Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

Science.gov (United States)

Snedden, Gregg A.; Steyer, Gregory D.

2013-01-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

Science.gov (United States)

Dai, Huanping; Micheyl, Christophe

2012-11-01

Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

Multinomial Response Models, for Modeling and Determining Important Factors in Different Contraceptive Methods in Women

Directory of Open Access Journals (Sweden)

E Haji Nejad

2001-06-01

Full Text Available Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART. Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.

Work Intensity of Households: Multinomial Logit Analysis and Correspondence Analysis for Slovak Republic

Directory of Open Access Journals (Sweden)

Erik Šoltés

2018-03-01

Full Text Available Exclusion from the labour market is a serious social problem that is also addressed by the Europe 2020 strategy. While in the past the attention of statisticians and sociologists in the fight against poverty and social exclusion has concentrated mainly on income poverty and material deprivation, in recent times many studies and analyses are much more focused on work intensity as well. Households that use their work potential to less than 20%, have a very low work intensity, and members of such households are included into the population of people who are at risk of poverty or social exclusion. Moreover, the low use of labour potential of households significantly increases the risk of income poverty and the threat of material deprivation. This article provides an analysis of work intensity levels of Slovak households depending on the factors that are monitored by the EU-SILC 2015. The impact of relevant factors is quantified by correspondence analysis and by multinomial logistic regression model.

Reproductive risk factors assessment for anaemia among pregnant women in India using a multinomial logistic regression model.

Science.gov (United States)

Perumal, Vanamail

2014-07-01

To assess reproductive risk factors for anaemia among pregnant women in urban and rural areas of India. The International Institute of Population Sciences, India, carried out third National Family Health Survey in 2005-2006 to estimate a key indicator from a sample of ever-married women in the reproductive age group 15-49 years. Data on various dimensions were collected using a structured questionnaire, and anaemia was measured using a portable HemoCue instrument. Anaemia prevalence among pregnant women was compared between rural and urban areas using chi-square test and odds ratio. Multinomial logistic regression analysis was used to determine risk factors. Anaemia prevalence was assessed among 3355 pregnant women from rural areas and 1962 pregnant women from urban areas. Moderate-to-severe anaemia in rural areas (32.4%) is significantly more common than in urban areas (27.3%) with an excess risk of 30%. Gestational age specific prevalence of anaemia significantly increases in rural areas after 6 months. Pregnancy duration is a significant risk factor in both urban and rural areas. In rural areas, increasing age at marriage and mass media exposure are significant protective factors of anaemia. However, more births in the last five years, alcohol consumption and smoking habits are significant risk factors. In rural areas, various reproductive factors and lifestyle characteristics constitute significant risk factors for moderate-to-severe anaemia. Therefore, intensive health education on reproductive practices and the impact of lifestyle characteristics are warranted to reduce anaemia prevalence. © 2014 John Wiley & Sons Ltd.

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

Science.gov (United States)

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

2009-11-01

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

Regression Models For Multivariate Count Data.

Science.gov (United States)

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.

The Finite and Moving Order Multinomial Universal Portfolio

International Nuclear Information System (INIS)

Tan, Choon Peng; Pang, Sook Theng

2013-01-01

An upper bound for the ratio of wealths of the best constant -rebalanced portfolio to that of the multinomial universal portfolio is derived. The finite- order multinomial universal portfolios can reduce the implementation time and computer-memory requirements for computation. The improved performance of the finite-order portfolios on some selected local stock-price data sets is observed.

The Application of Multinomial Logistic Regression Models for the Assessment of Parameters of Oocytes and Embryos Quality in Predicting Pregnancy and Miscarriage

Directory of Open Access Journals (Sweden)

Milewska Anna Justyna

2017-09-01

Full Text Available Infertility is a huge problem nowadays, not only from the medical but also from the social point of view. A key step to improve treatment outcomes is the possibility of effective prediction of treatment result. In a situation when a phenomenon with more than 2 states needs to be explained, e.g. pregnancy, miscarriage, non-pregnancy, the use of multinomial logistic regression is a good solution. The aim of this paper is to select those features that have a significant impact on achieving clinical pregnancy as well as those that determine the occurrence of spontaneous miscarriage (non-pregnancy was set as the reference category. Two multi-factor models were obtained, used in predicting infertility treatment outcomes. One of the models enabled to conclude that the number of follicles and the percentage of retrieved mature oocytes have a significant impact when prediction of treatment outcome is made on the basis of information about oocytes. The other model, built on the basis of information about embryos, showed the significance of the number of fertilized oocytes, the percentage of at least 7-cell embryos on day 3, the percentage of blasts on day 5, and the day of transfer.

Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

Science.gov (United States)

Al-Mudhafar, W. J.

2013-12-01

Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly

Multinomial logistic models explaining income changes of migrants to high-amenity counties.

Science.gov (United States)

Von Reichert, C; Rudzitis, G

1992-01-01

"A survey of residents of and migrants to 15 fast-growing wilderness counties [in the United States] showed that only 25 percent of the migrants increased their income, while almost 50 percent accepted income losses upon their moves to high-amenity counties. Concomitantly, amenities and quality of life were more important factors in the migration decision than was employment, for instance. We focused on migrants in the labor force and employed multinomial logistic regression to identify the impact of migrants' characteristics, their satisfaction/dissatisfaction with previous location (push), and the importance of destination features (pull) on income change." excerpt

Generalised Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

DEFF Research Database (Denmark)

Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted...

Pricing Mining Concessions Based on Combined Multinomial Pricing Model

Directory of Open Access Journals (Sweden)

Chang Xiao

2017-01-01

Full Text Available A combined multinomial pricing model is proposed for pricing mining concession in which the annualized volatility of the price of mineral products follows a multinomial distribution. First, a combined multinomial pricing model is proposed which consists of binomial pricing models calculated according to different volatility values. Second, a method is provided to calculate the annualized volatility and the distribution. Third, the value of convenience yields is calculated based on the relationship between the futures price and the spot price. The notion of convenience yields is used to adjust our model as well. Based on an empirical study of a Chinese copper mine concession, we verify that our model is easy to use and better than the model with constant volatility when considering the changing annualized volatility of the price of the mineral product.

Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data

DEFF Research Database (Denmark)

Mousavi, Seyed Nourollah

Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...

Uncovering a latent multinomial: Analysis of mark-recapture data with misidentification

Science.gov (United States)

Link, W.A.; Yoshizaki, J.; Bailey, L.L.; Pollock, K.H.

2010-01-01

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f = A???x of a latent multinomial variable x with cell probability vector ?? = ??(??). Given that full conditional distributions [?? | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, ??], which is made possible by knowledge of the null space of A???. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks. ?? 2009, The International Biometric Society.

Parameter identification in multinomial processing tree models

NARCIS (Netherlands)

Schmittmann, V.D.; Dolan, C.V.; Raijmakers, M.E.J.; Batchelder, W.H.

2010-01-01

Multinomial processing tree models form a popular class of statistical models for categorical data that have applications in various areas of psychological research. As in all statistical models, establishing which parameters are identified is necessary for model inference and selection on the basis

Interpreting Results from the Multinomial Logit Model

DEFF Research Database (Denmark)

Wulff, Jesper

2015-01-01

This article provides guidelines and illustrates practical steps necessary for an analysis of results from the multinomial logit model (MLM). The MLM is a popular model in the strategy literature because it allows researchers to examine strategic choices with multiple outcomes. However, there see...... suitable for both interpretation and communication of results. The pratical steps are illustrated through an application of the MLM to the choice of foreign market entry mode.......This article provides guidelines and illustrates practical steps necessary for an analysis of results from the multinomial logit model (MLM). The MLM is a popular model in the strategy literature because it allows researchers to examine strategic choices with multiple outcomes. However, there seem...... to be systematic issues with regard to how researchers interpret their results when using the MLM. In this study, I present a set of guidelines critical to analyzing and interpreting results from the MLM. The procedure involves intuitive graphical representations of predicted probabilities and marginal effects...

Multinomial-exponential reliability function: a software reliability model

International Nuclear Information System (INIS)

Saiz de Bustamante, Amalio; Saiz de Bustamante, Barbara

2003-01-01

The multinomial-exponential reliability function (MERF) was developed during a detailed study of the software failure/correction processes. Later on MERF was approximated by a much simpler exponential reliability function (EARF), which keeps most of MERF mathematical properties, so the two functions together makes up a single reliability model. The reliability model MERF/EARF considers the software failure process as a non-homogeneous Poisson process (NHPP), and the repair (correction) process, a multinomial distribution. The model supposes that both processes are statistically independent. The paper discusses the model's theoretical basis, its mathematical properties and its application to software reliability. Nevertheless it is foreseen model applications to inspection and maintenance of physical systems. The paper includes a complete numerical example of the model application to a software reliability analysis

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Science.gov (United States)

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

USE OF THE SIMPLE LINEAR REGRESSION MODEL IN MACRO-ECONOMICAL ANALYSES

Directory of Open Access Journals (Sweden)

Constantin ANGHELACHE

2011-10-01

Full Text Available The article presents the fundamental aspects of the linear regression, as a toolbox which can be used in macroeconomic analyses. The article describes the estimation of the parameters, the statistical tests used, the homoscesasticity and heteroskedasticity. The use of econometrics instrument in macroeconomics is an important factor that guarantees the quality of the models, analyses, results and possible interpretation that can be drawn at this level.

Estimation from incomplete multinomial data. Ph.D. Thesis - Harvard Univ.

Science.gov (United States)

Credeur, K. R.

1978-01-01

The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.

A dynamic random effects multinomial logit model of household car ownership

DEFF Research Database (Denmark)

Bue Bjørner, Thomas; Leth-Petersen, Søren

2007-01-01

Using a large household panel we estimate demand for car ownership by means of a dynamic multinomial model with correlated random effects. Results suggest that the persistence in car ownership observed in the data should be attributed to both true state dependence and to unobserved heterogeneity...... (random effects). It also appears that random effects related to single and multiple car ownership are correlated, suggesting that the IIA assumption employed in simple multinomial models of car ownership is invalid. Relatively small elasticities with respect to income and car costs are estimated...

Naive Bayesian classifiers for multinomial features: a theoretical analysis

CSIR Research Space (South Africa)

Van Dyk, E

2007-11-01

Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

Science.gov (United States)

Klauer, Karl Christoph

2010-01-01

Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…

Applications of MIDAS regression in analysing trends in water quality

Science.gov (United States)

Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.

2014-04-01

We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.

Testing independence between two Poisson-generated multinomial variables in case-series and cohort studies.

Science.gov (United States)

Hocine, Mounia; Guillemot, Didier; Tubert-Bitter, Pascale; Moreau, Thierry

2005-12-30

In case-series or cohort studies, we propose a test of independence between the occurrences of two types of recurrent events (such as two repeated infections) related to an intermittent exposure (such as an antibiotic treatment). The test relies upon an extension of a recent method for analysing case-series data, in the presence of one type of recurrent event. The test statistic is derived from a bivariate Poisson generated-multinomial distribution. Simulations for checking the validity of the test concerning the type I error and the power properties are presented. The test is illustrated using data from a cohort on antibiotics bacterial resistance in schoolchildren. Copyright 2005 John Wiley & Sons, Ltd.

A Multinomial Probit Model with Latent Factors

DEFF Research Database (Denmark)

Piatek, Rémi; Gensowski, Miriam

2017-01-01

be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler......We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can...

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

response data are generated from a discrete multinomial distribution with support on (0,1). The linear regression model, the variable-dispersion beta regression model and the fractional logit regression model all perform well across the simulation experiments under consideration. When employing beta regression to estimate covariate effects on (0,1) response data, researchers should ensure their dispersion sub-model is properly specified, else inferential errors could arise.

Parameter Estimation in Probit Model for Multivariate Multinomial Response Using SMLE

Directory of Open Access Journals (Sweden)

Jaka Nugraha

2012-02-01

Full Text Available In  the  research  field  of  transportation,  market  research and  politics,  often involving  the  response  of  the multinomial multivariate  observations.  In  this  paper, we discused  a  modeling  of  multivariate  multinomial  responses  using  probit  model.  The estimated  parameters  were  calculated  using Maximum  Likelihood  Estimations  (MLE based  on  the  GHK  simulation.  method  known  as Simulated  Maximum  Likelihood Estimations (SMLE. Likelihood function on the Probit model contains probability values that must be resolved by simulation. By using  the GHK simulation algorithm,  the estimator equation has been obtained for the parameters in the model Probit  Keywords : Probit Model, Newton-Raphson Iteration,  GHK simulator, MLE, simulated log-likelihood

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

Science.gov (United States)

Higgs, Megan D.; Link, William; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.

2013-01-01

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

Science.gov (United States)

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

How to deal with continuous and dichotomic outcomes in epidemiological research: linear and logistic regression analyses

NARCIS (Netherlands)

Tripepi, Giovanni; Jager, Kitty J.; Stel, Vianda S.; Dekker, Friedo W.; Zoccali, Carmine

2011-01-01

Because of some limitations of stratification methods, epidemiologists frequently use multiple linear and logistic regression analyses to address specific epidemiological questions. If the dependent variable is a continuous one (for example, systolic pressure and serum creatinine), the researcher

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model.

Science.gov (United States)

Islam, Mohammad Mafijul; Alam, Morshed; Tariquzaman, Md; Kabir, Mohammad Alamgir; Pervin, Rokhsona; Begum, Munni; Khan, Md Mobarak Hossain

2013-01-08

Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance variable namely mother's education, father's education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh.

Multinomial model and zero-inflated gamma model to study time spent on leisure time physical activity: an example of ELSA-Brasil.

Science.gov (United States)

Nobre, Aline Araújo; Carvalho, Marilia Sá; Griep, Rosane Härter; Fonseca, Maria de Jesus Mendes da; Melo, Enirtes Caetano Prates; Santos, Itamar de Souza; Chor, Dora

2017-08-17

To compare two methodological approaches: the multinomial model and the zero-inflated gamma model, evaluating the factors associated with the practice and amount of time spent on leisure time physical activity. Data collected from 14,823 baseline participants in the Longitudinal Study of Adult Health (ELSA-Brasil - Estudo Longitudinal de Saúde do Adulto ) have been analysed. Regular leisure time physical activity has been measured using the leisure time physical activity module of the International Physical Activity Questionnaire. The explanatory variables considered were gender, age, education level, and annual per capita family income. The main advantage of the zero-inflated gamma model over the multinomial model is that it estimates mean time (minutes per week) spent on leisure time physical activity. For example, on average, men spent 28 minutes/week longer on leisure time physical activity than women did. The most sedentary groups were young women with low education level and income. The zero-inflated gamma model, which is rarely used in epidemiological studies, can give more appropriate answers in several situations. In our case, we have obtained important information on the main determinants of the duration of leisure time physical activity. This information can help guide efforts towards the most vulnerable groups since physical inactivity is associated with different diseases and even premature death.

Interpreting Marginal Effects in the Multinomial Logit Model

DEFF Research Database (Denmark)

Wulff, Jesper

2014-01-01

with a substantial increase in the probability of entering a foreign market using a joint venture, while increases in the unpredictability in the host country environment are associated with a lower probability of wholly owned subsidiaries and a higher probability of exporting entries....... that have entered foreign markets. Through the application of a multinomial logit model, careful analysis of the marginal effects is performed through graphical representations, marginal effects at the mean, average marginal effects and elasticities. I show that increasing cultural distance is associated......This paper presents the challenges when researchers interpret results about relationships between variables from discrete choice models with multiple outcomes. The recommended approach is demonstrated by testing predictions from transaction cost theory on a sample of 246 Scandinavian firms...

Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach.

Directory of Open Access Journals (Sweden)

Ifechukwude Obiamaka Okwechime

Full Text Available Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida.Data covering the time period January-December 2013, were obtained from Florida's Behavioral Risk Factor Surveillance System (BRFSS. Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow's goodness of fit test.There were approximately 2,983 (7.3% and 5,189 (12.1% adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4% or obese (26.4%, hypertensive (34.6%, hypercholesteremic (40.3%, and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42, obese (RRR = 3.41, 95% CI = 2.61, 4.45, hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15, hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43, and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55 had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95

Multinomial model and zero-inflated gamma model to study time spent on leisure time physical activity: an example of ELSA-Brasil

Directory of Open Access Journals (Sweden)

Aline Araújo Nobre

2017-08-01

Full Text Available ABSTRACT OBJECTIVE To compare two methodological approaches: the multinomial model and the zero-inflated gamma model, evaluating the factors associated with the practice and amount of time spent on leisure time physical activity. METHODS Data collected from 14,823 baseline participants in the Longitudinal Study of Adult Health (ELSA-Brasil – Estudo Longitudinal de Saúde do Adulto have been analysed. Regular leisure time physical activity has been measured using the leisure time physical activity module of the International Physical Activity Questionnaire. The explanatory variables considered were gender, age, education level, and annual per capita family income. RESULTS The main advantage of the zero-inflated gamma model over the multinomial model is that it estimates mean time (minutes per week spent on leisure time physical activity. For example, on average, men spent 28 minutes/week longer on leisure time physical activity than women did. The most sedentary groups were young women with low education level and income CONCLUSIONS The zero-inflated gamma model, which is rarely used in epidemiological studies, can give more appropriate answers in several situations. In our case, we have obtained important information on the main determinants of the duration of leisure time physical activity. This information can help guide efforts towards the most vulnerable groups since physical inactivity is associated with different diseases and even premature death.

The number of subjects per variable required in linear regression analyses.

Science.gov (United States)

Austin, Peter C; Steyerberg, Ewout W

2015-06-01

To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Statistical and regression analyses of detected extrasolar systems

Czech Academy of Sciences Publication Activity Database

Pintr, Pavel; Peřinová, V.; Lukš, A.; Pathak, A.

