constructed by combining related DHS variables, and its association with current use of modern contraception was examined at each time point as well as over time using multivariate regression analysis. Results The observed prevalence for use of modern contraception was 2.8 and 2.6 times higher among women who had high sexual autonomy in 2008 and 2013, respectively. The corresponding figures for women with secondary or higher education were 8.2 and 11.8 times higher, respectively, compared with women with no education. But after controlling for wealth index, religion, place of residence, autonomy and experience of intimate partner violence (IPV), the likelihood of use of modern contraception was lowered to about 2.5 (from 8.2) and 2.8 (from 11.8) times during 2008 and 2013, respectively, among women with secondary or higher education. The likelihood of use of modern contraception lowered only to 1.6 (from 2.8) and 1.8 (from 2.6) times among women with high sexual autonomy after controlling for other covariates, respectively, during the same period. Conclusion Sexual autonomy seems to play an important role in women’s use of modern contraceptive methods independent of education and a number of other factors related to women’s status. Sexual autonomy needs to be simultaneously promoted alongside increasing educational opportunities to enhance women’s ability to use modern contraception. PMID:28883744

Sexual autonomy and contraceptive use among women in Nigeria: findings from the Demographic and Health Survey data.

PubMed

Viswan, Saritha P; Ravindran, T K Sundari; Kandala, Ngianga-Bakwin; Petzold, Max G; Fonn, Sharon

2017-01-01

The persistent low contraceptive use and high fertility in Nigeria despite improvements in educational achievements calls for an examination of the role of factors, which may moderate the use of modern contraception. This article explores the influence of sexual autonomy on the use of modern contraceptive methods among women and its relative importance compared with other, more traditional, indicators of women's autonomy such as education and occupation. Data from two Demographic and Health Surveys (DHS), 2008 and 2013, were used in this study. An index of sexual autonomy was constructed by combining related DHS variables, and its association with current use of modern contraception was examined at each time point as well as over time using multivariate regression analysis. The observed prevalence for use of modern contraception was 2.8 and 2.6 times higher among women who had high sexual autonomy in 2008 and 2013, respectively. The corresponding figures for women with secondary or higher education were 8.2 and 11.8 times higher, respectively, compared with women with no education. But after controlling for wealth index, religion, place of residence, autonomy and experience of intimate partner violence (IPV), the likelihood of use of modern contraception was lowered to about 2.5 (from 8.2) and 2.8 (from 11.8) times during 2008 and 2013, respectively, among women with secondary or higher education. The likelihood of use of modern contraception lowered only to 1.6 (from 2.8) and 1.8 (from 2.6) times among women with high sexual autonomy after controlling for other covariates, respectively, during the same period. Sexual autonomy seems to play an important role in women's use of modern contraceptive methods independent of education and a number of other factors related to women's status. Sexual autonomy needs to be simultaneously promoted alongside increasing educational opportunities to enhance women's ability to use modern contraception.

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis

NASA Astrophysics Data System (ADS)

Nallala, Jayakrupakar; Gobinet, Cyril; Diebold, Marie-Danièle; Untereiner, Valérie; Bouché, Olivier; Manfait, Michel; Sockalingum, Ganesh Dhruvananda; Piot, Olivier

2012-11-01

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

Work characteristics, socioeconomic position and health: a systematic review of mediation and moderation effects in prospective studies

PubMed Central

Hoven, Hanno; Siegrist, Johannes

2013-01-01

Social inequalities in health persist in modern societies. The contribution of adverse work and employment conditions towards their explanation is analysed by two approaches, mediation and moderation. Yet the relative significance of each approach remains unclear in respective research. We set out to study this question by conducting a systematic literature review. We included all original papers based on prospective observational studies of employed cohorts that were published between January 1980 and October 2012 meeting our search criteria, by using major databases and by observing established quality criteria. 26 reports were included after quality assessment. 17 studies examined the mediation hypothesis and nine studies tested the moderation hypothesis. Moderate support was found for the mediation hypothesis where OR or HR of health according to socioeconomic position (SEP) were reduced in a majority of analyses after introducing work characteristics in multivariate models. Evidence in favour of the moderation hypothesis was found in some studies, demonstrating stronger effects of adverse work on health among people with low SEP. Despite some support in favour of the two hypotheses future research should aim at reducing the heterogeneity in defining and measuring core variables and at applying advanced statistical analyses. Policy recommendations would benefit from a higher degree of consistency of respective research evidence. PMID:23739492

Efficient inference for genetic association studies with multiple outcomes.

PubMed

Ruffieux, Helene; Davison, Anthony C; Hager, Jorg; Irincheeva, Irina

2017-10-01

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Factors underlying the psychological and behavioral characteristics of Office of Strategic Services candidates: the assessment of men data revisited.

PubMed

Lenzenweger, Mark F

2015-01-01

During World War II, the Office of Strategic Services (OSS), the forerunner of the Central Intelligence Agency, sought the assistance of clinical psychologists and psychiatrists to establish an assessment program for evaluating candidates for the OSS. The assessment team developed a novel and rigorous program to evaluate OSS candidates. It is described in Assessment of Men: Selection of Personnel for the Office of Strategic Services (OSS Assessment Staff, 1948). This study examines the sole remaining multivariate data matrix that includes all final ratings for a group of candidates (n = 133) assessed near the end of the assessment program. It applies the modern statistical methods of both exploratory and confirmatory factor analysis to this rich and highly unique data set. An exploratory factor analysis solution suggested 3 factors underlie the OSS assessment staff ratings. Confirmatory factor analysis results of multiple plausible substantive models reveal that a 3-factor model provides the best fit to these data. The 3 factors are emotional/interpersonal factors (social relations, emotional stability, security), intelligence processing (effective IQ, propaganda skills, observing and reporting), and agency/surgency (motivation, energy and initiative, leadership, physical ability). These factors are discussed in terms of their potential utility for personnel selection within the intelligence community.

Work characteristics, socioeconomic position and health: a systematic review of mediation and moderation effects in prospective studies.

PubMed

Hoven, Hanno; Siegrist, Johannes

2013-09-01

Social inequalities in health persist in modern societies. The contribution of adverse work and employment conditions towards their explanation is analysed by two approaches, mediation and moderation. Yet the relative significance of each approach remains unclear in respective research. We set out to study this question by conducting a systematic literature review. We included all original papers based on prospective observational studies of employed cohorts that were published between January 1980 and October 2012 meeting our search criteria, by using major databases and by observing established quality criteria. 26 reports were included after quality assessment. 17 studies examined the mediation hypothesis and nine studies tested the moderation hypothesis. Moderate support was found for the mediation hypothesis where OR or HR of health according to socioeconomic position (SEP) were reduced in a majority of analyses after introducing work characteristics in multivariate models. Evidence in favour of the moderation hypothesis was found in some studies, demonstrating stronger effects of adverse work on health among people with low SEP. Despite some support in favour of the two hypotheses future research should aim at reducing the heterogeneity in defining and measuring core variables and at applying advanced statistical analyses. Policy recommendations would benefit from a higher degree of consistency of respective research evidence.

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Treesearch

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

Season of birth is different in Inuit suicide victims born into Traditional than into Modern Lifestyle: a register study from Greenland.

PubMed

Björkstén, Karin S; Bjerregaard, Peter

2015-07-04

There is growing evidence that living conditions at birth play a role in medical conditions later in life. Population-based studies from the Northern Hemisphere have shown that persons born in the spring or summer are at greater risk of committing suicide. A statistical correlation with light availability at birth has been observed in past research, but the cause remains unknown. Greenland is one of the most extreme of natural human habitats with regard to seasonal changes in light. The combination of rapid social changes and reliable population statistics offers a unique opportunity to make comparisons between persons born into a Traditional Lifestyle and those born into a Modern Lifestyle. The aim of this work was to assess whether season of birth differed between suicide victims born into an old or into a modern lifestyle. Official population and mortality registers were used. Suicide victims born (1903-1950) into the Traditional Lifestyle were compared with those born into the Modern Lifestyle (1961-1980). Rayleigh's test for circular distributions was used to assess the season of birth in suicide victims. Data regarding season of birth in the general population were collected. Persons born in March-June in the Traditional Lifestyle were much less likely to commit suicide than those born during other periods of the year. This is contrary to the findings of other studies. The seasonal differences had disappeared for those born into the Modern Lifestyle. The suicide rate increased from very low rates to about 140 suicides/100 000 person-years in the 1980s. The reason behind a variation in season of birth in suicide victims born into the old lifestyle is unknown. It is also unknown why the seasonal difference had disappeared with modern lifestyle. Possible influence of artificial light, nutrition, microbiota and seasonal infections are discussed. The underlying causes behind suicides may be different in traditional and modern Greenland.

Charles E. Land, Ph.D., acclaimed statistical expert on radiation risk assessment, died January 2018

Cancer.gov

Charles E. Land, Ph.D., an internationally acclaimed statistical expert on radiation risk assessment, died January 25, 2018. He retired in 2009 from the NCI Division of Cancer Epidemiology and Genetics. Dr. Land performed pioneering work in modern radiation dose-response analysis and modeling of low-dose cancer risk.

The Statistics of a Function

ERIC Educational Resources Information Center

Gordon, Sheldon P.; Gordon, Florence S.