2013-01-01

Roč. 75, č. 1 (2013), s. 37-45 ISSN 0032-0633 Institutional support: RVO:61389021 Keywords : Exoplanets * Kepler candidates * Regression analysis Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 1.630, year: 2013 http://www.sciencedirect.com/science/article/pii/S0032063312003066

Reducing Inter-Laboratory Differences between Semen Analyses Using Z Score and Regression Transformations

Directory of Open Access Journals (Sweden)

Esther Leushuis

2016-12-01

Full Text Available Background: Standardization of the semen analysis may improve reproducibility. We assessed variability between laboratories in semen analyses and evaluated whether a transformation using Z scores and regression statistics was able to reduce this variability. Materials and Methods: We performed a retrospective cohort study. We calculated between-laboratory coefficients of variation (CVB for sperm concentration and for morphology. Subsequently, we standardized the semen analysis results by calculating laboratory specific Z scores, and by using regression. We used analysis of variance for four semen parameters to assess systematic differences between laboratories before and after the transformations, both in the circulation samples and in the samples obtained in the prospective cohort study in the Netherlands between January 2002 and February 2004. Results: The mean CVB was 7% for sperm concentration (range 3 to 13% and 32% for sperm morphology (range 18 to 51%. The differences between the laboratories were statistically significant for all semen parameters (all P<0.001. Standardization using Z scores did not reduce the differences in semen analysis results between the laboratories (all P<0.001. Conclusion: There exists large between-laboratory variability for sperm morphology and small, but statistically significant, between-laboratory variation for sperm concentration. Standardization using Z scores does not eliminate between-laboratory variability.

Developmental trajectories of paediatric headache - sex-specific analyses and predictors.

Science.gov (United States)

Isensee, Corinna; Fernandez Castelao, Carolin; Kröner-Herwig, Birgit

2016-01-01

Headache is the most common pain disorder in children and adolescents and is associated with diverse dysfunctions and psychological symptoms. Several studies evidenced sex-specific differences in headache frequency. Until now no study exists that examined sex-specific patterns of change in paediatric headache across time and included pain-related somatic and (socio-)psychological predictors. Latent Class Growth Analysis (LCGA) was used in order to identify different trajectory classes of headache across four annual time points in a population-based sample (n = 3 227; mean age 11.34 years; 51.2 % girls). In multinomial logistic regression analyses the influence of several predictors on the class membership was examined. For girls, a four-class model was identified as the best fitting model. While the majority of girls reported no (30.5 %) or moderate headache frequencies (32.5 %) across time, one class with a high level of headache days (20.8 %) and a class with an increasing headache frequency across time (16.2 %) were identified. For boys a two class model with a 'no headache class' (48.6 %) and 'moderate headache class' (51.4 %) showed the best model fit. Regarding logistic regression analyses, migraine and parental headache proved to be stable predictors across sexes. Depression/anxiety was a significant predictor for all pain classes in girls. Life events, dysfunctional stress coping and school burden were also able to differentiate at least between some classes in both sexes. The identified trajectories reflect sex-specific differences in paediatric headache, as seen in the number and type of classes extracted. The documented risk factors can deliver ideas for preventive actions and considerations for treatment programmes.

Multinomial N-mixture models improve the applicability of electrofishing for developing population estimates of stream-dwelling Smallmouth Bass

Science.gov (United States)

Mollenhauer, Robert; Brewer, Shannon K.

2017-01-01

Failure to account for variable detection across survey conditions constrains progressive stream ecology and can lead to erroneous stream fish management and conservation decisions. In addition to variable detection’s confounding long-term stream fish population trends, reliable abundance estimates across a wide range of survey conditions are fundamental to establishing species–environment relationships. Despite major advancements in accounting for variable detection when surveying animal populations, these approaches remain largely ignored by stream fish scientists, and CPUE remains the most common metric used by researchers and managers. One notable advancement for addressing the challenges of variable detection is the multinomial N-mixture model. Multinomial N-mixture models use a flexible hierarchical framework to model the detection process across sites as a function of covariates; they also accommodate common fisheries survey methods, such as removal and capture–recapture. Effective monitoring of stream-dwelling Smallmouth Bass Micropterus dolomieu populations has long been challenging; therefore, our objective was to examine the use of multinomial N-mixture models to improve the applicability of electrofishing for estimating absolute abundance. We sampled Smallmouth Bass populations by using tow-barge electrofishing across a range of environmental conditions in streams of the Ozark Highlands ecoregion. Using an information-theoretic approach, we identified effort, water clarity, wetted channel width, and water depth as covariates that were related to variable Smallmouth Bass electrofishing detection. Smallmouth Bass abundance estimates derived from our top model consistently agreed with baseline estimates obtained via snorkel surveys. Additionally, confidence intervals from the multinomial N-mixture models were consistently more precise than those of unbiased Petersen capture–recapture estimates due to the dependency among data sets in the

Correlation and regression analyses of genetic effects for different types of cells in mammals under radiation and chemical treatment

International Nuclear Information System (INIS)

Slutskaya, N.G.; Mosseh, I.B.

2006-01-01

Data about genetic mutations under radiation and chemical treatment for different types of cells have been analyzed with correlation and regression analyses. Linear correlation between different genetic effects in sex cells and somatic cells have found. The results may be extrapolated on sex cells of human and mammals. (authors)

The outcome of tuberculosis treatment in subjects with chronic kidney disease in Brazil: a multinomial analysis

Directory of Open Access Journals (Sweden)

Barbara Reis-Santos

2013-09-01

Full Text Available OBJECTIVE: To analyze the association between clinical/epidemiological characteristics and outcomes of tuberculosis treatment in patients with concomitant tuberculosis and chronic kidney disease (CKD in Brazil. METHODS: We used the Brazilian Ministry of Health National Case Registry Database to identify patients with tuberculosis and CKD, treated between 2007 and 2011. The tuberculosis treatment outcomes were compared with epidemiological and clinical characteristics of the subjects using a hierarchical multinomial logistic regression model, in which cure was the reference outcome. RESULTS: The prevalence of CKD among patients with tuberculosis was 0.4% (95% CI: 0.37-0.42%. The sample comprised 1,077 subjects. The outcomes were cure, in 58%; treatment abandonment, in 7%; death from tuberculosis, in 13%; and death from other causes, in 22%. The characteristics that differentiated the ORs for treatment abandonment or death were age; alcoholism; AIDS; previous noncompliance with treatment; transfer to another facility; suspected tuberculosis on chest X-ray; positive results in the first smear microscopy; and indications for/use of directly observed treatment, short-course strategy. CONCLUSIONS: Our data indicate the importance of sociodemographic characteristics for the diagnosis of tuberculosis in patients with CKD and underscore the need for tuberculosis control strategies targeting patients with chronic noncommunicable diseases, such as CKD.

Prospective memory after moderate-to-severe traumatic brain injury: a multinomial modeling approach.

Science.gov (United States)

Pavawalla, Shital P; Schmitter-Edgecombe, Maureen; Smith, Rebekah E

2012-01-01

Prospective memory (PM), which can be understood as the processes involved in realizing a delayed intention, is consistently found to be impaired after a traumatic brain injury (TBI). Although PM can be empirically dissociated from retrospective memory, it inherently involves both a prospective component (i.e., remembering that an action needs to be carried out) and retrospective components (i.e., remembering what action needs to be executed and when). This study utilized a multinomial processing tree model to disentangle the prospective (that) and retrospective recognition (when) components underlying PM after moderate-to-severe TBI. Seventeen participants with moderate to severe TBI and 17 age- and education-matched control participants completed an event-based PM task that was embedded within an ongoing computer-based color-matching task. The multinomial processing tree modeling approach revealed a significant group difference in the prospective component, indicating that the control participants allocated greater preparatory attentional resources to the PM task compared to the TBI participants. Participants in the TBI group were also found to be significantly more impaired than controls in the when aspect of the retrospective component. These findings indicated that the TBI participants had greater difficulty allocating the necessary preparatory attentional resources to the PM task and greater difficulty discriminating between PM targets and nontargets during task execution, despite demonstrating intact posttest recall and/or recognition of the PM tasks and targets.

The number of subjects per variable required in linear regression analyses

NARCIS (Netherlands)

P.C. Austin (Peter); E.W. Steyerberg (Ewout)

2015-01-01

textabstractObjectives To determine the number of independent variables that can be included in a linear regression model. Study Design and Setting We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

OpenAIRE

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epide...

Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study.

Science.gov (United States)

Onder, Seyhan; Mutlu, Mert

2017-09-01

Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

Logistic regression applied to natural hazards: rare event logistic regression with replications

Science.gov (United States)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Correlation, Regression and Path Analyses of Seed Yield Components in Crambe abyssinica, a Promising Industrial Oil Crop

OpenAIRE

Huang, Banglian; Yang, Yiming; Luo, Tingting; Wu, S.; Du, Xuezhu; Cai, Detian; Loo, van, E.N.; Huang Bangquan

2013-01-01

In the present study correlation, regression and path analyses were carried out to decide correlations among the agro- nomic traits and their contributions to seed yield per plant in Crambe abyssinica. Partial correlation analysis indicated that plant height (X1) was significantly correlated with branching height and the number of first branches (P <0.01); Branching height (X2) was significantly correlated with pod number of primary inflorescence (P <0.01) and number of secondary branch...

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

Science.gov (United States)

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Logistic regression applied to natural hazards: rare event logistic regression with replications

Directory of Open Access Journals (Sweden)

M. Guns

2012-06-01

Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Social determinants of alcohol use among drivers in Calabar | Bello ...

African Journals Online (AJOL)

A semistructured questionnaire, which included the World Health Organization Alcohol Use Disorders Identification Test, was administered at interview. Binary and multinomial logistic regression analyses were used to identify social determinants of any and hazardous alcohol use. Results: Determinants of any alcohol use ...

Anàlisi cluster multinomial bayesià..Aplicació a dades electorals

OpenAIRE

Montón Domingo, Maria

2009-01-01

En aquest treball fi de màster se li vol donar una altra visió a les dades de resultats electorals, en concret, les del Parlament de Catalunya. Així doncs, l'eina d'anàlisi que s'utilitza és l'anàlisi clúster multinomial bayesià i les unitats d'estudi són les zones de recerca petita de la ciutat de Barcelona. D'aquesta manera es determina com s'agrupen les diferents zones de recerca petita de Barcelona des del punt de vista de les seves votacions i quina relació hi ha entre els partits en fun...

Ethnicity at the individual and neighborhood level as an explanation for moving out of the neighborhood

NARCIS (Netherlands)

Schaake, K.; Burgers, J.; Mulder, C.H.

2010-01-01

We address the influence of both the ethnic composition of the neighborhood and the ethnicity of individual residents on moving out of neighborhoods in the Netherlands. Using the Housing Research Netherlands survey and multinomial logistic regression analyses of moving out versus not moving or

Implicit moral evaluations: A multinomial modeling approach.

Science.gov (United States)

Cameron, C Daryl; Payne, B Keith; Sinnott-Armstrong, Walter; Scheffer, Julian A; Inzlicht, Michael

2017-01-01

Implicit moral evaluations-i.e., immediate, unintentional assessments of the wrongness of actions or persons-play a central role in supporting moral behavior in everyday life. Yet little research has employed methods that rigorously measure individual differences in implicit moral evaluations. In five experiments, we develop a new sequential priming measure-the Moral Categorization Task-and a multinomial model that decomposes judgment on this task into multiple component processes. These include implicit moral evaluations of moral transgression primes (Unintentional Judgment), accurate moral judgments about target actions (Intentional Judgment), and a directional tendency to judge actions as morally wrong (Response Bias). Speeded response deadlines reduced Intentional Judgment but not Unintentional Judgment (Experiment 1). Unintentional Judgment was stronger toward moral transgression primes than non-moral negative primes (Experiments 2-4). Intentional Judgment was associated with increased error-related negativity, a neurophysiological indicator of behavioral control (Experiment 4). Finally, people who voted for an anti-gay marriage amendment had stronger Unintentional Judgment toward gay marriage primes (Experiment 5). Across Experiments 1-4, implicit moral evaluations converged with moral personality: Unintentional Judgment about wrong primes, but not negative primes, was negatively associated with psychopathic tendencies and positively associated with moral identity and guilt proneness. Theoretical and practical applications of formal modeling for moral psychology are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

An Empirical Analysis of Television Commercial Ratings in Alternative Competitive Environments Using Multinomial Logit Model

Directory of Open Access Journals (Sweden)

Dilek ALTAŞ

2013-05-01

Full Text Available Watching the commercials depends on the choice of the viewer. Most of the television viewing takes place during “Prime-Time” unfortunately; many viewers opt to zap to other channels when commercials start. The television viewers’ demographic characteristics may indicate the likelihood of the zapping frequency. Analysis made by using Multinomial Logit Model indicates how effective the demographic variables are in the watching rate of the first minute of the television commercials.

Regression analysis with categorized regression calibrated exposure: some interesting findings

Directory of Open Access Journals (Sweden)

Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

The empathy impulse: A multinomial model of intentional and unintentional empathy for pain.

Science.gov (United States)

Cameron, C Daryl; Spring, Victoria L; Todd, Andrew R

2017-04-01

Empathy for pain is often described as automatic. Here, we used implicit measurement and multinomial modeling to formally quantify unintentional empathy for pain: empathy that occurs despite intentions to the contrary. We developed the pain identification task (PIT), a sequential priming task wherein participants judge the painfulness of target experiences while trying to avoid the influence of prime experiences. Using multinomial modeling, we distinguished 3 component processes underlying PIT performance: empathy toward target stimuli (Intentional Empathy), empathy toward prime stimuli (Unintentional Empathy), and bias to judge target stimuli as painful (Response Bias). In Experiment 1, imposing a fast (vs. slow) response deadline uniquely reduced Intentional Empathy. In Experiment 2, inducing imagine-self (vs. imagine-other) perspective-taking uniquely increased Unintentional Empathy. In Experiment 3, Intentional and Unintentional Empathy were stronger toward targets with typical (vs. atypical) pain outcomes, suggesting that outcome information matters and that effects on the PIT are not reducible to affective priming. Typicality of pain outcomes more weakly affected task performance when target stimuli were merely categorized rather than judged for painfulness, suggesting that effects on the latter are not reducible to semantic priming. In Experiment 4, Unintentional Empathy was stronger for participants who engaged in costly donation to cancer charities, but this parameter was also high for those who donated to an objectively worse but socially more popular charity, suggesting that overly high empathy may facilitate maladaptive altruism. Theoretical and practical applications of our modeling approach for understanding variation in empathy are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Effect of dietary fatty acid intake on prospective weight change in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition

DEFF Research Database (Denmark)

Nimptsch, Katharina; Berg-Beckhoff, Gabi; Linseisen, Jakob

2010-01-01

OBJECTIVE: To evaluate the association between fatty acid (alpha-linolenic acid (ALA), EPA, DHA, palmitic, stearic, oleic, linoleic and arachidonic acids) intake and prospective weight change in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition. DESIGN....... RESULTS: Stearic acid intake was linearly associated with weight gain (P acid intake, significantly so in women. In multinomial models, women in the highest tertile of ALA and stearic acid intake showed increased OR (95 % CI......) and categorised into four groups (weight loss, or =2.5 to or =7.5%/5 years). Energy-adjusted dietary fatty acid intake data were estimated from the FFQ completed at baseline. Multivariate linear regression models as well as multinomial logistic regression analyses (carbohydrate replacement models) were conducted...

Which Kindergarten Children Are at Greatest Risk for Attention-Deficit/Hyperactivity and Conduct Disorder Symptomatology as Adolescents?

Science.gov (United States)

Morgan, Paul L.; Li, Hui; Cook, Michael; Farkas, George; Hillemeier, Marianne M.; Lin, Yu-chu

2016-01-01

We sought to identify which kindergarten children are simultaneously at risk of moderate or severe symptomatology in both attention-deficit/hyperactivity disorder (ADHD) and conduct disorder (CD) as adolescents. These risk factor estimates have not been previously available. We conducted multinomial logistic regression analyses of multiinformant…

Utility of DSM-5 section III personality traits in differentiating borderline personality disorder from comparison groups

DEFF Research Database (Denmark)

Bach, B; Sellbom, M; Bo, S

2016-01-01

with the categorical DSM-IV/5 diagnosis of BPD (n=101) from systematically matched samples of other PD patients (n=101) and healthy controls (n=101). This was investigated using one-way ANOVA and multinomial logistic regression analyses. RESULTS: Results indicated that Emotional Lability, Risk Taking...

Impact of Perceived Risk and Friend Influence on Alcohol and Marijuana Use among Students

Science.gov (United States)

Merianos, Ashley L.; Rosen, Brittany L.; Montgomery, LaTrice; Barry, Adam E.; Smith, Matthew Lee

2017-01-01

We performed a secondary analysis of Adolescent Health Risk Behavior Survey data (N=937), examining associations between lifetime alcohol and marijuana use with intrapersonal (i.e., risk perceptions) and interpersonal (e.g., peer approval and behavior) factors. Multinomial and binary logistic regression analyses contend students reporting lifetime…

Genetic analyses of partial egg production in Japanese quail using multi-trait random regression models.

Science.gov (United States)

Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E

2017-12-01

1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.

Foreign Diploma versus Immigrant Background: Determinants of Labour Market Success or Failure?

Science.gov (United States)

Storen, Liv Anne; Wiers-Jenssen, Jannecke

2010-01-01

This article compares the labour market situation of graduates with different types of international background. The authors look at four groups of graduates: immigrants and ethnic Norwegians graduated in Norway and immigrants and ethnic Norwegians graduated abroad. By employing multinomial logistic regression analyses the authors find that ethnic…

Determinants of Male Circumcision for HIV/AIDS Prevention in East ...

African Journals Online (AJOL)

Safe Male Circumcision (SMC) is one the effective strategies for reducing HIV transmission. The paper examines factors associated with SMC for HIV prevention, based on 4,979 males from East Central Uganda. Data were analysed using chi-squared tests and multinomial logistic regression. Older males aged 31 years ...

Mind-Mindedness as a Multidimensional Construct: Appropriate and Nonattuned Mind-Related Comments Independently Predict Infant-Mother Attachment in a Socially Diverse Sample

Science.gov (United States)

Meins, Elizabeth; Fernyhough, Charles; de Rosnay, Marc; Arnott, Bronia; Leekam, Susan R.; Turner, Michelle

2012-01-01

In a socially diverse sample of 206 infant-mother pairs, we investigated predictors of infants' attachment security at 15 months, with a particular emphasis on mothers' tendency to comment appropriately or in a non-attuned manner on their 8-month-olds' internal states (so-called mind-mindedness). Multinomial logistic regression analyses showed…

Analyses of Developmental Rate Isomorphy in Ectotherms: Introducing the Dirichlet Regression.

Directory of Open Access Journals (Sweden)

David S Boukal

Full Text Available Temperature drives development in insects and other ectotherms because their metabolic rate and growth depends directly on thermal conditions. However, relative durations of successive ontogenetic stages often remain nearly constant across a substantial range of temperatures. This pattern, termed 'developmental rate isomorphy' (DRI in insects, appears to be widespread and reported departures from DRI are generally very small. We show that these conclusions may be due to the caveats hidden in the statistical methods currently used to study DRI. Because the DRI concept is inherently based on proportional data, we propose that Dirichlet regression applied to individual-level data is an appropriate statistical method to critically assess DRI. As a case study we analyze data on five aquatic and four terrestrial insect species. We find that results obtained by Dirichlet regression are consistent with DRI violation in at least eight of the studied species, although standard analysis detects significant departure from DRI in only four of them. Moreover, the departures from DRI detected by Dirichlet regression are consistently much larger than previously reported. The proposed framework can also be used to infer whether observed departures from DRI reflect life history adaptations to size- or stage-dependent effects of varying temperature. Our results indicate that the concept of DRI in insects and other ectotherms should be critically re-evaluated and put in a wider context, including the concept of 'equiproportional development' developed for copepods.

The consumer’s choice among television displays: A multinomial logit approach

Directory of Open Access Journals (Sweden)

Carlos Giovanni González Espitia

2013-07-01

Full Text Available The consumer’s choice over a bundle of products depends on observable and unobservable characteristics of goods and consumers. This choice is made in order to maximize utility subject to a budget constraint. At the same time, firms make product differentiation decisions to maximize profit. Quality is a form of differentiation. An example of this occurs in the TV market, where several displays are developed. Our objective is to determine the probability for a consumer of choosing a type of display from among five kinds: standard tube, LCD, plasma, projection and LED. Using a multinomial logit approach, we find that electronic appliances like DVDs and audio systems, as well as socioeconomic status, increase the probability of choosing a high-tech television display. Our empirical approximation contributes to further understanding rational consumer behavior through the theory of utility maximization and highlights the importance of studying market structure and analyzing changes in welfare and efficiency.

Analysis of the liquidity risk in credit unions: a logit multinomial approach

Directory of Open Access Journals (Sweden)

Rosiane Maria Lima Gonçalves

2008-10-01

Full Text Available Liquidity risk in financial institutions is associated to balance between working capital and financial demands. Other factors that affect credit union liquidity are an unanticipated increase of withdrawals without an offsetting amount of new deposits, and the lack of ability in promoting the product geographical diversification. The objective of this study is to analyze Minas Gerais state credit union liquidity risk and its factor determinants. Financial ratios and the multinomial logit model are used. The cooperatives were classified in five categories of liquidity risk: very low, low, medium, high and very high. The empirical results indicate that high levels of liquidity are related to smaller values of the outsourcing capital use, immobilization of the turnover capital, and provision ratios. So, they are associated to larger values of the deposit total/credit operations, and asset growth ratios.

"The empathy impulse: A multinomial model of intentional and unintentional empathy for pain": Correction.

Science.gov (United States)

2018-04-01

Reports an error in "The empathy impulse: A multinomial model of intentional and unintentional empathy for pain" by C. Daryl Cameron, Victoria L. Spring and Andrew R. Todd ( Emotion , 2017[Apr], Vol 17[3], 395-411). In this article, there was an error in the calculation of some of the effect sizes. The w effect size was manually computed incorrectly. The incorrect number of total observations was used, which affected the final effect size estimates. This computing error does not change any of the results or interpretations about model fit based on the G² statistic, or about significant differences across conditions in process parameters. Therefore, it does not change any of the hypothesis tests or conclusions. The w statistics for overall model fit should be .02 instead of .04 in Study 1, .01 instead of .02 in Study 2, .01 instead of .03 for the OIT in Study 3 (model fit for the PIT remains the same: .00), and .02 instead of .03 in Study 4. The corrected tables can be seen here: http://osf.io/qebku at the Open Science Framework site for the article. (The following abstract of the original article appeared in record 2017-01641-001.) Empathy for pain is often described as automatic. Here, we used implicit measurement and multinomial modeling to formally quantify unintentional empathy for pain: empathy that occurs despite intentions to the contrary. We developed the pain identification task (PIT), a sequential priming task wherein participants judge the painfulness of target experiences while trying to avoid the influence of prime experiences. Using multinomial modeling, we distinguished 3 component processes underlying PIT performance: empathy toward target stimuli (Intentional Empathy), empathy toward prime stimuli (Unintentional Empathy), and bias to judge target stimuli as painful (Response Bias). In Experiment 1, imposing a fast (vs. slow) response deadline uniquely reduced Intentional Empathy. In Experiment 2, inducing imagine-self (vs. imagine

Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

OpenAIRE

KELEŞ, Taliha; ALTUN, Murat

2016-01-01

Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

Improved Dietary Guidelines for Vitamin D: Application of Individual Participant Data (IPD)-Level Meta-Regression Analyses

Science.gov (United States)

Cashman, Kevin D.; Ritz, Christian; Kiely, Mairead

2017-01-01

Dietary Reference Values (DRVs) for vitamin D have a key role in the prevention of vitamin D deficiency. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse. This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials (RCTs) are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data (IPD)-level meta-regression, which is increasingly recognized as best practice, from seven winter-based RCTs (with 882 participants ranging in age from 4 to 90 years) of the vitamin D intake–serum 25-hydroxyvitamin D (25(OH)D) dose-response. Our IPD-derived estimates of vitamin D intakes required to maintain 97.5% of 25(OH)D concentrations >25, 30, and 50 nmol/L across the population are 10, 13, and 26 µg/day, respectively. In contrast, standard meta-regression analyses with aggregate data (as used by several agencies in recent years) from the same RCTs estimated that a vitamin D intake requirement of 14 µg/day would maintain 97.5% of 25(OH)D >50 nmol/L. These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25(OH)D to vitamin D intake. PMID:28481259

Improved Dietary Guidelines for Vitamin D: Application of Individual Participant Data (IPD-Level Meta-Regression Analyses

Directory of Open Access Journals (Sweden)

Kevin D. Cashman

2017-05-01

Full Text Available Dietary Reference Values (DRVs for vitamin D have a key role in the prevention of vitamin D deficiency. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse. This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials (RCTs are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data (IPD-level meta-regression, which is increasingly recognized as best practice, from seven winter-based RCTs (with 882 participants ranging in age from 4 to 90 years of the vitamin D intake–serum 25-hydroxyvitamin D (25(OHD dose-response. Our IPD-derived estimates of vitamin D intakes required to maintain 97.5% of 25(OHD concentrations >25, 30, and 50 nmol/L across the population are 10, 13, and 26 µg/day, respectively. In contrast, standard meta-regression analyses with aggregate data (as used by several agencies in recent years from the same RCTs estimated that a vitamin D intake requirement of 14 µg/day would maintain 97.5% of 25(OHD >50 nmol/L. These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25(OHD to vitamin D intake.

The Market for Ph.D. Holders in Greece: Probit and Multinomial Logit Analysis of their Employment Status

OpenAIRE

Joan Daouli; Eirini Konstantina Nikolatou

2015-01-01

The objective of this paper is to investigate the factors influencing the probability that a Ph.D. holder in Greece will work in the academic sector, as well as the probability of his or her choosing employment in various sectors of industry and occupational categories. Probit/multinomial logit models are employed using the 2001 Census data. The empirical results indicate that being young, married, having a Ph.D. in Natural Sciences and/or in Engineering, granted by a Greek university, increa...

Analysing inequalities in Germany a structured additive distributional regression approach

CERN Document Server

Silbersdorff, Alexander

2017-01-01

This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators. This new approach allows the observed discrepancy between the individuals’ realities and the abstract representation of those realities to be explicitly taken into consideration using the arithmetic mean alone. In turn, the method is applied to the question of economic inequality in Germany.

A Simulation Investigation of Principal Component Regression.

Science.gov (United States)

Allen, David E.

Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

Check-all-that-apply data analysed by Partial Least Squares regression

DEFF Research Database (Denmark)

Rinnan, Åsmund; Giacalone, Davide; Frøst, Michael Bom

2015-01-01

are analysed by multivariate techniques. CATA data can be analysed both by setting the CATA as the X and the Y. The former is the PLS-Discriminant Analysis (PLS-DA) version, while the latter is the ANOVA-PLS (A-PLS) version. We investigated the difference between these two approaches, concluding...

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

Science.gov (United States)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

2017-09-01

This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

Alpins and thibos vectorial astigmatism analyses: proposal of a linear regression model between methods

Directory of Open Access Journals (Sweden)

Giuliano de Oliveira Freitas

2013-10-01

Full Text Available PURPOSE: To determine linear regression models between Alpins descriptive indices and Thibos astigmatic power vectors (APV, assessing the validity and strength of such correlations. METHODS: This case series prospectively assessed 62 eyes of 31 consecutive cataract patients with preoperative corneal astigmatism between 0.75 and 2.50 diopters in both eyes. Patients were randomly assorted among two phacoemulsification groups: one assigned to receive AcrySof®Toric intraocular lens (IOL in both eyes and another assigned to have AcrySof Natural IOL associated with limbal relaxing incisions, also in both eyes. All patients were reevaluated postoperatively at 6 months, when refractive astigmatism analysis was performed using both Alpins and Thibos methods. The ratio between Thibos postoperative APV and preoperative APV (APVratio and its linear regression to Alpins percentage of success of astigmatic surgery, percentage of astigmatism corrected and percentage of astigmatism reduction at the intended axis were assessed. RESULTS: Significant negative correlation between the ratio of post- and preoperative Thibos APVratio and Alpins percentage of success (%Success was found (Spearman's ρ=-0.93; linear regression is given by the following equation: %Success = (-APVratio + 1.00x100. CONCLUSION: The linear regression we found between APVratio and %Success permits a validated mathematical inference concerning the overall success of astigmatic surgery.

Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Directory of Open Access Journals (Sweden)

Tolga Kaya

2010-11-01

Full Text Available The purpose of this study is to compare the performances of Artificial Neural Networks (ANN and Multinomial Probit (MNP approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

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

Science.gov (United States)

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

2013-10-01

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

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.

Science.gov (United States)

Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R

2016-12-01

: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We

The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses.

Science.gov (United States)

Xie, Heping; Wang, Fuxing; Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

2017-01-01

Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = -0.11, 95% CI = [-0.19, -0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = -0.70, 95% CI = [-1.02, -0.38], p < 0.001), as well as dtransfer for cueing (β = -0.60, 95% CI = [-0.92, -0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning.

Predicting Word Reading Ability: A Quantile Regression Study

Science.gov (United States)

McIlraith, Autumn L.

2018-01-01

Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…

Structural vascular disease in Africans: performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: the SABPA study

OpenAIRE

Botha, J.; De Ridder, J.H.; Potgieter, J.C.; Steyn, H.S.; Malan, L.

2013-01-01

A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fa...

Bisphenol-A exposures and behavioural aberrations: median and linear spline and meta-regression analyses of 12 toxicity studies in rodents.

Science.gov (United States)

Peluso, Marco E M; Munnia, Armelle; Ceppi, Marcello

2014-11-05

Exposures to bisphenol-A, a weak estrogenic chemical, largely used for the production of plastic containers, can affect the rodent behaviour. Thus, we examined the relationships between bisphenol-A and the anxiety-like behaviour, spatial skills, and aggressiveness, in 12 toxicity studies of rodent offspring from females orally exposed to bisphenol-A, while pregnant and/or lactating, by median and linear splines analyses. Subsequently, the meta-regression analysis was applied to quantify the behavioural changes. U-shaped, inverted U-shaped and J-shaped dose-response curves were found to describe the relationships between bisphenol-A with the behavioural outcomes. The occurrence of anxiogenic-like effects and spatial skill changes displayed U-shaped and inverted U-shaped curves, respectively, providing examples of effects that are observed at low-doses. Conversely, a J-dose-response relationship was observed for aggressiveness. When the proportion of rodents expressing certain traits or the time that they employed to manifest an attitude was analysed, the meta-regression indicated that a borderline significant increment of anxiogenic-like effects was present at low-doses regardless of sexes (β)=-0.8%, 95% C.I. -1.7/0.1, P=0.076, at ≤120 μg bisphenol-A. Whereas, only bisphenol-A-males exhibited a significant inhibition of spatial skills (β)=0.7%, 95% C.I. 0.2/1.2, P=0.004, at ≤100 μg/day. A significant increment of aggressiveness was observed in both the sexes (β)=67.9,C.I. 3.4, 172.5, P=0.038, at >4.0 μg. Then, bisphenol-A treatments significantly abrogated spatial learning and ability in males (Pbisphenol-A, e.g. ≤120 μg/day, were associated to behavioural aberrations in offspring. Copyright © 2014. Published by Elsevier Ireland Ltd.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Body size dissatisfaction among young adults from the 1982 Pelotas birth cohort

OpenAIRE

Mintem, G C; Horta, B L; Domingues, M R; Gigante, D P

2014-01-01

Background/Objectives: To identify the prevalence and factors associated with body dissatisfaction. Subjects/Methods: Birth cohort study investigating 4100 subjects (2187 men and 1913 women) aged between 22 and 23 years who answered questionnaires, including the body satisfaction Stunkard Scale were included in the study; they were weighed and measured. Multinomial logistic regression was used in the crude and adjusted analyses. Results: The prevalence of body dissatisfaction was 64% (95% CI,...

Wage mobility in Europe. A comparative analysis using restricted multinomial logit regression

NARCIS (Netherlands)

Pavlopoulos, D.; Muffels, R.; Vermunt, J.K.

2010-01-01

In this paper, we investigate cross-country differences in wage mobility in Europe using the European Community Household Panel. The paper is particularly focused on examining the impact of economic conditions, welfare state regimes and employment regulation on wage mobility. We apply a log-linear

Measuring political sentiment on Twitter: factor-optimal design for multinomial inverse regression

OpenAIRE

Taddy, Matt

2012-01-01

This article presents a short case study in text analysis: the scoring of Twitter posts for positive, negative, or neutral sentiment directed towards particular US politicians. The study requires selection of a sub-sample of representative posts for sentiment scoring, a common and costly aspect of sentiment mining. As a general contribution, our application is preceded by a proposed algorithm for maximizing sampling efficiency. In particular, we outline and illustrate greedy selection of docu...

Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

Science.gov (United States)

Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

2008-01-01

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

Science.gov (United States)

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

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

Science.gov (United States)

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

2018-02-01

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

Augmenting Data with Published Results in Bayesian Linear Regression

Science.gov (United States)

de Leeuw, Christiaan; Klugkist, Irene

2012-01-01

In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…

Isolated and Skeptical: Social Engagement and Trust in Information Sources Among Smokers

OpenAIRE

Finney Rutten, Lila J.; Blake, Kelly; Hesse, Bradford W.; Ackerson, Leland K.

2011-01-01

Our study compared indicators of social engagement and trust among current, former, and never smokers. Multinomial regression analyses of data from the 2005 U.S. Health Information National Trends Survey (n=5586) were conducted to identify independent associations between social engagement, trust in health information sources, and smoking status. Never smokers (odds ratio (OR)=2.08) and former smokers (OR=2.48) were significantly more likely to belong to community organizations than current s...

Better Autologistic Regression

Directory of Open Access Journals (Sweden)

Mark A. Wolters

2017-11-01

Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

Socio-economic determinants of severe and moderate stunting among under-five children of rural Bangladesh.

Science.gov (United States)

Mostafa, Kamal S M

2011-04-01

Malnutrition among under-five children is a chronic problem in developing countries. This study explores the socio-economic determinants of severe and moderate stunting among under-five children of rural Bangladesh. The study used data from the 2007 Bangladesh Demographic and Health Survey. Cross-sectional and multinomial logistic regression analyses were used to assess the effect of the socio-demographic variables on moderate and severe stunting over normal among the children. Findings revealed that over two-fifths of the children were stunted, of which 26.3% were moderately stunted and 15.1% were severely stunted. The multivariate multinomial logistic regression analysis yielded significantly increased risk of severe stunting (OR=2.53, 95% CI=1.34-4.79) and moderate stunting (OR=2.37, 95% CI=1.47-3.83) over normal among children with a thinner mother. Region, father's education, toilet facilities, child's age, birth order of children and wealth index were also important determinants of children's nutritional status. Development and poverty alleviation programmes should focus on the disadvantaged rural segments of people to improve their nutritional status.

Prevalence of and risk factors for depressive symptoms among people living with HIV/AIDS receiving antiretroviral treatment in Wuhan, China: a short report.

Science.gov (United States)

Rong, Hu; Nianhua, Xie; Jun, Xu; Lianguo, Ruan; Si, Wu; Sheng, Wei; Heng, Guo; Xia, Wang

2017-12-01

We aimed to explore the prevalence of and risk factors for depressive symptoms (DS) among people living with HIV/AIDS (PLWHA) receiving antiretroviral treatment (ART) in Wuhan, Hubei, China. A cross-sectional study evaluating adult PLWHA receiving ART in nine designated clinical hospitals was conducted from October to December 2015. The validated Beck Depression Inventory (BDI) was used to assess DS in eligible participants. Socio-demographical, epidemiological and clinical data were directly extracted from the case reporting database of the China HIV/AIDS Information Network. Multinomial regression analysis was used to explore the risk factors for DS. 394 participants were finally included in all analyses. 40.3% were found to have DS with 13.7% having mild DS and 26.6% having moderate to severe DS. The results of multinomial regression analysis suggested that being married or living with a partner, recent experience of ART-related side effects, and/or history of HCV infection were positively associated with mild DS, while increasing age was positively associated with moderate to severe DS.

Comparing parametric and nonparametric regression methods for panel data

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

Science.gov (United States)

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

Science.gov (United States)

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models.

Directory of Open Access Journals (Sweden)

Luise A Seeker

Full Text Available Telomeres cap the ends of linear chromosomes and shorten with age in many organisms. In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length (TL to exploring TL change within individuals over time. Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1 characterize the change in bovine relative leukocyte TL (RLTL across the lifetime in Holstein Friesian dairy cattle, 2 estimate genetic parameters of RLTL over time and 3 investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age. The analyses were based on 1,328 repeated RLTL measurements of 308 female Holstein Friesian dairy cattle. A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life. The heritability of RLTL ranged from 0.36 to 0.47 (SE = 0.05-0.08 and remained statistically unchanged over time. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0.69, indicating that TL later in life might be regulated by different genes than TL early in life. Even though animals differed in their RLTL profiles significantly, those differences were not correlated with productive lifespan (p = 0.954.