2010-01-01

One of the most important applications of the definite integral in a modern calculus course is the mean value of a function. Thus, if a function "f" is defined on an interval ["a", "b"], then the mean, or average value, of "f" is given by [image omitted]. In this note, we will investigate the meaning of other statistics associated with a function…

Pedagogical Utilization and Assessment of the Statistic Online Computational Resource in Introductory Probability and Statistics Courses

ERIC Educational Resources Information Center

Dinov, Ivo D.; Sanchez, Juana; Christou, Nicolas

2008-01-01

Technology-based instruction represents a new recent pedagogical paradigm that is rooted in the realization that new generations are much more comfortable with, and excited about, new technologies. The rapid technological advancement over the past decade has fueled an enormous demand for the integration of modern networking, informational and…

Improving Data Analysis in Second Language Acquisition by Utilizing Modern Developments in Applied Statistics

ERIC Educational Resources Information Center

Larson-Hall, Jenifer; Herrington, Richard

2010-01-01

In this article we introduce language acquisition researchers to two broad areas of applied statistics that can improve the way data are analyzed. First we argue that visual summaries of information are as vital as numerical ones, and suggest ways to improve them. Specifically, we recommend choosing boxplots over barplots and adding locally…

Quantifying falsifiability of scientific theories

NASA Astrophysics Data System (ADS)

Nemenman, Ilya

I argue that the notion of falsifiability, a key concept in defining a valid scientific theory, can be quantified using Bayesian Model Selection, which is a standard tool in modern statistics. This relates falsifiability to the quantitative version of the statistical Occam's razor, and allows transforming some long-running arguments about validity of scientific theories from philosophical discussions to rigorous mathematical calculations.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

[Statistical prediction methods in violence risk assessment and its application].

PubMed

Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

2013-06-01

It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Varying family planning strategies across age categories: differences in factors associated with current modern contraceptive use among youth and adult women in Luanda, Angola.

PubMed

Prata, Ndola; Bell, Suzanne; Weidert, Karen; Nieto-Andrade, Benjamin; Carvalho, Adelaide; Neves, Isilda

2016-01-01

The objective of this study is to identify factors associated with current modern contraceptive use among Angolan women. By differentiating according to age groups (15-24 and 25-49 years), this study aimed to help family planning program planners better tailor interventions to improve utilization of modern contraception. A household survey was used to collect data from 1,545 women of reproductive age living in Luanda Province, Angola. Data on sociodemographic characteristics, reproductive behavior and intentions, contraceptive knowledge and use, and attitudes and beliefs regarding contraception and abortion were collected. The analyses were stratified based on age: 15-24 years (youth) and 25-49 years (adult). Multivariate logistic regression models were built for each age group, adding different subsets of variables in groups to see how relationships changed across the models. Common factors associated with modern contraceptive use among all ages include education level, perceived contraceptive accessibility, contraceptive knowledge, communication with partner about family planning in last year, and self-efficacy. Exposure to family planning information in the media in the last few months, perceived partner approval of family planning, and marital status were all positively associated with current modern contraceptive use among women aged 15-24 years. Meanwhile, receiving information about family planning from a pharmacy in the last year was uniquely associated with current modern contraceptive use among women aged 25-49 years. Young women in Luanda, Angola seem to have a unique set of factors affecting their contraceptive use. These findings highlight the need for family planning programs to cater services and messages toward specific age groups.

Effect of an expansion in private sector provision of contraceptive supplies on horizontal inequity in modern contraceptive use: evidence from Africa and Asia

PubMed Central

2011-01-01

Background One strategic approach available to policy makers to improve the availability of reproductive and child health care supplies and services as well as the sustainability of programs is to expand the role of the private sector in providing these services. However, critics of this approach argue that increased reliance on the private sector will not serve the needs of the poor, and could lead to increases in socio-economic disparities in the use of health care services. The purpose of this study is to investigate whether the expansion of the role of private providers in the provision of modern contraceptive supplies is associated with increased horizontal inequity in modern contraceptive use. Methods The study is based on multiple rounds of Demographic and Health Survey data from four selected countries (Nigeria, Uganda, Bangladesh, and Indonesia) in which there was an increase in the private sector supply of contraceptives. The methodology involves estimating concentration indices to assess the degree of inequality and inequity in contraceptive use by wealth groups across time. In order to measure inequity in the use of modern contraceptives, the study uses multivariate methods to control for differences in the need for family planning services in relation to household wealth. Results The results suggest that the expansion of the private commercial sector supply of contraceptives in the four study countries did not lead to increased inequity in the use of modern contraceptives. In Nigeria and Uganda, inequity actually decreased over time; while in Bangladesh and Indonesia, inequity fluctuated. Conclusions The study results do not offer support to the hypothesis that the increased role of the private commercial sector in the supply of contraceptive supplies led to increased inequity in modern contraceptive use. PMID:21854584

Effect of an expansion in private sector provision of contraceptive supplies on horizontal inequity in modern contraceptive use: evidence from Africa and Asia.

PubMed

Hotchkiss, David R; Godha, Deepali; Do, Mai

2011-08-19

One strategic approach available to policy makers to improve the availability of reproductive and child health care supplies and services as well as the sustainability of programs is to expand the role of the private sector in providing these services. However, critics of this approach argue that increased reliance on the private sector will not serve the needs of the poor, and could lead to increases in socio-economic disparities in the use of health care services. The purpose of this study is to investigate whether the expansion of the role of private providers in the provision of modern contraceptive supplies is associated with increased horizontal inequity in modern contraceptive use. The study is based on multiple rounds of Demographic and Health Survey data from four selected countries (Nigeria, Uganda, Bangladesh, and Indonesia) in which there was an increase in the private sector supply of contraceptives. The methodology involves estimating concentration indices to assess the degree of inequality and inequity in contraceptive use by wealth groups across time. In order to measure inequity in the use of modern contraceptives, the study uses multivariate methods to control for differences in the need for family planning services in relation to household wealth. The results suggest that the expansion of the private commercial sector supply of contraceptives in the four study countries did not lead to increased inequity in the use of modern contraceptives. In Nigeria and Uganda, inequity actually decreased over time; while in Bangladesh and Indonesia, inequity fluctuated. The study results do not offer support to the hypothesis that the increased role of the private commercial sector in the supply of contraceptive supplies led to increased inequity in modern contraceptive use.

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Evaluation of Facility Management by Multivariate Statistics - Factor Analysis

NASA Astrophysics Data System (ADS)

Singovszki, Miloš; Vranayová, Zuzana

2013-06-01

Facility management is evolving, there is no exact than other sciences, although its development is fast forward. The knowledge and practical skills in facility management is not replaced, on the contrary, they complement each other. The existing low utilization of science in the field of facility management is mainly caused by the management of support activities are many variables and prevailing immediate reaction to the extraordinary situation arising from motives of those who have substantial experience and years of proven experience. Facility management is looking for a system that uses organized knowledge and will form the basis, which grows from a wide range of disciplines. Significant influence on its formation as a scientific discipline is the "structure, which follows strategy". The paper deals evaluate technology building as part of an facility management by multivariate statistic - factor analysis.

Multivariate statistics applied to the reaction of common bean plants to parasitism by Meloidogyne javanica.

PubMed

Santos, L N S; Cabral, P D S; Neves, G A R; Alves, F R; Teixeira, M B; Cunha, F N; Silva, N F

2017-03-16

The availability of common bean cultivars tolerant to Meloidogyne javanica is limited in Brazil. Thus, the present study aimed to evaluate the reactions of 33 common bean genotypes (23 landrace, 8 commercial, 1 susceptible standard and 1 resistant standard) to M. javanica, employing multivariate statistics to discriminate the reaction of the genotypes. The experiment was conducted in a greenhouse using a completely randomized design with seven replicates. The seeds were sown in 1-L pots containing autoclaved soil and sand in a 1:1 ratio (v:v). On day 19, after emergence of the seedlings, the plants were treated with inoculum containing 4000 eggs + second-stage juveniles (J2). At 60 days after inoculation, the seedlings were evaluated based on biometric and parasitism-related traits, such as number of galls, final nematode population per root system, reproduction factor, and percent reduction in the reproduction factor of the nematode (%RRF). The data were subjected to analysis of variance using the F-test. The Mahalanobis generalized distance was used to obtain the dissimilarity matrix, and the average linkage between groups was used for clustering. The use of multivariate statistics allowed groups to be separated according to the resistance levels of genotypes, as observed in the %RRF. The landrace genotypes FORT-09, FORT-17, FORT-31, FORT-32, FORT-34 and FORT-36 presented resistance to M. javanica; thus, these genotypes can be considered potential sources of resistance.

Peripheral vascular damage in systemic lupus erythematosus: data from LUMINA, a large multi-ethnic U.S. cohort (LXIX).