COMPARACION DE 13 INTERVALOS DE CONFIANZA PARA LOS PARAMETROS DE LA DISTRIBUCION MULTINOMIAL

Directory of Open Access Journals (Sweden)

Difariney González-Gómez

2015-07-01

Full Text Available La distribución multinomial es fundamental para la descripción de fenómenos en los que pueden ocurrir k > 2 eventos excluyentes, cada uno con probabilidad π = (π1, π2, . . . , πk. Algunos ejemplos de esta distribución incluyen la calidad de un producto o encuestas de selección múltiple. Un problema de gran interés en inferencia estadística es la construcción de intervalos de confianza los parámetros para π. En este trabajo se comparan, a través de un estudio de simulación, 13 metodologías para la construcción de intervalos de confianza para dicha distribución. Utilizando como criterios de comparación el nivel de confianza nominal, la longitud del intervalo y una combinación de estos, se encuentra que los intervalos de confianza basados en el Teorema del Límite Central no presentan el mejor desempeño. Finalmente se recomiendan los métodos basados en la distribución F (Leemis, 1996, seguido del método de verosimilitud relativa (Kalbfleish, 1985 y Quesenberry & Hurst (1964.

A hipótese de Cressey (1953) e a investigação da ocorrência de fraudes corporativas: uma análise empírica em instituições bancárias brasileiras

OpenAIRE

Machado, Michele Rílany Rodrigues; Gartner, Ivan Ricardo

2017-01-01

ABSTRACT This article fills a technical-scientific gap that currently exists in the Brazilian literature on corporative fraud, by combining the theoretical framework of agency theory, of criminology, and of the economics of crime. In addition, it focuses on a sector that is usually excluded from analyses due to its specific characteristics and shows the application of multinomial logit panel data regression with random effects, which is rarely used in studies in the area of accounting. The ai...

The Cressey hypothesis (1953) and an investigation into the occurrence of corporate fraud: an empirical analysis conducted in Brazilian banking institutions

OpenAIRE

Michele Rílany Rodrigues Machado; Ivan Ricardo Gartner

2017-01-01

ABSTRACT This article fills a technical-scientific gap that currently exists in the Brazilian literature on corporative fraud, by combining the theoretical framework of agency theory, of criminology, and of the economics of crime. In addition, it focuses on a sector that is usually excluded from analyses due to its specific characteristics and shows the application of multinomial logit panel data regression with random effects, which is rarely used in studies in the area of accounting. The ai...

BUYING BEHAVIOUR RELATED TO HEATING SYSTEMS IN GERMANY

OpenAIRE

Decker, Thomas; Zapilko, Marina; Menrad, Klaus

2010-01-01

The decision for buying a heating system is a long-term one, as many different aspects have an influence on this choice which were analysed in a Germany-wide, written survey. The respondents (only owners of a private house) had to answer questions about their attitude towards e.g. economics, convenience or ecological aspects related to heating systems and the respective combustibles. Using a multinomial logistic regression model the choice of the heating system is mainly explained by ecologic...

Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies.

NARCIS (Netherlands)

Kromhout, D.

2009-01-01

Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the

Measurement Error in Education and Growth Regressions

NARCIS (Netherlands)

Portela, M.; Teulings, C.N.; Alessie, R.

The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations

Measurement error in education and growth regressions

NARCIS (Netherlands)

Portela, Miguel; Teulings, Coen; Alessie, R.

2004-01-01

The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations

Panel data specifications in nonparametric kernel regression

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard; Henningsen, Arne

parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

Retro-regression--another important multivariate regression improvement.

Science.gov (United States)

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Modified Regression Correlation Coefficient for Poisson Regression Model

Science.gov (United States)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

SPECIFICS OF THE APPLICATIONS OF MULTIPLE REGRESSION MODEL IN THE ANALYSES OF THE EFFECTS OF GLOBAL FINANCIAL CRISES

Directory of Open Access Journals (Sweden)

Željko V. Račić

2010-12-01

Full Text Available This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand and the credit cards, i.e. indebtedness of the population on this basis (on the other hand, in the USA (from 1999. to 2008. We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program.

Brief Report: Association of Myositis Autoantibodies, Clinical Features, and Environmental Exposures at Illness Onset With Disease Course in Juvenile Myositis.

Science.gov (United States)

Habers, G Esther A; Huber, Adam M; Mamyrova, Gulnara; Targoff, Ira N; O'Hanlon, Terrance P; Adams, Sharon; Pandey, Janardan P; Boonacker, Chantal; van Brussel, Marco; Miller, Frederick W; van Royen-Kerkhof, Annet; Rider, Lisa G

2016-03-01

To identify early factors associated with disease course in patients with juvenile idiopathic inflammatory myopathies (IIMs). Univariable and multivariable multinomial logistic regression analyses were performed in a large juvenile IIM registry (n = 365) and included demographic characteristics, early clinical features, serum muscle enzyme levels, myositis autoantibodies, environmental exposures, and immunogenetic polymorphisms. Multivariable associations with chronic or polycyclic courses compared to a monocyclic course included myositis-specific autoantibodies (multinomial odds ratio [OR] 4.2 and 2.8, respectively), myositis-associated autoantibodies (multinomial OR 4.8 and 3.5), and a documented infection within 6 months of illness onset (multinomial OR 2.5 and 4.7). A higher overall clinical symptom score at diagnosis was associated with chronic or monocyclic courses compared to a polycyclic course. Furthermore, severe illness onset was associated with a chronic course compared to monocyclic or polycyclic courses (multinomial OR 2.1 and 2.6, respectively), while anti-p155/140 autoantibodies were associated with chronic or polycyclic courses compared to a monocyclic course (multinomial OR 3.9 and 2.3, respectively). Additional univariable associations of a chronic course compared to a monocyclic course included photosensitivity, V-sign or shawl sign rashes, and cuticular overgrowth (OR 2.2-3.2). The mean ultraviolet index and highest ultraviolet index in the month before diagnosis were associated with a chronic course compared to a polycyclic course in boys (OR 1.5 and 1.3), while residing in the Northwest was less frequently associated with a chronic course (OR 0.2). Our findings indicate that myositis autoantibodies, in particular anti-p155/140, and a number of early clinical features and environmental exposures are associated with a chronic course in patients with juvenile IIM. These findings suggest that early factors, which are associated with poorer

Independent contrasts and PGLS regression estimators are equivalent.

Science.gov (United States)

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Childhood abuse and late-life depression: Mediating effects of psychosocial factors for early- and late-onset depression.

Science.gov (United States)

Wielaard, Ilse; Hoyer, Mathijs; Rhebergen, Didi; Stek, Max L; Comijs, Hannie C

2018-03-01

Childhood abuse makes people vulnerable to developing depression, even in late life. Psychosocial factors that are common in late life, such as loneliness or lack of a partner, may explain this association. Our aim was to investigate whether the association between childhood abuse and depression in older adults can be explained by psychosocial factors. Cross-sectional data were derived from the Netherlands Study of Depression in Older Persons (aged 60-93), including 132 without lifetime depression, 242 persons with an early-onset depression (Childhood abuse (yes/no) and a frequency-based childhood abuse index were included. Multinomial regression and multivariable mediation analyses were used to examine the association between childhood abuse and the onset of depression, and the influence of loneliness, social network, and partner status. Multinomial regression analyses showed a significant association between childhood abuse and the childhood abuse index with early- and late-onset depression. Multivariable mediation analyses showed that the association between childhood abuse and early-onset depression was partly mediated by social network size and loneliness. This was particularly present for emotional neglect and psychological abuse, but not for physical and sexual abuse. No psychosocial mediators were found for the association between childhood abuse and late-onset depression. A smaller social network and feelings of loneliness mediate the association between childhood abuse and early-onset depression in older adults. Our findings show the importance of detecting childhood abuse as well as the age at depression onset and mapping of relevant psychosocial factors in the treatment of late-life depression. Copyright © 2018 John Wiley & Sons, Ltd.

Logistic regression and multiple classification analyses to explore risk factors of under-5 mortality in bangladesh

International Nuclear Information System (INIS)

Bhowmik, K.R.; Islam, S.

2016-01-01

Logistic regression (LR) analysis is the most common statistical methodology to find out the determinants of childhood mortality. However, the significant predictors cannot be ranked according to their influence on the response variable. Multiple classification (MC) analysis can be applied to identify the significant predictors with a priority index which helps to rank the predictors. The main objective of the study is to find the socio-demographic determinants of childhood mortality at neonatal, post-neonatal, and post-infant period by fitting LR model as well as to rank those through MC analysis. The study is conducted using the data of Bangladesh Demographic and Health Survey 2007 where birth and death information of children were collected from their mothers. Three dichotomous response variables are constructed from children age at death to fit the LR and MC models. Socio-economic and demographic variables significantly associated with the response variables separately are considered in LR and MC analyses. Both the LR and MC models identified the same significant predictors for specific childhood mortality. For both the neonatal and child mortality, biological factors of children, regional settings, and parents socio-economic status are found as 1st, 2nd, and 3rd significant groups of predictors respectively. Mother education and household environment are detected as major significant predictors of post-neonatal mortality. This study shows that MC analysis with or without LR analysis can be applied to detect determinants with rank which help the policy makers taking initiatives on a priority basis. (author)

Theoretical foundations for evidence-based health informatics

DEFF Research Database (Denmark)

Scott, P.; Georgiou, A.; Hypponen, H.

2016-01-01

such as conditional imputation and a ‘‘missing’’- category in multiple regression analyses. Health—exploring complexity: an interdisciplinary systems approach HEC2016 S23 123 Methods A cross sectional survey in a random sample of Danish fishermen was done in 2015 with application of the Nordic questionnaire...... considering relevant confounders were used to look at each single pain site with missing as an additional outcome. In all analyses, sideline occupations, work position, vessel type, education, and duration at sea were considered as further predictors. Results The prevalence of pain was high for all...... excluding all missing’s revealed similar results. Multinomial regression models showed that workload was the only consistent predictor for musculoskeletal pain, in particular regarding upper and lower limb pain. Two additional predictors were found for the nine different pain locations models; sideline...

Prediction, Regression and Critical Realism

DEFF Research Database (Denmark)

Næss, Petter

2004-01-01

This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...

Dual Regression

OpenAIRE

Spady, Richard; Stouli, Sami

2012-01-01

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

Intermediate and advanced topics in multilevel logistic regression analysis.

Science.gov (United States)

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Regression Analyses on the Butterfly Ballot Effect: A Statistical Perspective of the US 2000 Election

Science.gov (United States)

Wu, Dane W.

2002-01-01

The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines…

Predictors of success of external cephalic version and cephalic presentation at birth among 1253 women with non-cephalic presentation using logistic regression and classification tree analyses.

Science.gov (United States)

Hutton, Eileen K; Simioni, Julia C; Thabane, Lehana

2017-08-01

Among women with a fetus with a non-cephalic presentation, external cephalic version (ECV) has been shown to reduce the rate of breech presentation at birth and cesarean birth. Compared with ECV at term, beginning ECV prior to 37 weeks' gestation decreases the number of infants in a non-cephalic presentation at birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format. Data were collected as part of the Early ECV Pilot and Early ECV2 Trials, which randomized 1776 women with a fetus in breech presentation to either early ECV (34-36 weeks' gestation) or delayed ECV (at or after 37 weeks). The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree (CART) analyses. Among nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success. Among multiparas, non-engagement of the presenting part, gestation less than 37 weeks and an easily palpable fetal head were found to be independent predictors of success. These findings were consistent with results of the CART analyses. Regardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Associations between food insecurity and the severity of psychological distress among African-Americans.

Science.gov (United States)

Allen, Nickolas L; Becerra, Benjamin J; Becerra, Monideepa B

2018-07-01

Little research exists on the association between food insecurity and mild to moderate psychological distress (MPD) among Black/African-Americans. In this study, we assess the relationship between food insecurity with and without hunger to that of both MPD and serious psychological distress (SPD) among this population. 2009 and 2011/2012 adult public-use data from African-American respondents of the California Health Interview Survey were utilized for this study (n = 4003). Descriptive statistics were utilized to identify prevalence of psychological distress among sociodemographic and mental-health associated variables. Bivariate analyses were conducted between these variables and psychological distress using survey-weighted chi-square analyses. To evaluate the association between psychological distress, our primary exposure variable of food security, and other variables, we utilized survey-weighted multinomial logistic regression. Prevalence of mild to MPD was higher among those reporting food insecurity while SPD was highest for those with food insecurity and hunger. Results of multinomial logistic regression analysis demonstrate that while MPD was significantly associated with food insecurity, Black/African-Americans with food insecurity and hunger displayed over sixfold odds of higher serious psychological distress, as compared to those living at or above 200% federal poverty level. Our findings add to this growing segment of the literature on psychological distress and food insecurity. Further focus should be placed on improving the efficacy and reach of both formal and informal food support networks to improve the collective health and well-being of poor Black/African-American communities.

A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application

Science.gov (United States)

Zhang, Yongsheng; Wei, Heng; Zheng, Kangning

2017-01-01

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction. PMID:28591188

Geodesic least squares regression for scaling studies in magnetic confinement fusion

International Nuclear Information System (INIS)

Verdoolaege, Geert

2015-01-01

In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority of the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices

Impact of Perceived Risk and Friend Influence on Alcohol and Marijuana Use Among Students.

Science.gov (United States)

Merianos, Ashley L; Rosen, Brittany L; Montgomery, LaTrice; Barry, Adam E; Smith, Matthew Lee

2017-12-01

We performed a secondary analysis of Adolescent Health Risk Behavior Survey data ( N = 937), examining associations between lifetime alcohol and marijuana use with intrapersonal (i.e., risk perceptions) and interpersonal (e.g., peer approval and behavior) factors. Multinomial and binary logistic regression analyses contend students reporting lifetime alcohol use-compared to students who had never used alcohol or marijuana-perceived lower alcohol risk ( p academic performance decreased the risk of lifetime alcohol and marijuana use ( p = .043). Findings are beneficial to school nurses with students experiencing effects associated with substance use.

Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel

Directory of Open Access Journals (Sweden)

Roland Pfister

2013-10-01

Full Text Available Three different methods for extracting coefficientsof linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / LibreOffice Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed.

The analysis of nonstationary time series using regression, correlation and cointegration

DEFF Research Database (Denmark)

Johansen, Søren

2012-01-01

There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we...... analyse some monthly data from US on interest rates as an illustration of the methods...

Regression: A Bibliography.

Science.gov (United States)

Pedrini, D. T.; Pedrini, Bonnie C.

Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

Using multinomial logistic regression analysis to understand anglers willingness to substitute other fishing locations

Science.gov (United States)

Woo-Yong Hyun; Robert B. Ditton

2007-01-01

The concept of recreation substitutability has been a continuing research topic for outdoor recreation researchers. This study explores the relationships among variables regarding the willingness to substitute one location for another location. The objectives of the study are 1) to ascertain and predict the extent to which saltwater anglers were willing to substitute...

Tutorial on Using Regression Models with Count Outcomes Using R

Directory of Open Access Journals (Sweden)

A. Alexander Beaujean

2016-02-01

Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

Use of probabilistic weights to enhance linear regression myoelectric control.

Science.gov (United States)

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

2015-12-01

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

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

Science.gov (United States)

Marill, Keith A

2004-01-01

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

The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration

Directory of Open Access Journals (Sweden)

Søren Johansen

2012-06-01

Full Text Available There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we analyse some monthly data from US on interest rates as an illustration of the methods.

Tools to support interpreting multiple regression in the face of multicollinearity.

Science.gov (United States)

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner

Directory of Open Access Journals (Sweden)

Luciano Fanton

2012-01-01

Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.

Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression

DEFF Research Database (Denmark)

Scott, Neil W; Fayers, Peter M; Aaronson, Neil K

2010-01-01

Differential item functioning (DIF) methods can be used to determine whether different subgroups respond differently to particular items within a health-related quality of life (HRQoL) subscale, after allowing for overall subgroup differences in that scale. This article reviews issues that arise ...... when testing for DIF in HRQoL instruments. We focus on logistic regression methods, which are often used because of their efficiency, simplicity and ease of application....

Who Works Among Older Black and White, Well-Functioning Adults in the Health, Aging, and Body Composition Study?

Directory of Open Access Journals (Sweden)

Ronica N. Rooks PhD

2017-08-01

Full Text Available Objective: The aim of this study is to examine social, economic, and health factors related to paid work in well-functioning older adults and if and how these factors vary by race. Method: We used sex-stratified logistic and multinomial logistic regression to examine cross-sectional data in the Health, Aging, and Body Composition cohort study. The sample included 3,075 community-dwelling Black (42% and White adults aged 70 to 79 at baseline. Results: Multinomial logistic regression analyses show Black men were more likely to work full-time, and Black women were more likely to work part-time. Men with ≥US$50,000 family income were more likely to work full-time. Men with better physical functioning were more likely to work full- and part-time. Women with ≥US$50,000 family income and fewer chronic diseases were more likely to work full-time. Women who were overweight and had fewer chronic diseases were more likely to work part-time. Discussion: Results suggest that well-functioning, older Black adults were more likely to work than their White counterparts, and working relates to better health and higher income, providing support for a productive or successful aging perspective.

Using multinomial and imprecise probability for non-parametric modelling of rainfall in Manizales (Colombia

Directory of Open Access Journals (Sweden)

Ibsen Chivatá Cárdenas

2008-05-01

Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions

The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

Science.gov (United States)

Murphy, Kevin; Birn, Rasmus M; Handwerker, Daniel A; Jones, Tyler B; Bandettini, Peter A

2009-02-01

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

The multivariate Dirichlet-multinomial distribution and its application in forensic genetics to adjust for subpopulation effects using the θ-correction

DEFF Research Database (Denmark)

Tvedebrink, Torben; Eriksen, Poul Svante; Morling, Niels

2015-01-01

In this paper, we discuss the construction of a multivariate generalisation of the Dirichlet-multinomial distribution. An example from forensic genetics in the statistical analysis of DNA mixtures motivates the study of this multivariate extension. In forensic genetics, adjustment of the match...... probabilities due to remote ancestry in the population is often done using the so-called θ-correction. This correction increases the probability of observing multiple copies of rare alleles in a subpopulation and thereby reduces the weight of the evidence for rare genotypes. A recent publication by Cowell et al....... (2015) showed elegantly how to use Bayesian networks for efficient computations of likelihood ratios in a forensic genetic context. However, their underlying population genetic model assumed independence of alleles, which is not realistic in real populations. We demonstrate how the so-called θ...

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy

DEFF Research Database (Denmark)

Merlo, Juan; Wagner, Philippe; Ghith, Nermin

2016-01-01

BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

Science.gov (United States)

Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim

2012-01-01

Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…

Estimating the exceedance probability of rain rate by logistic regression

Science.gov (United States)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

The best of both worlds: Phylogenetic eigenvector regression and mapping

Directory of Open Access Journals (Sweden)

José Alexandre Felizola Diniz Filho

2015-09-01

Full Text Available Eigenfunction analyses have been widely used to model patterns of autocorrelation in time, space and phylogeny. In a phylogenetic context, Diniz-Filho et al. (1998 proposed what they called Phylogenetic Eigenvector Regression (PVR, in which pairwise phylogenetic distances among species are submitted to a Principal Coordinate Analysis, and eigenvectors are then used as explanatory variables in regression, correlation or ANOVAs. More recently, a new approach called Phylogenetic Eigenvector Mapping (PEM was proposed, with the main advantage of explicitly incorporating a model-based warping in phylogenetic distance in which an Ornstein-Uhlenbeck (O-U process is fitted to data before eigenvector extraction. Here we compared PVR and PEM in respect to estimated phylogenetic signal, correlated evolution under alternative evolutionary models and phylogenetic imputation, using simulated data. Despite similarity between the two approaches, PEM has a slightly higher prediction ability and is more general than the original PVR. Even so, in a conceptual sense, PEM may provide a technique in the best of both worlds, combining the flexibility of data-driven and empirical eigenfunction analyses and the sounding insights provided by evolutionary models well known in comparative analyses.

Reduced Rank Regression

DEFF Research Database (Denmark)

Johansen, Søren

2008-01-01

The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

International Nuclear Information System (INIS)

Janssen, I.; Stebbings, J.H.

1990-01-01

In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

Logistic Regression Analysis of Operational Errors and Routine Operations Using Sector Characteristics

National Research Council Canada - National Science Library

Pfleiderer, Elaine M; Scroggins, Cheryl L; Manning, Carol A

2009-01-01

Two separate logistic regression analyses were conducted for low- and high-altitude sectors to determine whether a set of dynamic sector characteristics variables could reliably discriminate between operational error (OE...

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

Recreation Value of Water to Wetlands in the San Joaquin Valley: Linked Multinomial Logit and Count Data Trip Frequency Models

Science.gov (United States)

Creel, Michael; Loomis, John

1992-10-01

The recreational benefits from providing increased quantities of water to wildlife and fisheries habitats is estimated using linked multinomial logit site selection models and count data trip frequency models. The study encompasses waterfowl hunting, fishing and wildlife viewing at 14 recreational resources in the San Joaquin Valley, including the National Wildlife Refuges, the State Wildlife Management Areas, and six river destinations. The economic benefits of increasing water supplies to wildlife refuges were also examined by using the estimated models to predict changing patterns of site selection and overall participation due to increases in water allocations. Estimates of the dollar value per acre foot of water are calculated for increases in water to refuges. The resulting model is a flexible and useful tool for estimating the economic benefits of alternative water allocation policies for wildlife habitat and rivers.

SHORT COMMUNICATIONS

African Journals Online (AJOL)

The relationships between S. mansoni, hookworm and S. mansoni + hookworm with P. falciparum were investigated by fitting logistic regression model taking P. falciparum as response variable. Furthermore, co-infections analysis was investigated by fitting multinomial logistic regression model where all combination of ...

Transaction cost determinants of credit governance structures of ...

African Journals Online (AJOL)

This paper explores transaction cost determinants of credit governance structures (CGS) of commercial banks in Tanzania. Descriptive statistics, linear regression model, binary and multinomial logistic regression models were employed for analysis. Findings revealed four modes of credit governance structures that are ...

Trouble Sleeping Associated with Lower Work Performance and Greater Healthcare Costs: Longitudinal Data from Kansas State Employee Wellness Program

Science.gov (United States)

Hui, Siu-kuen Azor; Grandner, Michael A.

2015-01-01

Objective To examine the relationships between employees’ trouble sleeping and absenteeism, work performance, and healthcare expenditures over a two year period. Methods Utilizing the Kansas State employee wellness program (EWP) dataset from 2008–2009, multinomial logistic regression analyses were conducted with trouble sleeping as the predictor and absenteeism, work performance, and healthcare costs as the outcomes. Results EWP participants (N=11,698 in 2008; 5,636 followed up in 2009) who had higher levels of sleep disturbance were more likely to be absent from work (all p performance ratings (all p performance, and healthcare costs. PMID:26461857

Regression Phalanxes

OpenAIRE

Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

2017-01-01

Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

Science.gov (United States)

Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O

2017-03-05

The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized

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

Science.gov (United States)

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

2006-02-15

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

The study of logistic regression of risk factor on the death cause of uranium miners

International Nuclear Information System (INIS)

Wen Jinai; Yuan Liyun; Jiang Ruyi

1999-01-01

Logistic regression model has widely been used in the field of medicine. The computer software on this model is popular, but it is worth to discuss how to use this model correctly. Using SPSS (Statistical Package for the Social Science) software, unconditional logistic regression method was adopted to carry out multi-factor analyses on the cause of total death, cancer death and lung cancer death of uranium miners. The data is from radioepidemiological database of one uranium mine. The result show that attained age is a risk factor in the logistic regression analyses of total death, cancer death and lung cancer death. In the logistic regression analysis of cancer death, there is a negative correlation between the age of exposure and cancer death. This shows that the younger the age at exposure, the bigger the risk of cancer death. In the logistic regression analysis of lung cancer death, there is a positive correlation between the cumulated exposure and lung cancer death, this show that cumulated exposure is a most important risk factor of lung cancer death on uranium miners. It has been documented by many foreign reports that the lung cancer death rate is higher in uranium miners

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

Science.gov (United States)

Marill, Keith A

2004-01-01

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

Characterization of annual disease progression of multiple sclerosis patients: A population-based study

DEFF Research Database (Denmark)

Freilich, Jonatan; Manouchehrinia, Ali; Trusheim, Mark

2017-01-01

Previous research characterizing factors influencing multiple sclerosis (MS) disease progression has typically been based on time to disease milestones (Kaplan-Meier, Cox hazard regression, etc.). A limitation of these methods is the handling of the often large groups of patients not reaching...... the milestone. To characterize clinical factors influencing MS disease progression as annual transitions from each Expanded Disability Status Scale (EDSS). The annual progression of 11,964 patients from the Swedish MS Registry was analysed with 10 multinomial logistic regressions, that is, one for transition...... from each full EDSS with explanatory variables age, sex, age at onset, time in current EDSS, highest prior EDSS, MS course and treatment. All factors (except sex) investigated had statistically significant impacts on transitions from at least one EDSS. However, significance and size of the effect...

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies

DEFF Research Database (Denmark)

Tybjærg-Hansen, Anne

2009-01-01

Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements...... of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study......-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies...

Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate

Science.gov (United States)

Albaqshi, Amani Mohammed H.

2017-01-01

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Contraceptive awareness among men in Bangladesh.

Science.gov (United States)

Islam, Mohammad Amirul; Padmadas, Sabu S; Smith, Peter W F

2006-04-01

A considerable gap exists between contraceptive awareness and use. Traditional approaches to measuring awareness are inadequate to properly understand the linkages between awareness and use. The objective of this study was to examine the degree of men's modern contraceptive awareness in Bangladesh and the associated determinants and further testing of a hypothesis that current contraceptive use confers a high degree of method awareness. This study used the couple data set from the Bangladesh Demographic and Health Survey (1999-2000). A two-level, multinomial logistic regression was used with the degree of contraceptive awareness as the dependent variable. The degree of awareness was measured by the reported number of modern contraceptive methods known among men aged 15-59 years. Men's responses on method awareness were classified according to those reported spontaneously and probed. Nearly 100% of the study participants reported having heard of at least one method and about half reported awareness of at least eight different methods of contraception. Multinomial logistic regression analyses showed that older and educated men were more likely to have reported a high degree of awareness. The findings confirmed our hypothesis that current contraceptive use is likely to confer a high degree of modern method awareness among men (pknowledge of contraceptive methods to improve the uptake of especially male-based modern methods.

Information fusion via constrained principal component regression for robust quantification with incomplete calibrations

International Nuclear Information System (INIS)

Vogt, Frank

2013-01-01

Graphical abstract: Analysis Task: Determine the albumin (= protein) concentration in microalgae cells as a function of the cells’ nutrient availability. Left Panel: The predicted albumin concentrations as obtained by conventional principal component regression features low reproducibility and are partially higher than the concentrations of algae in which albumin is contained. Right Panel: Augmenting an incomplete PCR calibration with additional expert information derives reasonable albumin concentrations which now reveal a significant dependency on the algae's nutrient situation. -- Highlights: •Make quantitative analyses of compounds embedded in largely unknown chemical matrices robust. •Improved concentration prediction with originally insufficient calibration models. •Chemometric approach for incorporating expertise from other fields and/or researchers. •Ensure chemical, biological, or medicinal meaningfulness of quantitative analyses. -- Abstract: Incomplete calibrations are encountered in many applications and hamper chemometric data analyses. Such situations arise when target analytes are embedded in a chemically complex matrix from which calibration concentrations cannot be determined with reasonable efforts. In other cases, the samples’ chemical composition may fluctuate in an unpredictable way and thus cannot be comprehensively covered by calibration samples. The reason for calibration model to fail is the regression principle itself which seeks to explain measured data optimally in terms of the (potentially incomplete) calibration model but does not consider chemical meaningfulness. This study presents a novel chemometric approach which is based on experimentally feasible calibrations, i.e. concentration series of the target analytes outside the chemical matrix (‘ex situ calibration’). The inherent lack-of-information is then compensated by incorporating additional knowledge in form of regression constraints. Any outside knowledge can be

Modelování vybraných ukazatelů o finanční situaci domácností v České republice

Czech Academy of Sciences Publication Activity Database

Řezanková, Hana

2013-01-01

Roč. 21, č. 3 (2013), s. 32-50 ISSN 0572-3043 R&D Projects: GA ČR GAP202/10/0262 Keywords : Czech households * financial indicator * household classification * classification tree * binary logistic regression * multinomial logistic regression * F-measure Subject RIV: BB - Applied Statistics, Operational Research

Paradox of spontaneous cancer regression: implications for fluctuational radiothermy and radiotherapy

International Nuclear Information System (INIS)

Roy, Prasun K.; Dutta Majumder, D.; Biswas, Jaydip

1999-01-01

Spontaneous regression of malignant tumours without treatment is a most enigmatic phenomenon with immense therapeutic potentialities. We analyse such cases to find that the commonest cause is a preceding episode of high fever-induced thermal fluctuation which produce fluctuation of biochemical and immunological parameters. Using Prigogine-Glansdorff thermodynamic stability formalism and biocybernetic principles, we develop the theoretical foundation of tumour regression induced by thermal, radiational or oxygenational fluctuations. For regression, a preliminary threshold condition of fluctuations is derived, namely σ > 2.83. We present some striking confirmation of such fluctuation-induced regression of various therapy-resistant masses as Ewing tumour, neurogranuloma and Lewis lung carcinoma by utilising σ > 2.83. Our biothermodynamic stability model of malignancy appears to illuminate the marked increase of aggressiveness of mammalian malignancy which occurred around 250 million years ago when homeothermic warm-blooded pre-mammals evolved. Using experimental data, we propose a novel approach of multi-modal hyper-fluctuation therapy involving modulation of radiotherapeutic hyper-fractionation, temperature, radiothermy and immune-status. (author)

Oral Microbiota and Risk for Esophageal Squamous Cell Carcinoma in a High-Risk Area of China.

Directory of Open Access Journals (Sweden)

Xingdong Chen

Full Text Available Poor oral health has been linked with an increased risk of esophageal squamous cell carcinoma (ESCC. We investigated whether alteration of oral microbiota is associated with ESCC risk. Fasting saliva samples were collected from 87 incident and histopathologicallly diagnosed ESCC cases, 63 subjects with dysplasia and 85 healthy controls. All subjects were also interviewed with a questionnaire. V3-V4 region of 16S rRNA was amplified and sequenced by 454-pyrosequencing platform. Carriage of each genus was compared by means of multivariate-adjusted odds ratios derived from logistic regression model. Relative abundance was compared using Metastats method. Beta diversity was estimated using Unifrac and weighted Unifrac distances. Principal coordinate analysis (PCoA was applied to ordinate dissimilarity matrices. Multinomial logistic regression was used to compare the coordinates between different groups. ESCC subjects had an overall decreased microbial diversity compared to control and dysplasia subjects (P<0.001. Decreased carriage of genera Lautropia, Bulleidia, Catonella, Corynebacterium, Moryella, Peptococcus and Cardiobacterium were found in ESCC subjects compared to non-ESCC subjects. Multinomial logistic regression analyses on PCoA coordinates also revealed that ESCC subjects had significantly different levels for several coordinates compared to non-ESCC subjects. In conclusion, we observed a correlation between altered salivary bacterial microbiota and ESCC risk. The results of our study on the saliva microbiome are of particular interest as it reflects the shift in microbial communities. Further studies are warranted to verify this finding, and if being verified, to explore the underlying mechanisms.

Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour

International Nuclear Information System (INIS)

Zhou, Cuiping; Zhang, Xiang; Duan, Xiaohui; Hu, Huijun; Wang, Dongye; Shen, Jun

2016-01-01

To determine the predictive CT imaging features for risk stratifications in patients with primary gastrointestinal stromal tumours (GISTs). One hundred and twenty-nine patients with histologically confirmed primary GISTs (diameter >2 cm) were enrolled. CT imaging features were reviewed. Tumour risk stratifications were determined according to the 2008 NIH criteria where GISTs were classified into four categories according to the tumour size, location, mitosis count, and tumour rupture. The association between risk stratifications and CT features was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. CT imaging features including tumour margin, size, shape, tumour growth pattern, direct organ invasion, necrosis, enlarged vessels feeding or draining the mass (EVFDM), lymphadenopathy, and contrast enhancement pattern were associated with the risk stratifications, as determined by univariate analysis (P < 0.05). Only lesion size, growth pattern and EVFDM remained independent risk factors in multinomial logistic regression analysis (OR = 3.480-100.384). ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.806 (95 % CI: 0.727-0.885). CT features including lesion size, tumour growth pattern, and EVFDM were predictors of the risk stratifications for GIST. (orig.)

Physics constrained nonlinear regression models for time series

International Nuclear Information System (INIS)

Majda, Andrew J; Harlim, John

2013-01-01

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

Science.gov (United States)

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Regressão múltipla stepwise e hierárquica em Psicologia Organizacional: aplicações, problemas e soluções Stepwise and hierarchical multiple regression in organizational psychology: Applications, problemas and solutions

Directory of Open Access Journals (Sweden)

Gardênia Abbad

2002-01-01

Full Text Available Este artigo discute algumas aplicações das técnicas de análise de regressão múltipla stepwise e hierárquica, as quais são muito utilizadas em pesquisas da área de Psicologia Organizacional. São discutidas algumas estratégias de identificação e de solução de problemas relativos à ocorrência de erros do Tipo I e II e aos fenômenos de supressão, complementaridade e redundância nas equações de regressão múltipla. São apresentados alguns exemplos de pesquisas nas quais esses padrões de associação entre variáveis estiveram presentes e descritas as estratégias utilizadas pelos pesquisadores para interpretá-los. São discutidas as aplicações dessas análises no estudo de interação entre variáveis e na realização de testes para avaliação da linearidade do relacionamento entre variáveis. Finalmente, são apresentadas sugestões para lidar com as limitações das análises de regressão múltipla (stepwise e hierárquica.This article discusses applications of stepwise and hierarchical multiple regression analyses to research in organizational psychology. Strategies for identifying type I and II errors, and solutions to potential problems that may arise from such errors are proposed. In addition, phenomena such as suppression, complementarity, and redundancy are reviewed. The article presents examples of research where these phenomena occurred, and the manner in which they were explained by researchers. Some applications of multiple regression analyses to studies involving between-variable interactions are presented, along with tests used to analyze the presence of linearity among variables. Finally, some suggestions are provided for dealing with limitations implicit in multiple regression analyses (stepwise and hierarchical.

Use of probabilistic weights to enhance linear regression myoelectric control

Science.gov (United States)

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

2015-12-01

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

Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

Science.gov (United States)

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

2016-04-01

The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption

Energy Technology Data Exchange (ETDEWEB)

Reddy, T.A. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States)); Claridge, D.E. (Energy Systems Lab., Texas A and M Univ., College Station, TX (United States))

1994-01-01

Multiple regression modeling of monitored building energy use data is often faulted as a reliable means of predicting energy use on the grounds that multicollinearity between the regressor variables can lead both to improper interpretation of the relative importance of the various physical regressor parameters and to a model with unstable regressor coefficients. Principal component analysis (PCA) has the potential to overcome such drawbacks. While a few case studies have already attempted to apply this technique to building energy data, the objectives of this study were to make a broader evaluation of PCA and multiple regression analysis (MRA) and to establish guidelines under which one approach is preferable to the other. Four geographic locations in the US with different climatic conditions were selected and synthetic data sequence representative of daily energy use in large institutional buildings were generated in each location using a linear model with outdoor temperature, outdoor specific humidity and solar radiation as the three regression variables. MRA and PCA approaches were then applied to these data sets and their relative performances were compared. Conditions under which PCA seems to perform better than MRA were identified and preliminary recommendations on the use of either modeling approach formulated. (orig.)

The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate

Directory of Open Access Journals (Sweden)

Mach Łukasz

2017-06-01

Full Text Available The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.

Tax System in Poland – Progressive or Regressive?

Directory of Open Access Journals (Sweden)

Jacek Tomkiewicz

2016-03-01

Full Text Available Purpose: To analyse the impact of the Polish fiscal regime on the general revenue of the country, and specifically to establish whether the cumulative tax burden borne by Polish households is progressive or regressive.Methodology: On the basis of Eurostat and OECD data, the author has analysed fiscal regimes in EU Member States and in OECD countries. The tax burden of households within different income groups has also been examined pursuant to applicable fiscal laws and data pertaining to the revenue and expenditure of households published by the Central Statistical Office (CSO.Conclusions: The fiscal regime in Poland is regressive; that is, the relative fiscal burden decreases as the taxpayer’s income increases.Research Implications: The article contributes to the on-going discussion on social cohesion, in particular with respect to economic policy instruments aimed at the redistribution of income within the economy.Originality: The author presents an analysis of data pertaining to fiscal policies in EU Member States and OECD countries and assesses the impact of the legal environment (fiscal regime and social security system in Poland on income distribution within the economy. The impact of the total tax burden (direct and indirect taxes, social security contributions on the economic situation of households from different income groups has been calculated using an original formula.