PubMed

Burgos, P I; Vilá, L M; Reveille, J D; Alarcón, G S

2009-12-01

To determine the factors associated with peripheral vascular damage in systemic lupus erythematosus patients and its impact on survival from Lupus in Minorities, Nature versus Nurture, a longitudinal US multi-ethnic cohort. Peripheral vascular damage was defined by the Systemic Lupus International Collaborating Clinics Damage Index (SDI). Factors associated with peripheral vascular damage were examined by univariable and multi-variable logistic regression models and its impact on survival by a Cox multi-variable regression. Thirty-four (5.3%) of 637 patients (90% women, mean [SD] age 36.5 [12.6] [16-87] years) developed peripheral vascular damage. Age and the SDI (without peripheral vascular damage) were statistically significant (odds ratio [OR] = 1.05, 95% confidence interval [CI] 1.01-1.08; P = 0.0107 and OR = 1.30, 95% CI 0.09-1.56; P = 0.0043, respectively) in multi-variable analyses. Azathioprine, warfarin and statins were also statistically significant, and glucocorticoid use was borderline statistically significant (OR = 1.03, 95% CI 0.10-1.06; P = 0.0975). In the survival analysis, peripheral vascular damage was independently associated with a diminished survival (hazard ratio = 2.36; 95% CI 1.07-5.19; P = 0.0334). In short, age was independently associated with peripheral vascular damage, but so was the presence of damage in other organs (ocular, neuropsychiatric, renal, cardiovascular, pulmonary, musculoskeletal and integument) and some medications (probably reflecting more severe disease). Peripheral vascular damage also negatively affected survival.

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

Characterization of Italian honeys (Marche Region) on the basis of their mineral content and some typical quality parameters

PubMed Central

Conti, Marcelo Enrique; Stripeikis, Jorge; Campanella, Luigi; Cucina, Domenico; Tudino, Mabel Beatriz

2007-01-01

Background The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined. Results Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe. Conclusion In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification. PMID:17880749

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

PubMed

Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

2017-02-01

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Craniofacial morphology in ancient and modern Greeks through 4,000 years.

PubMed

Papagrigorakis, Manolis J; Kousoulis, Antonis A; Synodinos, Philippos N

2014-01-01

Multiple 20th century studies have speculated on the anthropological similarities of the modern inhabitants of Greece with their ancient predecessors. The present investigation attempts to add to this knowledge by comparing the craniofacial configuration of 141 ancient (dating around 2,000-500 BC) and 240 modern Greek skulls (the largest material among relevant national studies). Skulls were grouped in age at death, sex, era and geographical categories; lateral cephalograms were taken and 53 variables were measured and correlated statistically. The craniofacial measurements and measurements of the basic quadrilateral and cranial polygon were compared in various groups using basic statistical methods, one-way ANOVA and assessment of the correlation matrices. Most of the measurements for both sexes combined followed an akin pattern in ancient and modern Greek skulls. Moreover, sketching and comparing the outline of the skull and upper face, we observed a clock-wise movement. The present study confirms that the morphological pattern of Greek skulls, as it changed during thousands of years, kept some characteristics unchanged, with others undergoing logical modifications. The analysis of our results allows us to believe that the influence upon the craniofacial complex of the various known factors, including genetic or environmental alterations, is apt to alter its form to adapt to new conditions. Even though 4,000 years seems too narrow a span to provoke evolutionary insights using conventional geometric morphometrics, the full presentation of our results makes up a useful atlas of solid data. Interpreted with caution, the craniofacial morphology in modern and ancient Greeks indicates elements of ethnic group continuation within the unavoidable multicultural mixtures.

Portable XRF and principal component analysis for bill characterization in forensic science.

PubMed

Appoloni, C R; Melquiades, F L

2014-02-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

[Analysis of variance of repeated data measured by water maze with SPSS].

PubMed

Qiu, Hong; Jin, Guo-qin; Jin, Ru-feng; Zhao, Wei-kang

2007-01-01

To introduce the method of analyzing repeated data measured by water maze with SPSS 11.0, and offer a reference statistical method to clinical and basic medicine researchers who take the design of repeated measures. Using repeated measures and multivariate analysis of variance (ANOVA) process of the general linear model in SPSS and giving comparison among different groups and different measure time pairwise. Firstly, Mauchly's test of sphericity should be used to judge whether there were relations among the repeatedly measured data. If any (P

A model-based approach to wildland fire reconstruction using sediment charcoal records

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; Hooten, Mevin B.; Higuera, Philip E.; Marlon, Jennifer R.; Kelly, Ryan; McLachlan, Jason S.

2017-01-01

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Comparison of US Antarctic Meteorite Collection to Other Cold and Hot Deserts and Modern Falls

NASA Technical Reports Server (NTRS)

McBride, K. M.; Righter, K.

2010-01-01

The US Antarctic meteorite collection has grown close to 18,000 specimens, over 16,000 of which have been classified. Because of this growth, the parallel growth of Antarctic meteorite collections by Japan and China, and also the hot desert collections (from Africa and Australia), we will update the statistical overview of the US collection (last done in 1990 [1]), and make comparisons to other collections and modern falls.

Assessing the impacts of socio-economic and hydrological factors on urban water demand: A multivariate statistical approach

NASA Astrophysics Data System (ADS)

Panagopoulos, George P.

2014-10-01

The multivariate statistical techniques conducted on quarterly water consumption data in Mytilene reveal valuable tools that could help the local authorities in assigning strategies aimed at the sustainable development of urban water resources. The proposed methodology is an innovative approach, applied for the first time in the international literature, to handling urban water consumption data in order to analyze statistically the interrelationships among the determinants of urban water use. Factor analysis of demographic, socio-economic and hydrological variables shows that total water consumption in Mytilene is the combined result of increases in (a) income, (b) population, (c) connections and (d) climate parameters. On the other hand, the per connection water demand is influenced by variations in water prices but with different consequences in each consumption class. Increases in water prices are faced by large consumers; they then reduce their consumption rates and transfer to lower consumption blocks. These shifts are responsible for the increase in the average consumption values in the lower blocks despite the increase in the marginal prices.

Statistical methods in personality assessment research.

PubMed

Schinka, J A; LaLone, L; Broeckel, J A

1997-06-01

Emerging models of personality structure and advances in the measurement of personality and psychopathology suggest that research in personality and personality assessment has entered a stage of advanced development, in this article we examine whether researchers in these areas have taken advantage of new and evolving statistical procedures. We conducted a review of articles published in the Journal of Personality, Assessment during the past 5 years. Of the 449 articles that included some form of data analysis, 12.7% used only descriptive statistics, most employed only univariate statistics, and fewer than 10% used multivariate methods of data analysis. We discuss the cost of using limited statistical methods, the possible reasons for the apparent reluctance to employ advanced statistical procedures, and potential solutions to this technical shortcoming.

Late Quaternary vegetation, biodiversity and fire dynamics on the southern Brazilian highland and their implication for conservation and management of modern Araucaria forest and grassland ecosystems.

PubMed

Behling, Hermann; Pillar, Valério DePatta

2007-02-28

Palaeoecological background information is needed for management and conservation of the highly diverse mosaic of Araucaria forest and Campos (grassland) in southern Brazil. Questions on the origin of Araucaria forest and grasslands; its development, dynamic and stability; its response to environmental change such as climate; and the role of human impact are essential. Further questions on its natural stage of vegetation or its alteration by pre- and post-Columbian anthropogenic activity are also important. To answer these questions, palaeoecological and palaeoenvironmental data based on pollen, charcoal and multivariate data analysis of radiocarbon dated sedimentary archives from southern Brazil are used to provide an insight into past vegetation changes, which allows us to improve our understanding of the modern vegetation and to develop conservation and management strategies for the strongly affected ecosystems in southern Brazil.

Sociocultural influences on body dissatisfaction and dieting in Hong Kong girls.

PubMed

Lam, T H; Lee, Stephanie W; Fung, Samantha; Ho, S Y; Lee, Peter W H; Stewart, Sunita M

2009-03-01

We investigated the relationship of sociocultural influences (SI) promoting thinness (parental, peer and media pressures for thinness, and individual value for modernity), age and body mass Index (BMI) to body dissatisfaction (BD) and dieting in 294 Hong Kong community adolescent girls. We proposed that BD mediated SI's relationship with dieting. In bivariate analyses, all variables were significantly (p < or = .05) related to BD (beta's from 0.14 to 0.59), and, except for modernity, to dieting (beta's from 0.17 to 0.51). In multivariate analyses, peer (beta = 0.32, p < .001) and media pressures for thinness (beta = 0.18, p < .01) bypassed BD and were directly associated with dieting. A culture of thinness appears to be associated with weight loss efforts among girls in modernising cultures independent of BD. Our findings call for public policy to restrict promotion of the impossibly thin ideal, and public education regarding the paradoxical effects of dieting.

Basic concepts of depression

PubMed Central

Paykel, Eugene S.

2008-01-01

This paper reviews concepts of depression, including history and classification. The original broad concept of melancholia included all forms of quiet insanity. The term depression began to appear in the nineteenth century as did the modern concept of affective disorders, with the core disturbance now viewed as one of mood. The 1930s saw the introduction of defined criteria into official diagnostic schemes. The modern separation into unipolar and bipolar disorder was introduced following empirical research by Angst and Perris in the 1960s. The partially overlapping distinctions between psychotic and neurotic depression, and between endogenous and reactive depression, started to generate debate in the 1920s, with considerable multivariate research in the 1960s. The symptom element in endogenous depression currently survives in melancholia or somatic syndrome. Life stress is common in various depressive pictures. Dysthymia, a valuable diagnosis, represents a form of what was regarded earlier as neurotic depression. Other subtypes are also discussed. PMID:18979941

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

The Driving Forces of Cultural Complexity : Neanderthals, Modern Humans, and the Question of Population Size.