The non-condition logistic regression analysis of the reason of hypothyroidism after hyperthyroidism with 131I treatment

International Nuclear Information System (INIS)

Dang Yaping; Hu Guoying; Meng Xianwen

1994-01-01

There are many opinions on the reason of hypothyroidism after hyperthyroidism with 131 I treatment. In this respect, there are a few scientific analyses and reports. The non-condition logistic regression solved this problem successfully. It has a higher scientific value and confidence in the risk factor analysis. 748 follow-up patients' data were analysed by the non-condition logistic regression. The results shown that the half-life and 131 I dose were the main causes of the incidence of hypothyroidism. The degree of confidence is 92.4%

Spatial regression analysis on 32 years of total column ozone data

NARCIS (Netherlands)

Knibbe, J.S.; van der A, J.R.; de Laat, A.T.J.

2014-01-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter

Economic Analyses of Ware Yam Production in Orlu Agricultural ...

African Journals Online (AJOL)

Economic Analyses of Ware Yam Production in Orlu Agricultural Zone of Imo State. ... International Journal of Agriculture and Rural Development ... statistics, gross margin analysis, marginal analysis and multiple regression analysis. Results ...

An econometric analysis of changes in arable land utilization using multinomial logit model in Pinggu district, Beijing, China.

Science.gov (United States)

Xu, Yueqing; McNamara, Paul; Wu, Yanfang; Dong, Yue

2013-10-15

Arable land in China has been decreasing as a result of rapid population growth and economic development as well as urban expansion, especially in developed regions around cities where quality farmland quickly disappears. This paper analyzed changes in arable land utilization during 1993-2008 in the Pinggu district, Beijing, China, developed a multinomial logit (MNL) model to determine spatial driving factors influencing arable land-use change, and simulated arable land transition probabilities. Land-use maps, as well as social-economic and geographical data were used in the study. The results indicated that arable land decreased significantly between 1993 and 2008. Lost arable land shifted into orchard, forestland, settlement, and transportation land. Significant differences existed for arable land transitions among different landform areas. Slope, elevation, population density, urbanization rate, distance to settlements, and distance to roadways were strong drivers influencing arable land transition to other uses. The MNL model was proved effective for predicting transition probabilities in land use from arable land to other land-use types, thus can be used for scenario analysis to develop land-use policies and land-management measures in this metropolitan area. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2012-07-07

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

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: A Quantile Regression Approach

Science.gov (United States)

Shafiq, M. Najeeb

2013-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

Measuring Response Styles Across the Big Five: A Multiscale Extension of an Approach Using Multinomial Processing Trees.

Science.gov (United States)

Khorramdel, Lale; von Davier, Matthias

2014-01-01

This study shows how to address the problem of trait-unrelated response styles (RS) in rating scales using multidimensional item response theory. The aim is to test and correct data for RS in order to provide fair assessments of personality. Expanding on an approach presented by Böckenholt (2012), observed rating data are decomposed into multiple response processes based on a multinomial processing tree. The data come from a questionnaire consisting of 50 items of the International Personality Item Pool measuring the Big Five dimensions administered to 2,026 U.S. students with a 5-point rating scale. It is shown that this approach can be used to test if RS exist in the data and that RS can be differentiated from trait-related responses. Although the extreme RS appear to be unidimensional after exclusion of only 1 item, a unidimensional measure for the midpoint RS is obtained only after exclusion of 10 items. Both RS measurements show high cross-scale correlations and item response theory-based (marginal) reliabilities. Cultural differences could be found in giving extreme responses. Moreover, it is shown how to score rating data to correct for RS after being proved to exist in the data.

Trouble Sleeping Associated With Lower Work Performance and Greater Health Care Costs: Longitudinal Data From Kansas State Employee Wellness Program.

Science.gov (United States)

Hui, Siu-kuen Azor; Grandner, Michael A

2015-10-01

To examine the relationships between employees' trouble sleeping and absenteeism, work performance, and health care expenditures over a 2-year period. Utilizing the Kansas State employee wellness program (EWP) data set from 2008 to 2009, multinomial logistic regression analyses were conducted with trouble sleeping as the predictor and absenteeism, work performance, and health care costs as the outcomes. EWP participants (N = 11,698 in 2008; 5636 followed up in 2009) who had higher levels of sleep disturbance were more likely to be absent from work (all P work performance ratings (all P health care costs (P work attendance, work performance, and health care costs.

The levels and patterns of resilience among male street children in Dhaka City.

Science.gov (United States)

Sayem, Amir M; Kidd, Sean A

2013-01-01

Abstract This study was carried out to identify the factors affecting resilience among street children. The study used a convenience sample of 366 male street children aged 14-17 years in Dhaka City, Bangladesh. The levels of resilience among the street children were mostly moderately low-low (54.1%), followed by low (29.0%), and moderately high-high resilience (16.9%). Multinomial logistic regression analyses indicated that the pre-street factors of education, economic constraint in the family, as well as experiencing and witnessing domestic violence were associated with resilience, with associated street characteristics being drug abuse, experiencing and witnessing violence on street, access to prevention services, and political involvement.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

Science.gov (United States)

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Logistic Regression in the Identification of Hazards in Construction

Science.gov (United States)

Drozd, Wojciech

2017-10-01

The construction site and its elements create circumstances that are conducive to the formation of risks to safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This article attempts to analyse the characteristics related to the construction site, in order to indicate their importance in defining the circumstances of accidents at work. The study includes sites inspected in 2014 - 2016 by the employees of the District Labour Inspectorate in Krakow (Poland). The analysed set of detailed (disaggregated) data includes both quantitative and qualitative characteristics. The substantive task focused on classification modelling in the identification of hazards in construction and identifying those of the analysed characteristics that are important in an accident. In terms of methodology, resource data analysis using statistical classifiers, in the form of logistic regression, was the method used.

Type 2 diabetes mellitus unawareness, prevalence, trends and risk factors: National Health and Nutrition Examination Survey (NHANES) 1999–2010

Science.gov (United States)

Zhang, Nana; Yang, Xin; Zhu, Xiaolin; Zhao, Bin; Huang, Tianyi

2017-01-01

Objectives To determine whether the associations with key risk factors in patients with diagnosed and undiagnosed type 2 diabetes mellitus (T2DM) are different using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010. Methods The study analysed the prevalence and association with risk factors of undiagnosed and diagnosed T2DM using a regression model and a multinomial logistic regression model. Data from the NHANES 1999–2010 were used for the analyses. Results The study analysed data from 10 570 individuals. The overall prevalence of diagnosed and undiagnosed T2DM increased significantly from 1999 to 2010. The prevalence of undiagnosed T2DM was significantly higher in non-Hispanic whites, in individuals educational level had no effect on T2DM diagnosis rates. Though diagnosed T2DM was associated with favourable diet/carbohydrate intake behavioural changes, it had no effect on physical activity levels. Conclusion The overall T2DM prevalence increased between 1999 and 2010, particularly for undiagnosed T2DM in patients that were formerly classified as low risk. PMID:28415936

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

Science.gov (United States)

Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

2017-06-01

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

Use of multiple linear regression and logistic regression models to investigate changes in birthweight for term singleton infants in Scotland.

Science.gov (United States)

Bonellie, Sandra R

2012-10-01

To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother.   Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

Disentangling WTP per QALY data: different analytical approaches, different answers.

Science.gov (United States)

Gyrd-Hansen, Dorte; Kjaer, Trine

2012-03-01

A large random sample of the Danish general population was asked to value health improvements by way of both the time trade-off elicitation technique and willingness-to-pay (WTP) using contingent valuation methods. The data demonstrate a high degree of heterogeneity across respondents in their relative valuations on the two scales. This has implications for data analysis. We show that the estimates of WTP per QALY are highly sensitive to the analytical strategy. For both open-ended and dichotomous choice data we demonstrate that choice of aggregated approach (ratios of means) or disaggregated approach (means of ratios) affects estimates markedly as does the interpretation of the constant term (which allows for disproportionality across the two scales) in the regression analyses. We propose that future research should focus on why some respondents are unwilling to trade on the time trade-off scale, on how to interpret the constant value in the regression analyses, and on how best to capture the heterogeneity in preference structures when applying mixed multinomial logit. Copyright © 2011 John Wiley & Sons, Ltd.

Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics

OpenAIRE

Ole E. Barndorff-Nielsen; Neil Shephard

2002-01-01

This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

Applied logistic regression

CERN Document Server

Hosmer, David W; Sturdivant, Rodney X

2013-01-01

 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Directory of Open Access Journals (Sweden)

Gascón Adrià

2017-10-01

Full Text Available We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013, and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

Science.gov (United States)

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

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

NARCIS (Netherlands)

Eilers, P.H.C.; Roder, E.; Savelkoul, H.F.J.; Wijk, van R.G.

2012-01-01

Background Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical

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

NARCIS (Netherlands)

P.H.C. Eilers (Paul); E. Röder (Esther); H.F.J. Savelkoul (Huub); R. Gerth van Wijk (Roy)

2012-01-01

textabstractBackground: Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced

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

Science.gov (United States)

Bennett, Bradley C; Husby, Chad E

2008-03-28

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

The Role of Cultural Capital in Higher Education Access and Institutional Choice

Directory of Open Access Journals (Sweden)

Iva Košutić

2017-03-01

Full Text Available This paper aims to explore social inequalities in school achievement and educational decision-making of the final-year students of secondary schools in the City of Zagreb and Zagreb County, Croatia (N = 534. The theoretical framework of the paper was Bourdieu’s theory of cultural and social reproduction (1977a. The main objectives were an analysis of the association between the students’ cultural capital and their school achievement and analyses of the predictive power of the cultural capital theory in the context of educational decisions in the transition to tertiary education. In the analysis of school achievement, sequential multiple regression analysis was used, while in the analyses of educational decisions logistic regression analyses were performed (binary and multinomial logistic regression. The results indicated that cultural capital had statistically significant correlation with school performance. Among the cultural capital indicators, statistically significant predictors of the probability of the intention to enrol into vocational higher education were the material dimension of cultural capital and naturalness of higher education aspirations of students. For the prediction of the probability of intention to enrol in university, significant predictors were embodied cultural capital, the naturalness of higher education aspirations of students, and father’s educational level. The study results on a selected sample of graduates tend to support Bourdieu’s theory of cultural reproduction through education.

Vector regression introduced

Directory of Open Access Journals (Sweden)

Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

A note on the use of multiple linear regression in molecular ecology.

Science.gov (United States)

Frasier, Timothy R

2016-03-01

Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information. © 2015 John Wiley & Sons Ltd.

The Effect of Task Duration on Event-Based Prospective Memory: A Multinomial Modeling Approach

Directory of Open Access Journals (Sweden)

Hongxia Zhang

2017-11-01

Full Text Available Remembering to perform an action when a specific event occurs is referred to as Event-Based Prospective Memory (EBPM. This study investigated how EBPM performance is affected by task duration by having university students (n = 223 perform an EBPM task that was embedded within an ongoing computer-based color-matching task. For this experiment, we separated the overall task’s duration into the filler task duration and the ongoing task duration. The filler task duration is the length of time between the intention and the beginning of the ongoing task, and the ongoing task duration is the length of time between the beginning of the ongoing task and the appearance of the first Prospective Memory (PM cue. The filler task duration and ongoing task duration were further divided into three levels: 3, 6, and 9 min. Two factors were then orthogonally manipulated between-subjects using a multinomial processing tree model to separate the effects of different task durations on the two EBPM components. A mediation model was then created to verify whether task duration influences EBPM via self-reminding or discrimination. The results reveal three points. (1 Lengthening the duration of ongoing tasks had a negative effect on EBPM performance while lengthening the duration of the filler task had no significant effect on it. (2 As the filler task was lengthened, both the prospective and retrospective components show a decreasing and then increasing trend. Also, when the ongoing task duration was lengthened, the prospective component decreased while the retrospective component significantly increased. (3 The mediating effect of discrimination between the task duration and EBPM performance was significant. We concluded that different task durations influence EBPM performance through different components with discrimination being the mediator between task duration and EBPM performance.

Applied linear regression

CERN Document Server

Weisberg, Sanford

2013-01-01

Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Directory of Open Access Journals (Sweden)

Minh Vu Trieu

2017-03-01

Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Science.gov (United States)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit

Directory of Open Access Journals (Sweden)

Annie Chu

2009-04-01

Full Text Available The web-based, Java-written SOCR (Statistical Online Computational Resource toolshave been utilized in many undergraduate and graduate level statistics courses for sevenyears now (Dinov 2006; Dinov et al. 2008b. It has been proven that these resourcescan successfully improve students' learning (Dinov et al. 2008b. Being rst publishedonline in 2005, SOCR Analyses is a somewhat new component and it concentrate on datamodeling for both parametric and non-parametric data analyses with graphical modeldiagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learn-ing for high school and undergraduate students. As we have already implemented SOCRDistributions and Experiments, SOCR Analyses and Charts fulll the rest of a standardstatistics curricula. Currently, there are four core components of SOCR Analyses. Linearmodels included in SOCR Analyses are simple linear regression, multiple linear regression,one-way and two-way ANOVA. Tests for sample comparisons include t-test in the para-metric category. Some examples of SOCR Analyses' in the non-parametric category areWilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirno testand Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman'stest and Fisher's exact test. The last component of Analyses is a utility for computingsample sizes for normal distribution. In this article, we present the design framework,computational implementation and the utilization of SOCR Analyses.

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

Science.gov (United States)

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

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

Science.gov (United States)

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

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

Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; systematic review and meta-regression analyses.

Science.gov (United States)

Samdal, Gro Beate; Eide, Geir Egil; Barth, Tom; Williams, Geoffrey; Meland, Eivind

2017-03-28

This systematic review aims to explain the heterogeneity in results of interventions to promote physical activity and healthy eating for overweight and obese adults, by exploring the differential effects of behaviour change techniques (BCTs) and other intervention characteristics. The inclusion criteria specified RCTs with ≥ 12 weeks' duration, from January 2007 to October 2014, for adults (mean age ≥ 40 years, mean BMI ≥ 30). Primary outcomes were measures of healthy diet or physical activity. Two reviewers rated study quality, coded the BCTs, and collected outcome results at short (≤6 months) and long term (≥12 months). Meta-analyses and meta-regressions were used to estimate effect sizes (ES), heterogeneity indices (I 2 ) and regression coefficients. We included 48 studies containing a total of 82 outcome reports. The 32 long term reports had an overall ES = 0.24 with 95% confidence interval (CI): 0.15 to 0.33 and I 2  = 59.4%. The 50 short term reports had an ES = 0.37 with 95% CI: 0.26 to 0.48, and I 2  = 71.3%. The number of BCTs unique to the intervention group, and the BCTs goal setting and self-monitoring of behaviour predicted the effect at short and long term. The total number of BCTs in both intervention arms and using the BCTs goal setting of outcome, feedback on outcome of behaviour, implementing graded tasks, and adding objects to the environment, e.g. using a step counter, significantly predicted the effect at long term. Setting a goal for change; and the presence of reporting bias independently explained 58.8% of inter-study variation at short term. Autonomy supportive and person-centred methods as in Motivational Interviewing, the BCTs goal setting of behaviour, and receiving feedback on the outcome of behaviour, explained all of the between study variations in effects at long term. There are similarities, but also differences in effective BCTs promoting change in healthy eating and physical activity and

Introduction to regression graphics

CERN Document Server

Cook, R Dennis

2009-01-01

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

Science.gov (United States)

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

Regression analysis of growth responses to water depth in three wetland plant species

DEFF Research Database (Denmark)

Sorrell, Brian K; Tanner, Chris C; Brix, Hans

2012-01-01

depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...

Time-trend of melanoma screening practice by primary care physicians: A meta-regression analysis

OpenAIRE

Valachis, Antonis; Mauri, Davide; Karampoiki, Vassiliki; Polyzos, Nikolaos P; Cortinovis, Ivan; Koukourakis, Georgios; Zacharias, Georgios; Xilomenos, Apostolos; Tsappi, Maria; Casazza, Giovanni

2009-01-01

Objective To assess whether the proportion of primary care physicians implementing full body skin examination (FBSE) to screen for melanoma changed over time. Methods Meta-regression analyses of available data. Data Sources: MEDLINE, ISI, Cochrane Central Register of Controlled Trials. Results Fifteen studies surveying 10,336 physicians were included in the analyses. Overall, 15%?82% of them reported to perform FBSE to screen for melanoma. The proportion of physicians using FBSE screening ten...

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

Science.gov (United States)

Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model

Drivers of multidimensional eco-innovation: empirical evidence from the Brazilian industry.

Science.gov (United States)

da Silva Rabêlo, Olivan; de Azevedo Melo, Andrea Sales Soares

2018-03-08

The study analyses the relationships between the main drivers of eco-innovation introduced by innovative industries, focused on cooperation strategy. Eco-innovation is analysed by means of a multidimensional identification strategy, showing the relationships between the independent variables and the variable of interest. The literature discussing environmental innovation is different from the one discussing other types of innovation inasmuch as it seeks to grasp its determinants and to mostly highlight the relevance of environmental regulation. The key feature of this paper is that it ascribes special relevance to cooperation strategy with external partners and to the propensity of innovative industry introducing eco-innovation. A sample of 35,060 Brazilian industries were analysed, between 2003 and 2011, by means of Binomial, Multinomial and Ordinal logistic regressions with microdata collected with the research and innovation department (PINTEC) from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística). The econometric results estimated by the Logit Multinomial method suggest that the cooperation with external partners practiced by innovative industries facilitates the adoption of eco-innovation in dimension 01 with probability of 64.59%, 57.63% in dimension 02 and 81.02% in dimension 03. The data reveal that the higher the degree of eco-innovation complexity, the harder industries seek to obtain cooperation with external partners. When calculating with the Logit Ordinal and Binomial models, cooperation increases the probability that the industry is eco-innovative in 65.09% and 89.34%, respectively. Environmental regulation and innovation in product and information management were also positively correlated as drivers of eco-innovation.

Multivariate differential analyses of adolescents' experiences of aggression in families

Directory of Open Access Journals (Sweden)

Chris Myburgh

2011-01-01

Full Text Available Aggression is part of South African society and has implications for the mental health of persons living in South Africa. If parents are aggressive adolescents are also likely to be aggressive and that will impact negatively on their mental health. In this article the nature and extent of adolescents' experiences of aggression and aggressive behaviour in the family are investigated. A deductive explorative quantitative approach was followed. Aggression is reasoned to be dependent on aspects such as self-concept, moral reasoning, communication, frustration tolerance and family relationships. To analyse the data from questionnaires of 101 families (95 adolescents, 95 mothers and 91 fathers Cronbach Alpha, various consecutive first and second order factor analyses, correlations, multiple regression, MANOVA, ANOVA and Scheffè/ Dunnett tests were used. It was found that aggression correlated negatively with the independent variables; and the correlations between adolescents and their parents were significant. Regression analyses indicated that different predictors predicted aggression. Furthermore, differences between adolescents and their parents indicated that the experienced levels of aggression between adolescents and their parents were small. Implications for education are given.

Latitudinal variation in the symbiotic dinoflagellateSymbiodiniumof the common reef zoantharianPalythoa tuberculosaon the Saudi Arabian coast of the Red Sea

KAUST Repository

Reimer, James D.; Herrera Sarrias, Marcela; Gatins, Remy; Roberts, May B.; Parkinson, John E.; Berumen, Michael L.

2016-01-01

Main conclusions Multinomial logistic regression analyses established that predictions based on the combination of temperature, chlorophyll-a and salinity accurately reflected symbiont distributions in the central and northern Red Sea. Palythoa tuberculosa host Pt-1-a in the coldest region, the Gulf of Aqaba (annual average SST = 24.5–25.0 °C), while immediately to the south Pt-3-a dominates (SST = 26.0–26.5 °C), with warmest southern sites dominated by Pt-3-b (SST > 26.5 °C). The Gulf of Aqaba is a unique environment, and more research on Symbiodinium outside the Gulf is required to understand symbiont diversity patterns within the Red Sea.

Trajectories of suicidal ideation over 6 months among 482 outpatients with bipolar disorder

DEFF Research Database (Denmark)

Köhler-Forsberg, Ole; Madsen, Trine; Behrendt-Møller, Ida

2017-01-01

INTRODUCTION: Suicidal ideation occurs frequently among individuals with bipolar disorder; however, its course and persistence over time remains unclear. We aimed to investigate 6-months trajectories of suicidal ideation among adults with bipolar disorder. METHODS: The Bipolar CHOICE study...... randomized 482 outpatients with bipolar disorder to 6 months of lithium- or quetiapine-based treatment including other psychotropic medications as clinically indicated. Participants were asked at 9 visits about suicidal ideation using the Concise Health Risk Tracking scale. We performed latent Growth Mixture...... Modelling analysis to empirically identify trajectories of suicidal ideation. Multinomial logistic regression analyses were applied to estimate associations between trajectories and potential predictors. RESULTS: We identified four distinct trajectories. The Moderate-Stable group represented 11...

Preference learning with evolutionary Multivariate Adaptive Regression Spline model

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll

2015-01-01

This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

A random regression model in analysis of litter size in pigs | Lukovi& ...

African Journals Online (AJOL)

Dispersion parameters for number of piglets born alive (NBA) were estimated using a random regression model (RRM). Two data sets of litter records from the Nemščak farm in Slovenia were used for analyses. The first dataset (DS1) included records from the first to the sixth parity. The second dataset (DS2) was extended ...

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Optimising import phytosanitary inspection

NARCIS (Netherlands)

Surkov, I.

2007-01-01

Keywords: quarantine pest, plant health policy, optimization, import phytosanitary inspection, ‘reduced checks’, optimal allocation of resources, multinomial logistic regression, the Netherlands World trade is a major vector of spread of quarantine plant pests. Border phytosanitary inspection

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

Science.gov (United States)

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Application of support vector regression (SVR) for stream flow prediction on the Amazon basin

CSIR Research Space (South Africa)

Du Toit, Melise

2016-10-01

Full Text Available regression technique is used in this study to analyse historical stream flow occurrences and predict stream flow values for the Amazon basin. Up to twelve month predictions are made and the coefficient of determination and root-mean-square error are used...

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Linear regression in astronomy. I

Science.gov (United States)

Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

1990-01-01

Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

Logic regression and its extensions.

Science.gov (United States)

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Practical Aspects of Log-ratio Coordinate Representations in Regression with Compositional Response

Directory of Open Access Journals (Sweden)

Fišerová Eva

2016-10-01

Full Text Available Regression analysis with compositional response, observations carrying relative information, is an appropriate tool for statistical modelling in many scientific areas (e.g. medicine, geochemistry, geology, economics. Even though this technique has been recently intensively studied, there are still some practical aspects that deserve to be further analysed. Here we discuss the issue related to the coordinate representation of compositional data. It is shown that linear relation between particular orthonormal coordinates and centred log-ratio coordinates can be utilized to simplify the computation concerning regression parameters estimation and hypothesis testing. To enhance interpretation of regression parameters, the orthogonal coordinates and their relation with orthonormal and centred log-ratio coordinates are presented. Further we discuss the quality of prediction in different coordinate system. It is shown that the mean squared error (MSE for orthonormal coordinates is less or equal to the MSE for log-transformed data. Finally, an illustrative real-world example from geology is presented.

Tumor regression patterns in retinoblastoma

International Nuclear Information System (INIS)

Zafar, S.N.; Siddique, S.N.; Zaheer, N.

2016-01-01

To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

Analysis of Palm Oil Production, Export, and Government Consumption to Gross Domestic Product of Five Districts in West Kalimantan by Panel Regression

Science.gov (United States)

Sulistianingsih, E.; Kiftiah, M.; Rosadi, D.; Wahyuni, H.

2017-04-01

Gross Domestic Product (GDP) is an indicator of economic growth in a region. GDP is a panel data, which consists of cross-section and time series data. Meanwhile, panel regression is a tool which can be utilised to analyse panel data. There are three models in panel regression, namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The models will be chosen based on results of Chow Test, Hausman Test and Lagrange Multiplier Test. This research analyses palm oil about production, export, and government consumption to five district GDP are in West Kalimantan, namely Sanggau, Sintang, Sambas, Ketapang and Bengkayang by panel regression. Based on the results of analyses, it concluded that REM, which adjusted-determination-coefficient is 0,823, is the best model in this case. Also, according to the result, only Export and Government Consumption that influence GDP of the districts.

Combining Alphas via Bounded Regression

Directory of Open Access Journals (Sweden)

Zura Kakushadze

2015-11-01

Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

Type 2 diabetes mellitus unawareness, prevalence, trends and risk factors: National Health and Nutrition Examination Survey (NHANES) 1999-2010.

Science.gov (United States)

Zhang, Nana; Yang, Xin; Zhu, Xiaolin; Zhao, Bin; Huang, Tianyi; Ji, Qiuhe

2017-04-01

Objectives To determine whether the associations with key risk factors in patients with diagnosed and undiagnosed type 2 diabetes mellitus (T2DM) are different using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010. Methods The study analysed the prevalence and association with risk factors of undiagnosed and diagnosed T2DM using a regression model and a multinomial logistic regression model. Data from the NHANES 1999-2010 were used for the analyses. Results The study analysed data from 10 570 individuals. The overall prevalence of diagnosed and undiagnosed T2DM increased significantly from 1999 to 2010. The prevalence of undiagnosed T2DM was significantly higher in non-Hispanic whites, in individuals 130-159 mg/dl) or very high (≥220 mg/dl) non-high-density lipoprotein cholesterol levels compared with diagnosed T2DM. Body mass index, low economic status or low educational level had no effect on T2DM diagnosis rates. Though diagnosed T2DM was associated with favourable diet/carbohydrate intake behavioural changes, it had no effect on physical activity levels. Conclusion The overall T2DM prevalence increased between 1999 and 2010, particularly for undiagnosed T2DM in patients that were formerly classified as low risk.

riskRegression

DEFF Research Database (Denmark)

Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

2017-01-01

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....

Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Shephard, N.

2004-01-01

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....

Regression in autistic spectrum disorders.

Science.gov (United States)

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

Item Response Theory Modeling and Categorical Regression Analyses of the Five-Factor Model Rating Form: A Study on Italian Community-Dwelling Adolescent Participants and Adult Participants.

Science.gov (United States)

Fossati, Andrea; Widiger, Thomas A; Borroni, Serena; Maffei, Cesare; Somma, Antonella

2017-06-01

To extend the evidence on the reliability and construct validity of the Five-Factor Model Rating Form (FFMRF) in its self-report version, two independent samples of Italian participants, which were composed of 510 adolescent high school students and 457 community-dwelling adults, respectively, were administered the FFMRF in its Italian translation. Adolescent participants were also administered the Italian translation of the Borderline Personality Features Scale for Children-11 (BPFSC-11), whereas adult participants were administered the Italian translation of the Triarchic Psychopathy Measure (TriPM). Cronbach α values were consistent with previous findings; in both samples, average interitem r values indicated acceptable internal consistency for all FFMRF scales. A multidimensional graded item response theory model indicated that the majority of FFMRF items had adequate discrimination parameters; information indices supported the reliability of the FFMRF scales. Both categorical (i.e., item-level) and scale-level regression analyses suggested that the FFMRF scores may predict a nonnegligible amount of variance in the BPFSC-11 total score in adolescent participants, and in the TriPM scale scores in adult participants.

Understanding logistic regression analysis

OpenAIRE

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

Nonparametric regression using the concept of minimum energy

International Nuclear Information System (INIS)

Williams, Mike

2011-01-01

It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.

Linear regression in astronomy. II

Science.gov (United States)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

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

A Matlab program for stepwise regression

Directory of Open Access Journals (Sweden)

Yanhong Qi

2016-03-01

Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.

Perceived fairness of the division of household labor: A comparative study in 29 countries

NARCIS (Netherlands)

Jansen, L.; Weber, T.; Kraaykamp, G.L.M.; Verbakel, C.M.C.

2016-01-01

This study investigates the relationship between the division of household labor and individuals' perceived fairness concerning this division. We applied multilevel multinomial logistic regression to analyze data on both men and women across 29 countries using the International Social Survey

Quantile regression theory and applications

CERN Document Server

Davino, Cristina; Vistocco, Domenico

2013-01-01

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

Fungible weights in logistic regression.

Science.gov (United States)

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Association between employee benefits and frailty in community-dwelling older adults.

Science.gov (United States)

Avila-Funes, José Alberto; Paniagua-Santos, Diana Leticia; Escobar-Rivera, Vicente; Navarrete-Reyes, Ana Patricia; Aguilar-Navarro, Sara; Amieva, Hélène

2016-05-01

The phenotype of frailty has been associated with an increased vulnerability for the development of adverse health-related outcomes. The origin of frailty is multifactorial and financial issues could be implicated, as they have been associated with health status, well-being and mortality. However, the association between economic benefits and frailty has been poorly explored. Therefore, the objective was to determine the association between employee benefits and frailty. A cross-sectional study of 927 community-dwelling older adults aged 70 years and older participating in the Mexican Study of Nutritional and Psychosocial Markers of Frailty was carried out. Employee benefits were established according to eight characteristics: bonus, profit sharing, pension, health insurance, food stamps, housing credit, life insurance, and Christmas bonus. Frailty was defined according to a slightly modified version of the phenotype proposed by Fried et al. Multinomial logistic regression models were run to determine the association between employee benefits and frailty adjusting by sociodemographic and health covariates. The prevalence of frailty was 14.1%, and 4.4% of participants rated their health status as "poor." Multinomial logistic regression analyses showed that employee benefits were statistically and independently associated with the frail subgroup (OR 0.85; 95% CI 0.74-0.98; P = 0.027) even after adjusting for potential confounders. Fewer employee benefits are associated with frailty. Supporting spreading employee benefits for older people could have a positive impact on the development of frailty and its consequences. Geriatr Gerontol Int 2016; 16: 606-611. © 2015 Japan Geriatrics Society.

Concern about falling in older women with a history of falls: associations with health, functional ability, physical activity and quality of life.

Science.gov (United States)

Patil, Radhika; Uusi-Rasi, Kirsti; Kannus, Pekka; Karinkanta, Saija; Sievänen, Harri

2014-01-01

Fear of falling has been linked to activity restriction, functional decline, decreased quality of life and increased risk of falling. Factors that distinguish persons with a high concern about falling from those with low concern have not been systematically studied. This study aimed to expose potential health-related, functional and psychosocial factors that correlate with fear of falling among independently living older women who had fallen in the past year. Baseline data of 409 women aged 70-80 years recruited to a randomised falls prevention trial (DEX) (NCT00986466) were used. Participants were classified according to their level of concern about falling using the Falls Efficacy Scale International (FES-I). Multinomial logistic regression analyses were performed to explore associations between health-related variables, functional performance tests, amount of physical activity, quality of life and FES-I scores. 68% of the participants reported a moderate to high concern (FES-I ≥ 20) about falls. Multinomial logistic regression showed that highly concerned women were significantly more likely to have poorer health and quality of life and lower functional ability. Reported difficulties in instrumental activities of daily living, balance, outdoor mobility and poorer quality of life contributed independently to a greater concern about falling. Concern about falling was highly prevalent in our sample of community-living older women. In particular, poor perceived general health and mobility constraints contributed independently to the difference between high and low concern of falling. Knowledge of these associations may help in developing interventions to reduce fear of falling and activity avoidance in old age.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

Science.gov (United States)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Predicting Teacher Value-Added Results in Non-Tested Subjects Based on Confounding Variables: A Multinomial Logistic Regression

Science.gov (United States)

Street, Nathan Lee

2017-01-01

Teacher value-added measures (VAM) are designed to provide information regarding teachers' causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous…

An introduction to using Bayesian linear regression with clinical data.

Science.gov (United States)

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Length bias correction in gene ontology enrichment analysis using logistic regression.

Science.gov (United States)

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

Directory of Open Access Journals (Sweden)

Hsin-Lun Wu

Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.

Logistic regression models

CERN Document Server

Hilbe, Joseph M

2009-01-01

This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

Institutions and deforestation in the Brazilian amazon: a geographic regression discontinuity analysis

OpenAIRE

Bogetvedt, Ingvild Engen; Hauge, Mari Johnsrud

2017-01-01

This study explores the impact of institutional quality at the municipal level on deforestation in the Legal Amazon. We add to this insufficiently understood topic by implementing a geographic regression discontinuity design. By taking advantage of high-resolution spatial data on deforestation combined with an objective measure of corruption used as a proxy for institutional quality, we analyse 138 Brazilian municipalities in the period of 2002-2004. Our empirical findings show...

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data.

Science.gov (United States)

Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A

2015-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

Interpret with caution: multicollinearity in multiple regression of cognitive data.

Science.gov (United States)

Morrison, Catriona M

2003-08-01

Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

Understanding logistic regression analysis.

Science.gov (United States)

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Minimax Regression Quantiles

DEFF Research Database (Denmark)

Bache, Stefan Holst

A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Evaluating the Performance of Polynomial Regression Method with Different Parameters during Color Characterization

Directory of Open Access Journals (Sweden)

Bangyong Sun

2014-01-01

Full Text Available The polynomial regression method is employed to calculate the relationship of device color space and CIE color space for color characterization, and the performance of different expressions with specific parameters is evaluated. Firstly, the polynomial equation for color conversion is established and the computation of polynomial coefficients is analysed. And then different forms of polynomial equations are used to calculate the RGB and CMYK’s CIE color values, while the corresponding color errors are compared. At last, an optimal polynomial expression is obtained by analysing several related parameters during color conversion, including polynomial numbers, the degree of polynomial terms, the selection of CIE visual spaces, and the linearization.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Caregiver Burdens and Preventive Dental Care for Children with Autism Spectrum disorder, developmental disability and/or mental health conditions: National Survey of CSHCN, 2009–10

Science.gov (United States)

Vohra, Rini; Sambamoorthi, Usha; Madhavan, S. Suresh

2016-01-01

Objective The purpose of this study is to examine the burdens of caregivers on one perception of the need and receipt of preventive dental care for a subset of children with special health care needs—children with Autism Spectrum disorder, developmental disability and/or mental health conditions (CASD/DD/MHC). Methods The authors used the 2009–2010 National Survey of CSHCN. The survey included questions addressing preventive dental care and caregivers’ financial, employment, and time-related burdens. The associations of these burdens on perceptions and receipt of preventive dental care use were analyzed with bivariate Chi square analyses and multinomial logistic regressions for CASD/DD/MHC (N=16,323). Results Overall, 16.3% of CASD/DD/MHC had an unmet preventive dental care need. There were 40.0% of caregivers who reported financial burden, 20.3% who reported employment burden, and 10.8% who reported time burden. A higher percentage of caregivers with financial burden, employment burden, and time-related burden reported that their CASD/DD/MHC did not receive needed preventive dental care (14.1 %, 16.5%, 17.7% respectively) compared to caregivers without financial, employment, or time burdens (9.0%, 9.6%, 11.0% respectively). Caregivers with financial burden (adjusted multinomial odds ratio, 1.38 [95%CI: 1.02, 1.86]) and employment burden (adjusted multinomial odds ratio, 1.45 [95%CI: 1.02, 2.06]) were more likely to report that their child did not receive preventive dental care despite perceived need compared to caregivers without financial or employment burdens. Conclusions for practice Unmet needs for preventive dental care were associated with employment and financial burdens of the caregivers of CASD/DD/MHC. PMID:27465058

The role of agri-business incentive on under-five child immunization ...

African Journals Online (AJOL)

A multinomial logistic regression model used to analyze the determinant of partial or noneimmunized. Maternal health practices and access to a motivating intervention are significant factors that ensure a parent/guardian's compliance to their child immunization. The study recommends sustainability and diversification of ...

The Role of Predictor Courses and Teams on Individual Student Success

Science.gov (United States)

Baker-Eveleth, Lori Jo; O'Neill, Michele; Sisodiya, Sanjay R.

2014-01-01

Research suggests that diverse environments enhance conscious modes of thought, resulting in greater intellectual engagement and active thinking. Ordinal and multinomial logistic regression results indicate that accounting courses and business law classes are useful predictors of subsequent performance. Odds ratio estimates indicate that students…

In-season and out-of-season variation of rotavirus genotype distribution and age of infection across 12 European countries before the introduction of routine vaccination, 2007/08 to 2012/13

DEFF Research Database (Denmark)

Hungerford, Daniel; Vivancos, Roberto; Read, Jonathan M

2016-01-01

distribution and age distribution of rotavirus gastroenteritis (RVGE) cases in and out of peak season in 12 countries which were yet to implement routine rotavirus vaccination. In multinomial multivariate logistic regression, adjusting for year, country and age, the odds of infection caused by genotype...