PubMed

Fogarty, Laurel; Wakano, Joe Yuichiro; Feldman, Marcus W; Aoki, Kenichi

2017-03-01

The forces driving cultural accumulation in human populations, both modern and ancient, are hotly debated. Did genetic, demographic, or cognitive features of behaviorally modern humans (as opposed to, say, early modern humans or Neanderthals) allow culture to accumulate to its current, unprecedented levels of complexity? Theoretical explanations for patterns of accumulation often invoke demographic factors such as population size or density, whereas statistical analyses of variation in cultural complexity often point to the importance of environmental factors such as food stability, in determining cultural complexity. Here we use both an analytical model and an agent-based simulation model to show that a full understanding of the emergence of behavioral modernity, and the cultural evolution that has followed, depends on understanding and untangling the complex relationships among culture, genetically determined cognitive ability, and demographic history. For example, we show that a small but growing population could have a different number of cultural traits from a shrinking population with the same absolute number of individuals in some circumstances.

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System.

PubMed

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure ( P <0.05). Multivariate logistic regression analysis showed no statistically significant relationship ( P >0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant ( P <0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System

PubMed Central

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D.; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

AIM To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. METHODS The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. RESULTS Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure (P<0.05). Multivariate logistic regression analysis showed no statistically significant relationship (P>0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant (P<0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. CONCLUSION After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y. PMID:28393027

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer

PubMed Central

Church, David N.; Stelloo, Ellen; Nout, Remi A.; Valtcheva, Nadejda; Depreeuw, Jeroen; ter Haar, Natalja; Noske, Aurelia; Amant, Frederic; Wild, Peter J.; Lambrechts, Diether; Jürgenliemk-Schulz, Ina M.; Jobsen, Jan J.; Smit, Vincent T. H. B. M.; Creutzberg, Carien L.; Bosse, Tjalling

2015-01-01

Background: Current risk stratification in endometrial cancer (EC) results in frequent over- and underuse of adjuvant therapy, and may be improved by novel biomarkers. We examined whether POLE proofreading mutations, recently reported in about 7% of ECs, predict prognosis. Methods: We performed targeted POLE sequencing in ECs from the PORTEC-1 and -2 trials (n = 788), and analyzed clinical outcome according to POLE status. We combined these results with those from three additional series (n = 628) by meta-analysis to generate multivariable-adjusted, pooled hazard ratios (HRs) for recurrence-free survival (RFS) and cancer-specific survival (CSS) of POLE-mutant ECs. All statistical tests were two-sided. Results: POLE mutations were detected in 48 of 788 (6.1%) ECs from PORTEC-1 and-2 and were associated with high tumor grade (P < .001). Women with POLE-mutant ECs had fewer recurrences (6.2% vs 14.1%) and EC deaths (2.3% vs 9.7%), though, in the total PORTEC cohort, differences in RFS and CSS were not statistically significant (multivariable-adjusted HR = 0.43, 95% CI = 0.13 to 1.37, P = .15; HR = 0.19, 95% CI = 0.03 to 1.44, P = .11 respectively). However, of 109 grade 3 tumors, 0 of 15 POLE-mutant ECs recurred, compared with 29 of 94 (30.9%) POLE wild-type cancers; reflected in statistically significantly greater RFS (multivariable-adjusted HR = 0.11, 95% CI = 0.001 to 0.84, P = .03). In the additional series, there were no EC-related events in any of 33 POLE-mutant ECs, resulting in a multivariable-adjusted, pooled HR of 0.33 for RFS (95% CI = 0.12 to 0.91, P = .03) and 0.26 for CSS (95% CI = 0.06 to 1.08, P = .06). Conclusion: POLE proofreading mutations predict favorable EC prognosis, independently of other clinicopathological variables, with the greatest effect seen in high-grade tumors. This novel biomarker may help to reduce overtreatment in EC. PMID:25505230

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

Heavy metals in soils of Hechuan County in the upper Yangtze (SW China): Comparative pollution assessment using multiple indices with high-spatial-resolution sampling.

PubMed

Ni, Maofei; Mao, Rong; Jia, Zhongmin; Dong, Ruozhu; Li, Siyue

2018-02-01

In order to assess heavy metals (HMs) in soils of the upper Yangtze Basin, a very high-spatial-resolution sampling (582 soil samples) was conducted from Hechuan County, an important agricultural practice area in the Southwest China. Multiple indices including geoaccumulation index (I geo ), enrichment factor (EF), sediment pollution index (SPI) and risk index (RI), as well as multivariate statistics were employed for pollution assessment and source identification of HMs in soils. Our results demonstrated that the averages of eight HMs decreased in the following order: Zn (82.8 ± 15.9) > Cr (71.6 ± 12.2) > Ni (32.1 ± 9.89) > Pb (27.6 ± 13.8) > Cu (25.9 ± 11.8) > As (5.48 ± 3.42) > Cd (0.30 ± 0.077) > Hg (0.082 ± 0.092). Averages of HMs except Cd were lower than threshold value of Environmental Quality Standard for Soils, while 43% of total samples had Cd concentration exceeding the national standard, 1% of samples for Hg and 5% samples for Ni, moreover, Cd and Hg averages were much higher than their background levels. I geo and EF indicated that their levels decreased as follows: Cd > Hg > Zn > Pb > Ni > Cu > Cr > As, with moderate enrichments of Cd and Hg. RI indicated that 61.7% of all samples showed moderate risk, while 6.5% of samples with greater than considerable risk due to human activities should be paid more attention. Multivariate analysis showed lithogenic source of Cu, Cr, Ni and Zn, while Cd and Hg were largely contributed by anthropogenic activities such as agricultural practices. Our study would be helpful for improving soil environmental quality in SW, China, as well as supplying modern approaches for other areas with soil HM pollution. Copyright © 2017 Elsevier Inc. All rights reserved.

Sexual and Reproductive Health Knowledge, Contraception Uptake, and Factors Associated with Unmet Need for Modern Contraception among Adolescent Female Sex Workers in China

PubMed Central

Lim, Megan S. C.; Zhang, Xu-Dong; Kennedy, Elissa; Li, Yan; Yang, Yin; Li, Lin; Li, Yun-Xia; Temmerman, Marleen; Luchters, Stanley

2015-01-01

Objective In China, policy and social taboo prevent unmarried adolescents from accessing sexual and reproductive health (SRH) services. Research is needed to determine the SRH needs of highly disadvantaged groups, such as adolescent female sex workers (FSWs). This study describes SRH knowledge, contraception use, pregnancy, and factors associated with unmet need for modern contraception among adolescent FSWs in Kunming, China. Methods A cross-sectional study using a one-stage cluster sampling method was employed to recruit adolescents aged 15 to 20 years, and who self-reported having received money or gifts in exchange for sex in the past 6 months. A semi-structured questionnaire was administered by trained peer educators or health workers. Multivariable logistic regression was conducted to determine correlates of low knowledge and unmet need for modern contraception. Results SRH knowledge was poor among the 310 adolescents surveyed; only 39% had heard of any long-acting reversible contraception (implant, injection or IUD). Despite 98% reporting not wanting to get pregnant, just 43% reported consistent condom use and 28% currently used another form of modern contraception. Unmet need for modern contraception was found in 35% of adolescents, and was associated with having a current non-paying partner, regular alcohol use, and having poorer SRH knowledge. Past abortion was common (136, 44%). In the past year, 76% had reported a contraception consultation but only 27% reported ever receiving SRH information from a health service. Conclusions This study demonstrated a low level of SRH knowledge, a high unmet need for modern contraception and a high prevalence of unintended pregnancy among adolescent FSWs in Kunming. Most girls relied on condoms, emergency contraception, or traditional methods, putting them at risk of unwanted pregnancy. This study identifies an urgent need for Chinese adolescent FSWs to be able to access quality SRH information and effective modern contraception. PMID:25625194

Factor associated with experience of modern contraceptive use before pregnancy among women who gave birth in Kersa HDSS, Ethiopia.

PubMed

Musa, Abdulbasit; Assefa, Nega; Weldegebreal, Fitsum; Mitiku, Habtamu; Teklemariam, Zelalem

2016-07-22

Worldwide, every year 289,000 women die related to pregnancy and its complications. Nearly, all of these deaths occur in developing countries and more than half of this deaths occur in sub-Saharan Africa. Report suggested that using contraceptives can reduce this maternal mortality by 44 %. Even if, Ethiopia is one of the countries with highest maternal mortality, only 41 % of married women are using family planning. This analysis aimed at assessing factor associated with experience of contraceptive use before pregnancy among women who gave birth in Kersa Health and Demographic Surveillance System, Ethiopia. This study was part of data generated for Kersa Health and Demographic Surveillance System. Women who gave birth during October 2011 to September 2012 were asked whether they had used contraceptive before getting their last pregnancy. Data were collected by using Kersa Health and Demographic Surveillance System questionnaire. Both bi-variate and multivariate analysis were used to identify associated factors. The proportion of modern contraceptive before pregnancy among the study participants was found to be 383 (40.9 %). The most commonly used modern contraceptives was Injectable contraceptive 270 (70.0 %) followed by oral contraceptives, 66 (17.23 %). Modern contraceptive use was negatively association with being Muslim (AOR = 0.2, 95 % CI = 0.05, 0.72) and being young mother (AOR = 0.44, 95 % CI = 0.22, 0.86). Rural town residence (AOR = 2.23, 95 % CI = 1.15, 4.35) was found to have positive association with utilization of modern contraceptives. Among women giving birth, only a minority had attempted to delay or prevent their recent birth by using contraception. Being young, being Muslim and living in rural area were significantly associated with low utilization of modern contraceptive. Increasing family planning education and involving religious leaders in family planning promotion would improve utilization of modern contraceptive use.

Multivariate evoked response detection based on the spectral F-test.

PubMed

Rocha, Paulo Fábio F; Felix, Leonardo B; Miranda de Sá, Antonio Mauricio F L; Mendes, Eduardo M A M

2016-05-01

Objective response detection techniques, such as magnitude square coherence, component synchrony measure, and the spectral F-test, have been used to automate the detection of evoked responses. The performance of these detectors depends on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. Recently, multivariate detectors were developed to increase the detection rate even in the case of a low signal-to-noise ratio or of short data records originated from EEG signals. In this context, an extension to the multivariate case of the spectral F-test detector is proposed. The performance of this technique is assessed using Monte Carlo. As an example, EEG data from 12 subjects during photic stimulation is used to demonstrate the usefulness of the proposed detector. The multivariate method showed detection rates consistently higher than those ones when only one signal was used. It is shown that the response detection in EEG signals with the multivariate technique was statistically significant if two or more EEG derivations were used. Copyright © 2016 Elsevier B.V. All rights reserved.

Metal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: environmental management and policy/decision-making tools.

PubMed

Cavalcante, Y L; Hauser-Davis, R A; Saraiva, A C F; Brandão, I L S; Oliveira, T F; Silveira, A M

2013-01-01

This paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate statistical characterization of charged and uncharged domain walls in multiferroic hexagonal YMnO3 single crystal visualized by a spherical aberration-corrected STEM.

PubMed

Matsumoto, Takao; Ishikawa, Ryo; Tohei, Tetsuya; Kimura, Hideo; Yao, Qiwen; Zhao, Hongyang; Wang, Xiaolin; Chen, Dapeng; Cheng, Zhenxiang; Shibata, Naoya; Ikuhara, Yuichi

2013-10-09

A state-of-the-art spherical aberration-corrected STEM was fully utilized to directly visualize the multiferroic domain structure in a hexagonal YMnO3 single crystal at atomic scale. With the aid of multivariate statistical analysis (MSA), we obtained unbiased and quantitative maps of ferroelectric domain structures with atomic resolution. Such a statistical image analysis of the transition region between opposite polarizations has confirmed atomically sharp transitions of ferroelectric polarization both in antiparallel (uncharged) and tail-to-tail 180° (charged) domain boundaries. Through the analysis, a correlated subatomic image shift of Mn-O layers with that of Y layers, exhibiting a double-arc shape of reversed curvatures, have been elucidated. The amount of image shift in Mn-O layers along the c-axis is statistically significant as small as 0.016 nm, roughly one-third of the evident image shift of 0.048 nm in Y layers. Interestingly, a careful analysis has shown that such a subatomic image shift in Mn-O layers vanishes at the tail-to-tail 180° domain boundaries. Furthermore, taking advantage of the annular bright field (ABF) imaging technique combined with MSA, the tilting of MnO5 bipyramids, the very core mechanism of multiferroicity of the material, is evaluated.

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Influence of internal migration on reproductive health in Myanmar: results from a recent cross-sectional survey.

PubMed

Sudhinaraset, May; Diamond-Smith, Nadia; Thet, May Me; Aung, Tin

2016-03-09

Maternal and reproductive health remains a significant public health issue in Myanmar. Little data exists on women's health issues, including social and demographic influences. While past studies have demonstrated rural/urban health disparities, an increasingly important population resulting from urban growth in Myanmar is the internal migrant population, individuals moving within the country for better job or educational opportunities. Past studies suggest that women make up more than half of internal migrants, yet there is a dearth of information on this new wave of migration, particularly on women's reproductive health issues. The objective of this study is to assess the influence of women's migration in Myanmar on reproductive health outcomes, including delivering in a facility, using a skilled birth attendant, and using a modern method of family planning. Data from a cross-sectional household survey using multistage cluster sampling design conducted between September to October 2014 was used to assess the accessibility and the use of maternal and child health products and services. A total of 1800 currently married women of reproductive age, including 348 from urban and 1452 from rural areas, were recruited to complete surveys. A set of multivariable regressions was performed to assess reproductive health outcomes and predictors. Across health indicators, female migrants had better health outcomes compared to non-migrants. Controlling for demographic characteristics, migrants were 1.60 times more likely to use a modern form of family planning compared to non-migrants (p < 0.01) and use antenatal care during pregnancy (p < 0.05). While not statistically significant, migrants were 1.29 times more likely to deliver with a skilled attendant and 1.08 times more likely to deliver in a facility. This study found that female migrants in Myanmar reported better health outcomes compared to non-migrant women in regards to family planning and maternal health. Future research should focus on monitoring the outcomes of migrants and their children over time to assess long-term impacts.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

1995-01-01

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prolonged instability prior to a regime shift

USGS Publications Warehouse

Spanbauer, Trisha; Allen, Craig R.; Angeler, David G.; Eason, Tarsha; Fritz, Sherilyn C.; Garmestani, Ahjond S.; Nash, Kirsty L.; Stone, Jeffery R.

2014-01-01

Regime shifts are generally defined as the point of ‘abrupt’ change in the state of a system. However, a seemingly abrupt transition can be the product of a system reorganization that has been ongoing much longer than is evident in statistical analysis of a single component of the system. Using both univariate and multivariate statistical methods, we tested a long-term high-resolution paleoecological dataset with a known change in species assemblage for a regime shift. Analysis of this dataset with Fisher Information and multivariate time series modeling showed that there was a∼2000 year period of instability prior to the regime shift. This period of instability and the subsequent regime shift coincide with regional climate change, indicating that the system is undergoing extrinsic forcing. Paleoecological records offer a unique opportunity to test tools for the detection of thresholds and stable-states, and thus to examine the long-term stability of ecosystems over periods of multiple millennia.

Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison.

PubMed

Cardot, J-M; Roudier, B; Schütz, H

2017-07-01

The f 2 test is generally used for comparing dissolution profiles. In cases of high variability, the f 2 test is not applicable, and the Multivariate Statistical Distance (MSD) test is frequently proposed as an alternative by the FDA and EMA. The guidelines provide only general recommendations. MSD tests can be performed either on raw data with or without time as a variable or on parameters of models. In addition, data can be limited-as in the case of the f 2 test-to dissolutions of up to 85% or to all available data. In the context of the present paper, the recommended calculation included all raw dissolution data up to the first point greater than 85% as a variable-without the various times as parameters. The proposed MSD overcomes several drawbacks found in other methods.

Multivariate analysis and geochemical approach for assessment of metal pollution state in sediment cores.

PubMed

Jamshidi-Zanjani, Ahmad; Saeedi, Mohsen

2017-07-01

Vertical distribution of metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, Cd, and Li) in four sediment core samples (C 1 , C 2 , C 3 , and C 4 ) from Anzali international wetland located southwest of the Caspian Sea was examined. Background concentration of each metal was calculated according to different statistical approaches. The results of multivariate statistical analysis showed that Fe and Mn might have significant role in the fate of Ni and Zn in sediment core samples. Different sediment quality indexes were utilized to assess metal pollution in sediment cores. Moreover, a new sediment quality index named aggregative toxicity index (ATI) based on sediment quality guidelines (SQGs) was developed to assess the degree of metal toxicity in an aggregative manner. The increasing pattern of metal pollution and their toxicity degree in upper layers of core samples indicated increasing effects of anthropogenic sources in the study area.

Geographically Sourcing Cocaine's Origin - Delineation of the Nineteen Major Coca Growing Regions in South America.

PubMed

Mallette, Jennifer R; Casale, John F; Jordan, James; Morello, David R; Beyer, Paul M

2016-03-23

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ((2)H and (18)O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Geographically Sourcing Cocaine’s Origin - Delineation of the Nineteen Major Coca Growing Regions in South America

NASA Astrophysics Data System (ADS)

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-03-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Multivariate space - time analysis of PRE-STORM precipitation

NASA Technical Reports Server (NTRS)

Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

1994-01-01

This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

Inference for the physical sciences

PubMed Central

Jones, Nick S.; Maccarone, Thomas J.

2013-01-01

There is a disconnect between developments in modern data analysis and some parts of the physical sciences in which they could find ready use. This introduction, and this issue, provides resources to help experimental researchers access modern data analysis tools and exposure for analysts to extant challenges in physical science. We include a table of resources connecting statistical and physical disciplines and point to appropriate books, journals, videos and articles. We conclude by highlighting the relevance of each of the articles in the associated issue. PMID:23277613

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

NASA Technical Reports Server (NTRS)

Magri, Christopher

1994-01-01

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

PubMed

Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

2017-01-01

The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Time management in acute vertebrobasilar occlusion.

PubMed

Kamper, Lars; Rybacki, Konrad; Mansour, Michael; Winkler, Sven B; Kempkes, Udo; Haage, Patrick

2009-03-01

Acute vertebrobasilar occlusion (VBO) is associated with a high risk of stroke and death. Although local thrombolysis may achieve recanalization and improve outcome, mortality is still between 35% and 75%. However, without recanalization the chance of a good outcome is extremely poor, with mortality rates of 80-90%. Early treatment is a fundamental factor, but detailed studies of the exact time management of the diagnostic and interventional workflow are still lacking. Data on 18 patients were retrospectively evaluated. Time periods between symptom onset, admission to hospital, time of diagnosis, and beginning of intervention were correlated with postinterventional neurological status. The Glasgow Coma Scale and National Institute of Health Stroke Scale (NIHSS) were used to examine patients before and after local thrombolysis. Additionally, multivariate statistics were applied to reveal similarities between patients with neurological improvement. Primary recanalization was achieved in 77% of patients. The overall mortality was 55%. Major complications were intracranial hemorrhage and peripheral embolism. The time period from symptom onset to intervention showed a strong correlation with the postinterventional NIHSS as well as the patient's age, with the best results in a 4-h interval. Multivariate statistics revealed similarities among the patients. Evaluation of time management in acute VBO by multivariate statistics is a helpful tool for definition of similarities in this patient group. Similarly to the door-to-balloon time for acute coronary interventions, the chances for a good outcome depend on a short time interval between symptom onset and intervention. While the only manipulable time period starts with hospital admission, our results emphasize the necessity of efficient intrahospital workflow.

Comparison of Dissolution Similarity Assessment Methods for Products with Large Variations: f2 Statistics and Model-Independent Multivariate Confidence Region Procedure for Dissolution Profiles of Multiple Oral Products.

PubMed

Yoshida, Hiroyuki; Shibata, Hiroko; Izutsu, Ken-Ichi; Goda, Yukihiro

2017-01-01

The current Japanese Ministry of Health Labour and Welfare (MHLW)'s Guideline for Bioequivalence Studies of Generic Products uses averaged dissolution rates for the assessment of dissolution similarity between test and reference formulations. This study clarifies how the application of model-independent multivariate confidence region procedure (Method B), described in the European Medical Agency and U.S. Food and Drug Administration guidelines, affects similarity outcomes obtained empirically from dissolution profiles with large variations in individual dissolution rates. Sixty-one datasets of dissolution profiles for immediate release, oral generic, and corresponding innovator products that showed large variation in individual dissolution rates in generic products were assessed on their similarity by using the f 2 statistics defined in the MHLW guidelines (MHLW f 2 method) and two different Method B procedures, including a bootstrap method applied with f 2 statistics (BS method) and a multivariate analysis method using the Mahalanobis distance (MV method). The MHLW f 2 and BS methods provided similar dissolution similarities between reference and generic products. Although a small difference in the similarity assessment may be due to the decrease in the lower confidence interval for expected f 2 values derived from the large variation in individual dissolution rates, the MV method provided results different from those obtained through MHLW f 2 and BS methods. Analysis of actual dissolution data for products with large individual variations would provide valuable information towards an enhanced understanding of these methods and their possible incorporation in the MHLW guidelines.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

PubMed

Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

2015-01-01

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

PubMed

Matiatos, Ioannis

2016-01-15

Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

Correlative weighted stacking for seismic data in the wavelet domain

USGS Publications Warehouse

Zhang, S.; Xu, Y.; Xia, J.; ,

2004-01-01

Horizontal stacking plays a crucial role for modern seismic data processing, for it not only compresses random noise and multiple reflections, but also provides a foundational data for subsequent migration and inversion. However, a number of examples showed that random noise in adjacent traces exhibits correlation and coherence. The average stacking and weighted stacking based on the conventional correlative function all result in false events, which are caused by noise. Wavelet transform and high order statistics are very useful methods for modern signal processing. The multiresolution analysis in wavelet theory can decompose signal on difference scales, and high order correlative function can inhibit correlative noise, for which the conventional correlative function is of no use. Based on the theory of wavelet transform and high order statistics, high order correlative weighted stacking (HOCWS) technique is presented in this paper. Its essence is to stack common midpoint gathers after the normal moveout correction by weight that is calculated through high order correlative statistics in the wavelet domain. Synthetic examples demonstrate its advantages in improving the signal to noise (S/N) ration and compressing the correlative random noise.

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

Robustness of Multiple Objective Decision Analysis Preference Functions

DTIC Science & Technology

2002-06-01

p p′ : The probability of some event. ,i ip q : The probability of event . i Π : An aggregation of proportional data used in calculating a test ...statistical tests of the significance of the term and also is conducted in a multivariate framework rather than the ROSA univariate approach. A...residual error is ˆ−e = y y (45) The coefficient provides a ready indicator of the contribution for the associated variable and statistical tests

Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis.

PubMed

Whist, A C; Liland, K H; Jonsson, M E; Sæbø, S; Sviland, S; Østerås, O; Norström, M; Hopp, P

2014-11-01

Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T(2) statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds. The use of multivariate statistical process control for selection of herds will be beneficial when a diagnostic test suitable for mass screening is available and validated on the Norwegian cattle population, thus making it possible to increase the number of sampled herds. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multivariate flood risk assessment: reinsurance perspective

NASA Astrophysics Data System (ADS)

Ghizzoni, Tatiana; Ellenrieder, Tobias

2013-04-01

For insurance and re-insurance purposes the knowledge of the spatial characteristics of fluvial flooding is fundamental. The probability of simultaneous flooding at different locations during one event and the associated severity and losses have to be estimated in order to assess premiums and for accumulation control (Probable Maximum Losses calculation). Therefore, the identification of a statistical model able to describe the multivariate joint distribution of flood events in multiple location is necessary. In this context, copulas can be viewed as alternative tools for dealing with multivariate simulations as they allow to formalize dependence structures of random vectors. An application of copula function for flood scenario generation is presented for Australia (Queensland, New South Wales and Victoria) where 100.000 possible flood scenarios covering approximately 15.000 years were simulated.

DEFINITION OF MULTIVARIATE GEOCHEMICAL ASSOCIATIONS WITH POLYMETALLIC MINERAL OCCURRENCES USING A SPATIALLY DEPENDENT CLUSTERING TECHNIQUE AND RASTERIZED STREAM SEDIMENT DATA - AN ALASKAN EXAMPLE.

USGS Publications Warehouse

Jenson, Susan K.; Trautwein, C.M.

1984-01-01

The application of an unsupervised, spatially dependent clustering technique (AMOEBA) to interpolated raster arrays of stream sediment data has been found to provide useful multivariate geochemical associations for modeling regional polymetallic resource potential. The technique is based on three assumptions regarding the compositional and spatial relationships of stream sediment data and their regional significance. These assumptions are: (1) compositionally separable classes exist and can be statistically distinguished; (2) the classification of multivariate data should minimize the pair probability of misclustering to establish useful compositional associations; and (3) a compositionally defined class represented by three or more contiguous cells within an array is a more important descriptor of a terrane than a class represented by spatial outliers.

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

Performance points. The reform club.

PubMed

Edwards, Nick

2004-03-18

The improvement Partnership for Hospitals programme is the vanguard of Modernization Agency work. It is based on statistical process control to eliminate variations in performance, especially in elective service. All starred trusts will join IPH by next April.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

NASA Astrophysics Data System (ADS)

Zakour, Sihem Ben; Taleb, Hassen

2017-09-01

Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Application of meandering centreline migration modelling and object-based approach of Long Nab member

NASA Astrophysics Data System (ADS)

Saadi, Saad

2017-04-01

Characterizing the complexity and heterogeneity of the geometries and deposits in meandering river system is an important concern for the reservoir modelling of fluvial environments. Re-examination of the Long Nab member in the Scalby formation of the Ravenscar Group (Yorkshire, UK), integrating digital outcrop data and forward modelling approaches, will lead to a geologically realistic numerical model of the meandering river geometry. The methodology is based on extracting geostatistics from modern analogous, meandering rivers that exemplify both the confined and non-confined meandering point bars deposits and morphodynamics of Long Nab member. The parameters derived from the modern systems (i.e. channel width, amplitude, radius of curvature, sinuosity, wavelength, channel length and migration rate) are used as a statistical control for the forward simulation and resulting object oriented channel models. The statistical data derived from the modern analogues is multi-dimensional in nature, making analysis difficult. We apply data mining techniques such as parallel coordinates to investigate and identify the important relationships within the modern analogue data, which can then be used drive the development of, and as input to the forward model. This work will increase our understanding of meandering river morphodynamics, planform architecture and stratigraphic signature of various fluvial deposits and features. We will then use these forward modelling based channel objects to build reservoir models, and compare the behaviour of the forward modelled channels with traditional object modelling in hydrocarbon flow simulations.

Validation of inverse seasonal peak mortality in medieval plagues, including the Black Death, in comparison to modern Yersinia pestis-variant diseases.

PubMed

Welford, Mark R; Bossak, Brian H

2009-12-22

Recent studies have noted myriad qualitative and quantitative inconsistencies between the medieval Black Death (and subsequent "plagues") and modern empirical Y. pestis plague data, most of which is derived from the Indian and Chinese plague outbreaks of A.D. 1900+/-15 years. Previous works have noted apparent differences in seasonal mortality peaks during Black Death outbreaks versus peaks of bubonic and pneumonic plagues attributed to Y. pestis infection, but have not provided spatiotemporal statistical support. Our objective here was to validate individual observations of this seasonal discrepancy in peak mortality between historical epidemics and modern empirical data. We compiled and aggregated multiple daily, weekly and monthly datasets of both Y. pestis plague epidemics and suspected Black Death epidemics to compare seasonal differences in mortality peaks at a monthly resolution. Statistical and time series analyses of the epidemic data indicate that a seasonal inversion in peak mortality does exist between known Y. pestis plague and suspected Black Death epidemics. We provide possible explanations for this seasonal inversion. These results add further evidence of inconsistency between historical plagues, including the Black Death, and our current understanding of Y. pestis-variant disease. We expect that the line of inquiry into the disputed cause of the greatest recorded epidemic will continue to intensify. Given the rapid pace of environmental change in the modern world, it is crucial that we understand past lethal outbreaks as fully as possible in order to prepare for future deadly pandemics.

Internet addiction based on personality characteristics of high school students in kerman, iran.

PubMed

Zamani, Bibi Eshrat; Abedini, Yasamin; Kheradmand, Ali

2011-01-01

The new phenomenon of Internet addiction among teenagers and young adults is one of the modern addictions in industrial and post-industrial societies. The purpose of this research was to predict the Internet addiction based on the personality characteristics of high school students in Kerman. This research was a descriptive correlational study. The statistical population included 538 male and female students in the second grade of high school in Kerman during 2010. The subjects were randomly selected by multistage clustering. Data was collected by two questionnaires including the five-factor Revised NEO Personality Inventory and the Internet dependency questionnaire. The data was analyzed using ANOVA test and multivariable regression analysis. The findings showed a significant relationship between the personality trait of emotional stability and academic fields, i.e. students with higher emotional stability experience less negative emotions when confronting with problems. Therefore, it is less likely for them to alleviate the negative emotions by the extreme and obsessed usage of the Internet. In addition, it appears that the students with high extroversion scores prefer social, face to face interactions with other people to interaction with the virtual world. Conversely, more introvert students avoid interactions with other people due to their shyness. Thus, they communicate with the virtual world more. Three personality traits of loyalty, emotional stability, and extroversion are the most significant predictors of Internet addiction in high school students.

Local richness along gradients in the Siskiyou herb flora: R.H. Whittaker revisited

USGS Publications Warehouse

Grace, James B.; Harrison, Susan; Damschen, Ellen Ingman

2011-01-01

In his classic study in the Siskiyou Mountains (Oregon, USA), one of the most botanically rich forested regions in North America, R. H. Whittaker (1960) foreshadowed many modern ideas on the multivariate control of local species richness along environmental gradients related to productivity. Using a structural equation model to analyze his data, which were never previously statistically analyzed, we demonstrate that Whittaker was remarkably accurate in concluding that local herb richness in these late-seral forests is explained to a large extent by three major abiotic gradients (soils, topography, and elevation), and in turn, by the effects of these gradients on tree densities and the numbers of individual herbs. However, while Whittaker also clearly appreciated the significance of large-scale evolutionary and biogeographic influences on community composition, he did not fully articulate the more recent concept that variation in the species richness of local communities could be explained in part by variation in the sizes of regional species pools. Our model of his data is among the first to use estimates of regional species pool size to explain variation in local community richness along productivity-related gradients. We find that regional pool size, combined with a modest number of other interacting abiotic and biotic factors, explains most of the variation in local herb richness in the Siskiyou biodiversity hotspot.

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

Spatial Elucidation of Spinal Cord Lipid- and Metabolite- Regulations in Amyotrophic Lateral Sclerosis

NASA Astrophysics Data System (ADS)

Hanrieder, Jörg; Ewing, Andrew G.

2014-06-01

Amyotrophic lateral sclerosis (ALS) is a devastating, rapidly progressing disease of the central nervous system that is characterized by motor neuron degeneration in the brain stem and the spinal cord. We employed time of flight secondary ion mass spectrometry (ToF-SIMS) to profile spatial lipid- and metabolite- regulations in post mortem human spinal cord tissue from ALS patients to investigate chemical markers of ALS pathogenesis. ToF-SIMS scans and multivariate analysis of image and spectral data were performed on thoracic human spinal cord sections. Multivariate statistics of the image data allowed delineation of anatomical regions of interest based on their chemical identity. Spectral data extracted from these regions were compared using two different approaches for multivariate statistics, for investigating ALS related lipid and metabolite changes. The results show a significant decrease for cholesterol, triglycerides, and vitamin E in the ventral horn of ALS samples, which is presumably a consequence of motor neuron degeneration. Conversely, the biogenic mediator lipid lysophosphatidylcholine and its fragments were increased in ALS ventral spinal cord, pointing towards neuroinflammatory mechanisms associated with neuronal cell death. ToF-SIMS imaging is a promising approach for chemical histology and pathology for investigating the subcellular mechanisms underlying motor neuron degeneration in amyotrophic lateral sclerosis.

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

PubMed

Soltani, Shahla; Asghari Moghaddam, Asghar; Barzegar, Rahim; Kazemian, Naeimeh; Tziritis, Evangelos

2017-08-18

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO 3 , Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na + , K + , Ca 2+ , Mg 2+ , SO 4 2- , and Cl - ), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO 3 - , SiO 2 , and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil.

PubMed

Alves, Darlan Daniel; Riegel, Roberta Plangg; de Quevedo, Daniela Müller; Osório, Daniela Montanari Migliavacca; da Costa, Gustavo Marques; do Nascimento, Carlos Augusto; Telöken, Franko

2018-06-08

Assessment of surface water quality is an issue of currently high importance, especially in polluted rivers which provide water for treatment and distribution as drinking water, as is the case of the Sinos River, southern Brazil. Multivariate statistical techniques allow a better understanding of the seasonal variations in water quality, as well as the source identification and source apportionment of water pollution. In this study, the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA), and positive matrix factorization (PMF) were used, along with the Kruskal-Wallis test and Spearman's correlation analysis in order to interpret a water quality data set resulting from a monitoring program conducted over a period of almost two years (May 2013 to April 2015). The water samples were collected from the raw water inlet of the municipal water treatment plant (WTP) operated by the Water and Sewage Services of Novo Hamburgo (COMUSA). CA allowed the data to be grouped into three periods (autumn and summer (AUT-SUM); winter (WIN); spring (SPR)). Through the PCA, it was possible to identify that the most important parameters in contribution to water quality variations are total coliforms (TCOLI) in SUM-AUT, water level (WL), water temperature (WT), and electrical conductivity (EC) in WIN and color (COLOR) and turbidity (TURB) in SPR. PMF was applied to the complete data set and enabled the source apportionment water pollution through three factors, which are related to anthropogenic sources, such as the discharge of domestic sewage (mostly represented by Escherichia coli (ECOLI)), industrial wastewaters, and agriculture runoff. The results provided by this study demonstrate the contribution provided by the use of integrated statistical techniques in the interpretation and understanding of large data sets of water quality, showing also that this approach can be used as an efficient methodology to optimize indicators for water quality assessment.

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

Using Fisher information to track stability in multivariate systems

EPA Science Inventory

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI...

Controls on Deep Seated Gravitational Slope Deformations in the European Alps

NASA Astrophysics Data System (ADS)

Crosta, Giovanni B.; Frattini, Paolo; Agliardi, Federico

2013-04-01

DSGSDs are very large, slow mass movements affecting entire high-relief valley slopes. The first orogen-scale inventory of such phenomena at has been recently presented for the European Alps (Crosta et al 2008, Agliardi et al 2012), and then further implemented. The inventory includes 1034 Deep Seated Gravitational Slope Deformations, widespread over the entire orogen and clustered along major valleys and in some specific sectors of the Alps. In this contribution we systematically explore lithological, structural and topographic controls on DSGSD distribution with the help of multivariate statistical techniques (Principal Component Analysis, Discriminant Analysis). Analysis units for statistical analysis were obtained by creating three square vector grids with 2.5 km, 5 km and 10 km grid cell size, respectively, covering the entire area (about 110,000 km2). For each grid cell, we calculated the density of DSGSD, and we assigned a value for each of the controlling variable considered in the analysis. From the NASA SRTM (Shuttle Radar Topography Mission) DEM we derived land surface parameters, such as relief, slope gradients, slope aspect, mean vertical distance from base level and ruggedness. The SRTM DEM was also used to extract the drainage density, with a threshold of 1 km2 and 10 km2. We also computer the stream power of the 1km2 river network Lithology was obtained by assembling different geological maps (1:200.000 map of Salzburg, 1:250.000 map of France, 1:500.000 maps of Switzerland and Austria, 1:1.000.000 map of Italy) and by reclassifying the geological units into 8 lithological classes (carbonate rocks, metapelites, sandstones and marls, paragneiss, ortogneiss, flysch-type rocks, granitoid/metabasite, Quaternary units, and volcanic rocks). To study the role of seismicity, we calculated the number of earthquakes (CPTI11 and USGS-NEIC database) within a distance dmax from the square cell, calculated adopting Keefer's (1984) equation, and the sum of Arias Intensities of all earthquakes lying within dmax. Fission-track ages on apatite have been collected from published sources, and interpolated over the entire Alps by using a natural-neighbour interpolator. Finally, the ice thickness during the Last Glacial Maximum, the modern rock uplift, and the mean annual rainfall have been used. Results of the multivariate statistical analysis confirm the results of the previous orogen-scale investigations (Crosta et al., 2008; Agliardi et al., 2012) and shed new light on the relative importance of the (positive or negative) contributions of different controlling factors. The most important controls on DSGSD distribution are: lithology, landscape morphology, LGM ice thickness, modern uplift rate and mean annual rainfall. Lithology is the dominant factor, with units highly favourable (chiefly metapelites, followed by paragneiss and flysch-type rocks) and other unfavourable (especially carbonates rocks) to DSGSD. Landscape morphology plays a role that is difficult to correctly evaluate because of the interplay between morphology and geological and hydrological parameters. DSGSDs are more frequent along main alpine valleys, where long and regular slopes can accommodate these large phenomena, but also where the action of glaciers and the presence of main tectonic lineaments are more important. Favourable landscape morphologies seem also controlled by exhumation and uplift rate. Mean annual rainfall is inversely correlated with DSGSD density. This can be interpreted as the long-term effects of climate in shaping large-scale topography and favouring other types of landslides as players of long-term erosion. Crosta, G.B., Agliardi, F., Frattini, P., Zanchi, A. (2008) Alpine inventory of Deep-Seated Gravitational Slope Deformations. Vol. 10, EGU2008-A-02709, 2008, SRef-ID: 1607-7962/gra/EGU2008-A-0270. Agliardi, F., Crosta, G., Frattini, P. (2012). Slow rock-slope deformation. In: Clague JJ;Stead D;(eds). Landslides Types, Mechanisms and Modeling. p. 207-221, Cambridge University Press, ISBN: 978-1-107-00206-7.

Genome data from a sixteenth century pig illuminate modern breed relationships

PubMed Central

Ramírez, O; Burgos-Paz, W; Casas, E; Ballester, M; Bianco, E; Olalde, I; Santpere, G; Novella, V; Gut, M; Lalueza-Fox, C; Saña, M; Pérez-Enciso, M

2015-01-01

Ancient DNA (aDNA) provides direct evidence of historical events that have modeled the genome of modern individuals. In livestock, resolving the differences between the effects of initial domestication and of subsequent modern breeding is not straight forward without aDNA data. Here, we have obtained shotgun genome sequence data from a sixteenth century pig from Northeastern Spain (Montsoriu castle), the ancient pig was obtained from an extremely well-preserved and diverse assemblage. In addition, we provide the sequence of three new modern genomes from an Iberian pig, Spanish wild boar and a Guatemalan Creole pig. Comparison with both mitochondrial and autosomal genome data shows that the ancient pig is closely related to extant Iberian pigs and to European wild boar. Although the ancient sample was clearly domestic, admixture with wild boar also occurred, according to the D-statistics. The close relationship between Iberian, European wild boar and the ancient pig confirms that Asian introgression in modern Iberian pigs has not existed or has been negligible. In contrast, the Guatemalan Creole pig clusters apart from the Iberian pig genome, likely due to introgression from international breeds. PMID:25204303

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

PubMed

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach

PubMed Central

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

Objectives This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. Methodology A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. Results The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. Conclusion This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff. PMID:23559904

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Multivariate normality

NASA Technical Reports Server (NTRS)

Crutcher, H. L.; Falls, L. W.

1976-01-01

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

PubMed

Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

2017-08-07

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

Variation of Water Quality Parameters with Siltation Depth for River Ichamati Along International Border with Bangladesh Using Multivariate Statistical Techniques

NASA Astrophysics Data System (ADS)

Roy, P. K.; Pal, S.; Banerjee, G.; Biswas Roy, M.; Ray, D.; Majumder, A.

2014-12-01

River is considered as one of the main sources of freshwater all over the world. Hence analysis and maintenance of this water resource is globally considered a matter of major concern. This paper deals with the assessment of surface water quality of the Ichamati river using multivariate statistical techniques. Eight distinct surface water quality observation stations were located and samples were collected. For the samples collected statistical techniques were applied to the physico-chemical parameters and depth of siltation. In this paper cluster analysis is done to determine the relations between surface water quality and siltation depth of river Ichamati. Multiple regressions and mathematical equation modeling have been done to characterize surface water quality of Ichamati river on the basis of physico-chemical parameters. It was found that surface water quality of the downstream river was different from the water quality of the upstream. The analysis of the water quality parameters of the Ichamati river clearly indicate high pollution load on the river water which can be accounted to agricultural discharge, tidal effect and soil erosion. The results further reveal that with the increase in depth of siltation, water quality degraded.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

PubMed Central

Broadaway, K. Alaine; Cutler, David J.; Duncan, Richard; Moore, Jacob L.; Ware, Erin B.; Jhun, Min A.; Bielak, Lawrence F.; Zhao, Wei; Smith, Jennifer A.; Peyser, Patricia A.; Kardia, Sharon L.R.; Ghosh, Debashis; Epstein, Michael P.

2016-01-01

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. PMID:26942286

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

REINVENTING PERSONAL EXPOSURE TO PARTICULATE MATTER

EPA Science Inventory

Recent epidemiologic studies of modern air pollution show statistically significant relationships between fluctuations of daily non-trauma mortality and fluctuations of daily ambient particulate matter (PM) levels at low concentrations. A review of historic smoke-fog (smog)episo...

Modern contraceptive use among migrant and non-migrant women in Kenya.

PubMed

Ochako, Rhoune; Askew, Ian; Okal, Jerry; Oucho, John; Temmerman, Marleen

2016-06-01

Manifest socio-economic differences are a trigger for internal migration in many sub-Saharan settings including Kenya. An interplay of the social, political and economic factors often lead to internal migration. Internal migration potentially has significant consequences on an individual's economic growth and on access to health services, however, there has been little research on these dynamics. In Kenya, where regional differentials in population growth and poverty reduction continue to be priorities in the post MDG development agenda, understanding the relationships between contraceptive use and internal migration is highly relevant. Using data from the 2008-09 Kenya Demographic and Health Survey (DHS), we analyze data from 5,905 women aged 15-49 years who reported being sexually active in the last 12 months prior to the survey. Bivariate and multivariate logistic regressions are fitted to predict correlates of contraceptive use in the presence of migration streams among other explanatory variables. Modern contraceptive use was significantly higher among women in all migration streams (non-migrant urban (OR = 2.8, p < 0.001), urban-urban (OR = 2.0, p < 0.001), urban-rural (OR = 2.0, p < 0.001), rural-urban (OR = 2.6, p < 0.001), rural-rural (OR = 1.7, p < 0.001), than non-migrant rural women. Women who internally migrate within Kenya, whether from rural to urban or between urban centres, were more likely to use modern contraception than non-migrant rural women. This phenomenon appears to be due to selection, adaption and disruption effects which are likely to promote use of modern contraceptives. Programmatically, the differentials in modern contraceptive use by the different migration streams should be considered when designing family planning programmes among migrant and non-migrant women.

The Physics of Semiconductors

NASA Astrophysics Data System (ADS)

Brennan, Kevin F.

1999-02-01

Modern fabrication techniques have made it possible to produce semiconductor devices whose dimensions are so small that quantum mechanical effects dominate their behavior. This book describes the key elements of quantum mechanics, statistical mechanics, and solid-state physics that are necessary in understanding these modern semiconductor devices. The author begins with a review of elementary quantum mechanics, and then describes more advanced topics, such as multiple quantum wells. He then disusses equilibrium and nonequilibrium statistical mechanics. Following this introduction, he provides a thorough treatment of solid-state physics, covering electron motion in periodic potentials, electron-phonon interaction, and recombination processes. The final four chapters deal exclusively with real devices, such as semiconductor lasers, photodiodes, flat panel displays, and MOSFETs. The book contains many homework exercises and is suitable as a textbook for electrical engineering, materials science, or physics students taking courses in solid-state device physics. It will also be a valuable reference for practicing engineers in optoelectronics and related areas.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

DTIC Science & Technology

2008-07-07

analyzing multivariate data sets. The system was developed using the Java Development Kit (JDK) version 1.5; and it yields interactive performance on a... script and captures output from the MATLAB’s “regress” and “stepwisefit” utilities that perform simple and stepwise regression, respectively. The MATLAB...Statistical Association, vol. 85, no. 411, pp. 664–675, 1990. [9] H. Hauser, F. Ledermann, and H. Doleisch, “ Angular brushing of extended parallel coordinates

A multi-analyte serum test for the detection of non-small cell lung cancer

PubMed Central

Farlow, E C; Vercillo, M S; Coon, J S; Basu, S; Kim, A W; Faber, L P; Warren, W H; Bonomi, P; Liptay, M J; Borgia, J A

2010-01-01

Background: In this study, we appraised a wide assortment of biomarkers previously shown to have diagnostic or prognostic value for non-small cell lung cancer (NSCLC) with the intent of establishing a multi-analyte serum test capable of identifying patients with lung cancer. Methods: Circulating levels of 47 biomarkers were evaluated against patient cohorts consisting of 90 NSCLC and 43 non-cancer controls using commercial immunoassays. Multivariate statistical methods were used on all biomarkers achieving statistical relevance to define an optimised panel of diagnostic biomarkers for NSCLC. The resulting biomarkers were fashioned into a classification algorithm and validated against serum from a second patient cohort. Results: A total of 14 analytes achieved statistical relevance upon evaluation. Multivariate statistical methods then identified a panel of six biomarkers (tumour necrosis factor-α, CYFRA 21-1, interleukin-1ra, matrix metalloproteinase-2, monocyte chemotactic protein-1 and sE-selectin) as being the most efficacious for diagnosing early stage NSCLC. When tested against a second patient cohort, the panel successfully classified 75 of 88 patients. Conclusions: Here, we report the development of a serum algorithm with high specificity for classifying patients with NSCLC against cohorts of various ‘high-risk' individuals. A high rate of false positives was observed within the cohort in which patients had non-neoplastic lung nodules, possibly as a consequence of the inflammatory nature of these conditions. PMID:20859284

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.