Primary and Secondary Socialization Impacts on Support for Same-Sex Marriage After Legalization in the Netherlands

NARCIS (Netherlands)

Lubbers, M.; Jaspers, E.; Ultee, W.C.

2009-01-01

Two years after the legalization of same-sex marriages in the Netherlands, 65% of the Dutch population largely or completely disagrees with the statement "gay marriage should be abolished." This article shows, by way of multinomial logistic regression analysis of survey data, which socializing

Predictors of Early Termination in a University Counseling Training Clinic

Science.gov (United States)

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

Evaluating the Locational Attributes of Education Management Organizations (EMOs)

Science.gov (United States)

Gulosino, Charisse; Miron, Gary

2017-01-01

This study uses logistic and multinomial logistic regression models to analyze neighborhood factors affecting EMO (Education Management Organization)-operated schools' locational attributes (using census tracts) in 41 states for the 2014-2015 school year. Our research combines market-based school reform, institutional theory, and resource…

Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

Science.gov (United States)

Pistoia, Jenny; Pinardi, Nadia; Oddo, Paolo; Collins, Matthew; Korres, Gerasimos; Drillet, Yann

2017-04-01

Short-term ocean analyses for Sea Surface Temperature SST in the Mediterranean Sea can be improved by a statistical post-processing technique, called super-ensemble. This technique consists in a multi-linear regression algorithm applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset, a collection of different operational forecasting analyses together with ad-hoc simulations produced by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is capable to prevent overfitting problems, even if best performances are achieved when we add correlation to the super-ensemble structure using a simple spatial filter applied after the linear regression. Our outcomes show that super-ensemble performances depend on the selection of an unbiased operator and the length of the learning period, but the quality of the generating MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. Lower RMSE analysis estimates result from the following choices: 15 days training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

Post-processing through linear regression

Science.gov (United States)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

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

Single-electron multiplication statistics as a combination of Poissonian pulse height distributions using constraint regression methods

International Nuclear Information System (INIS)

Ballini, J.-P.; Cazes, P.; Turpin, P.-Y.

1976-01-01

Analysing the histogram of anode pulse amplitudes allows a discussion of the hypothesis that has been proposed to account for the statistical processes of secondary multiplication in a photomultiplier. In an earlier work, good agreement was obtained between experimental and reconstructed spectra, assuming a first dynode distribution including two Poisson distributions of distinct mean values. This first approximation led to a search for a method which could give the weights of several Poisson distributions of distinct mean values. Three methods have been briefly exposed: classical linear regression, constraint regression (d'Esopo's method), and regression on variables subject to error. The use of these methods gives an approach of the frequency function which represents the dispersion of the punctual mean gain around the whole first dynode mean gain value. Comparison between this function and the one employed in Polya distribution allows the statement that the latter is inadequate to describe the statistical process of secondary multiplication. Numerous spectra obtained with two kinds of photomultiplier working under different physical conditions have been analysed. Then two points are discussed: - Does the frequency function represent the dynode structure and the interdynode collection process. - Is the model (the multiplication process of all dynodes but the first one, is Poissonian) valid whatever the photomultiplier and the utilization conditions. (Auth.)

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Semiparametric regression during 2003–2007

KAUST Repository

Ruppert, David; Wand, M.P.; Carroll, Raymond J.

2009-01-01

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

Science.gov (United States)

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

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

Linear regression

CERN Document Server

Olive, David J

2017-01-01

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

Regression modeling of ground-water flow

Science.gov (United States)

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

CERN Document Server

Faraway, Julian J

2005-01-01

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

Post-processing through linear regression

Directory of Open Access Journals (Sweden)

B. Van Schaeybroeck

2011-03-01

Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Gingival crevicular fluid alkaline phosphatase activity in relation to pubertal growth spurt and dental maturation: A multiple regression study

Directory of Open Access Journals (Sweden)

Perinetti, G.

2016-04-01

Full Text Available Introduction: The identification of the onset of the pubertal growth spurt has major clinical implications when dealing with orthodontic treatment in growing subjects. Aim: Through multivariate methods, this study evaluated possible relationships between the gingival crevicular fluid (GCF alkaline phosphatase (ALP activity and pubertal growth spurt and dentition phase. Materials and methods: One hundred healthy growing subjects (62 females, 38 males; mean age, 11.5±2.4 years were enrolled into this doubleblind, prospective, cross-sectional-design study. Phases of skeletal maturation (pre - pubertal, pubertal, post - pubertal was assessed using the cervical vertebral maturation method. Samples of GCF for the ALP activity determination were collected at the mesial and distal sites of the mandibular central incisors. The phases of the dentition were recorded as intermediate mixed, late mixed, or permanent. A multinomial multiple logistic regression model was used to assess relationships of the enzymatic activity to growth phases and dentition phases. Results: The GCF ALP activity was greater in the pubertal growth phase as compared to the pre - pubertal and post - pubertal growth phases. Significant adjusted odds ratios for the GCF ALP activity for the pre - pubertal and post - pubertal subjects, in relation to the pubertal group, were 0.76 and 0.84, respectively. No significant correlations were seen for the dentition phase. Conclusions: The GCF ALP activity is a valid candidate as a non - invasive biomarker for the identification of the pubertal growth spurt irrespective of the dentition phase.

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

Logistic regression applied to natural hazards: rare event logistic regression with replications

OpenAIRE

Guns, M.; Vanacker, Veerle

2012-01-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

Factors influencing the planning of social activities : empirical analysis of social interaction diary data

NARCIS (Netherlands)

Berg, van den P.E.W.; Arentze, T.A.; Timmermans, H.J.P.

2010-01-01

Results of a study on the planning of social activities are reported. Data collected in the Netherlands from social interaction diaries were used to estimate a multinomial logistic regression model to analyze whether a social activity is prearranged, routine, or spontaneous as a function of personal

Primary and secondary socialization impacts on support for same-sex marriage after legalization in the Netherlands.

NARCIS (Netherlands)

Lubbers, M; Jaspers, E.; Ultee, W.C.

2009-01-01

Two years after the legalization of same-sex marriages in the Netherlands, 65% of the Dutch population largely or completely disagrees with the statement “gay marriage should be abolished.” This article shows, by way of multinomial logistic regression analysis of survey data, which socializing

Relating cost-benefit analysis results with transport project decisions in the Netherlands

NARCIS (Netherlands)

Annema, Jan Anne; Frenken, Koen|info:eu-repo/dai/nl/207145253; Koopmans, Carl; Kroesen, Maarten

2017-01-01

This paper relates the cost-benefit analysis (CBA) results of transportation policy proposals in the Netherlands with the decision to implement or abandon the proposal. The aim of this study is to explore the relation between the CBA results and decision-making. Multinomial logit regression models

Relating cost-benefit analysis results with transport project decisions in the Netherlands

NARCIS (Netherlands)

Annema, J.A.; Frenken, Koen; Koopmans, Carl; Kroesen, M.

2017-01-01

This paper relates the cost-benefit analysis (CBA) results of transportation policy proposals in the Netherlands with the decision to implement or abandon the proposal. The aim of this study is to explore the relation between the CBA results and decision-making. Multinomial logit regression

Household Income during Childhood and Young Adult Weight Status: Evidence from a Nutrition Transition Setting

Science.gov (United States)

Schmeer, Kammi K.

2010-01-01

This article explores whether household income at different stages of childhood is associated with weight status in early adulthood in a nutrition transition setting (a developing country with both underweight and overweight populations). I use multinomial logistic regression to analyze prospective, longitudinal data from Cebu, Philippines.…

Predicting College Success: Achievement, Demographic, and Psychosocial Predictors of First-Semester College Grade Point Average

Science.gov (United States)

Saltonstall, Margot

2013-01-01

This study seeks to advance and expand research on college student success. Using multinomial logistic regression analysis, the study investigates the contribution of psychosocial variables above and beyond traditional achievement and demographic measures to predicting first-semester college grade point average (GPA). It also investigates if…

Barriers to participation in vocational orientation programmes among prisoners

Directory of Open Access Journals (Sweden)

Dorien Brosens

2015-11-01

Full Text Available This study investigates the barriers to prisoners’ participation in vocational education, as well as the predictors of different types of barriers. Survey data derived from a project in a remand prison in Belgium (N=468 provided the empirical evidence for the analyses. The results indicate that facing situational and informational barriers are most common. Based on the different kinds of barriers, various types of non-participants can be distinguished and multinomial logistic regression analyses are conducted to identify in what way participants of vocational education differ from various types of non-participants. For instance, prisoners with a poor understanding of the Dutch language and those who never/rarely receive visitors participate less in vocational education as they are more likely to be confronted with informational barriers. We conclude this article by discussing paths for future research and implications for policy and practice to anticipate the barriers for those who want to participate in vocational education.

The Context of Body Art

Directory of Open Access Journals (Sweden)

Renata Clerici

2011-10-01

Full Text Available The present study analyses factors associated with the propensity for piercing and tattooing among adolescents, from a survey conducted in 2007 in high schools in the Veneto Region (Northeast Italy. After some descriptive analyses, multinomial logistic regression models were applied to identify what predicts the practice of, interest in, or indifference to piercing and tattooing. Results show gender differences in the approach to body modification: For girls, body art is considered a mainstream activity involving younger cohorts; piercing and tattooing become a way of increasing their own body satisfaction. For boys, factors connected with the propensity for these practices are a low cultural background and the importance given to physical appearance. For both boys and girls, a significant aspect in defining attitudes toward body art practices is the values system: Values such as enjoyment, beauty, and youth, are found to be closely connected with a high propensity for these practices.

Multilevel covariance regression with correlated random effects in the mean and variance structure.

Science.gov (United States)

Quintero, Adrian; Lesaffre, Emmanuel

2017-09-01

Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

International Nuclear Information System (INIS)

Hooman, A.; Mohammadzadeh, M.

2008-01-01

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curve (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions

A Seemingly Unrelated Poisson Regression Model

OpenAIRE

King, Gary

1989-01-01

This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

Recursive Algorithm For Linear Regression

Science.gov (United States)

Varanasi, S. V.

1988-01-01

Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

Standards for Standardized Logistic Regression Coefficients

Science.gov (United States)

Menard, Scott

2011-01-01

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

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

Science.gov (United States)

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Logistic regression for dichotomized counts.

Science.gov (United States)

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Quantifying Shapes: Mathematical Techniques for Analysing Visual Representations of Sound and Music

Directory of Open Access Journals (Sweden)

Genevieve L. Noyce

2013-12-01

Full Text Available Research on auditory-visual correspondences has a long tradition but innovative experimental paradigms and analytic tools are sparse. In this study, we explore different ways of analysing real-time visual representations of sound and music drawn by both musically-trained and untrained individuals. To that end, participants' drawing responses captured by an electronic graphics tablet were analysed using various regression, clustering, and classification techniques. Results revealed that a Gaussian process (GP regression model with a linear plus squared-exponential covariance function was able to model the data sufficiently, whereas a simpler GP was not a good fit. Spectral clustering analysis was the best of a variety of clustering techniques, though no strong groupings are apparent in these data. This was confirmed by variational Bayes analysis, which only fitted one Gaussian over the dataset. Slight trends in the optimised hyperparameters between musically-trained and untrained individuals allowed for the building of a successful GP classifier that differentiated between these two groups. In conclusion, this set of techniques provides useful mathematical tools for analysing real-time visualisations of sound and can be applied to similar datasets as well.

Bayesian ARTMAP for regression.

Science.gov (United States)

Sasu, L M; Andonie, R

2013-10-01

Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mechanisms of neuroblastoma regression

Science.gov (United States)

Brodeur, Garrett M.; Bagatell, Rochelle

2014-01-01

Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

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

Science.gov (United States)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

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

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Exploring reasons for the observed inconsistent trial reports on intra-articular injections with hyaluronic acid in the treatment of osteoarthritis: Meta-regression analyses of randomized trials.

Science.gov (United States)

Johansen, Mette; Bahrt, Henriette; Altman, Roy D; Bartels, Else M; Juhl, Carsten B; Bliddal, Henning; Lund, Hans; Christensen, Robin

2016-08-01

The aim was to identify factors explaining inconsistent observations concerning the efficacy of intra-articular hyaluronic acid compared to intra-articular sham/control, or non-intervention control, in patients with symptomatic osteoarthritis, based on randomized clinical trials (RCTs). A systematic review and meta-regression analyses of available randomized trials were conducted. The outcome, pain, was assessed according to a pre-specified hierarchy of potentially available outcomes. Hedges׳s standardized mean difference [SMD (95% CI)] served as effect size. REstricted Maximum Likelihood (REML) mixed-effects models were used to combine study results, and heterogeneity was calculated and interpreted as Tau-squared and I-squared, respectively. Overall, 99 studies (14,804 patients) met the inclusion criteria: Of these, only 71 studies (72%), including 85 comparisons (11,216 patients), had adequate data available for inclusion in the primary meta-analysis. Overall, compared with placebo, intra-articular hyaluronic acid reduced pain with an effect size of -0.39 [-0.47 to -0.31; P hyaluronic acid. Based on available trial data, intra-articular hyaluronic acid showed a better effect than intra-articular saline on pain reduction in osteoarthritis. Publication bias and the risk of selective outcome reporting suggest only small clinical effect compared to saline. Copyright © 2016 Elsevier Inc. All rights reserved.

Panel Smooth Transition Regression Models

DEFF Research Database (Denmark)

González, Andrés; Terasvirta, Timo; Dijk, Dick van

We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

Farmers' choice of cattle marketing channels under transaction cost ...

African Journals Online (AJOL)

The theoretical predictions of transaction cost economics were tested based on primary data collected from 230 cattle farm households in 13 communities of the Okhahlamba Local Municipality. The results of a multinomial logit regression revealed some unique insights. They showed that the probability of selling at auction ...

Primary and Secondary Socialization Impacts on Support for Same-Sex Marriage after Legalization in the Netherlands

Science.gov (United States)

Lubbers, Marcel; Jaspers, Eva; Ultee, Wout

2009-01-01

Two years after the legalization of same-sex marriages in the Netherlands, 65% of the Dutch population largely or completely disagrees with the statement "gay marriage should be abolished." This article shows, by way of multinomial logistic regression analysis of survey data, which socializing agents influence one's attitude toward…

Support vector machines classifiers of physical activities in preschoolers

Science.gov (United States)

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

The Association between Electronic Bullying and School Absenteeism among High School Students in the United States

Science.gov (United States)

Grinshteyn, Erin; Yang, Y. T.

2017-01-01

Background: We examined the relationship between exposure to electronic bullying and absenteeism as a result of being afraid. Methods: This multivariate, multinomial regression analysis of the 2013 Youth Risk Behavior Survey data assessed the association between experiencing electronic bullying in the past year and how often students were absent…

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

Science.gov (United States)

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

[From clinical judgment to linear regression model.

Science.gov (United States)

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

2013-01-01

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

Refining cost-effectiveness analyses using the net benefit approach and econometric methods: an example from a trial of anti-depressant treatment.

Science.gov (United States)

Sabes-Figuera, Ramon; McCrone, Paul; Kendricks, Antony

2013-04-01

Economic evaluation analyses can be enhanced by employing regression methods, allowing for the identification of important sub-groups and to adjust for imperfect randomisation in clinical trials or to analyse non-randomised data. To explore the benefits of combining regression techniques and the standard Bayesian approach to refine cost-effectiveness analyses using data from randomised clinical trials. Data from a randomised trial of anti-depressant treatment were analysed and a regression model was used to explore the factors that have an impact on the net benefit (NB) statistic with the aim of using these findings to adjust the cost-effectiveness acceptability curves. Exploratory sub-samples' analyses were carried out to explore possible differences in cost-effectiveness. Results The analysis found that having suffered a previous similar depression is strongly correlated with a lower NB, independent of the outcome measure or follow-up point. In patients with previous similar depression, adding an selective serotonin reuptake inhibitors (SSRI) to supportive care for mild-to-moderate depression is probably cost-effective at the level used by the English National Institute for Health and Clinical Excellence to make recommendations. This analysis highlights the need for incorporation of econometric methods into cost-effectiveness analyses using the NB approach.

Autistic Regression

Science.gov (United States)

Matson, Johnny L.; Kozlowski, Alison M.

2010-01-01

Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…

Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

Directory of Open Access Journals (Sweden)

Sharad Damodar Gore

2009-10-01

Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

Discriminative Elastic-Net Regularized Linear Regression.

Science.gov (United States)

Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

2017-03-01

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

Suicidal ideation and attempts in adolescents: associations with depression and six domains of self-esteem.

Science.gov (United States)

Wild, Lauren G; Flisher, Alan J; Lombard, Carl

2004-12-01

This study aimed to disentangle the influence of depression and self-esteem on suicidal behaviour in adolescence. Grades 8 and 11 students in Cape Town, South Africa (n = 939) completed questionnaires assessing suicidal ideation and behaviour, depression, and self-esteem with respect to family, peers, school, sports/athletics, body image and global self-worth. Data were analysed using a series of multinomial logistic regression models adjusted for gender, grade, race and the sampling strategy. Results indicated that depression and low self-esteem in the family context were independently associated with suicide ideation and attempts. Moreover, low family self-esteem significantly differentiated suicide attempters from ideators. Screening for depression and low self-esteem in the family context is discussed as a possible strategy for helping to identify adolescents at risk for suicide attempts.

Environmental Sound Perception: Metadescription and Modeling Based on Independent Primary Studies

Directory of Open Access Journals (Sweden)

Stephen McAdams

2010-01-01

Full Text Available The aim of the study is to transpose and extend to a set of environmental sounds the notion of sound descriptors usually used for musical sounds. Four separate primary studies dealing with interior car sounds, air-conditioning units, car horns, and closing car doors are considered collectively. The corpus formed by these initial stimuli is submitted to new experimental studies and analyses, both for revealing metacategories and for defining more precisely the limits of each of the resulting categories. In a second step, the new structure is modeled: common and specific dimensions within each category are derived from the initial results and new investigations of audio features are performed. Furthermore, an automatic classifier based on two audio descriptors and a multinomial logistic regression procedure is implemented and validated with the corpus.

Psychosocial job strain and risk of adverse birth outcomes

DEFF Research Database (Denmark)

Larsen, Ann Dyreborg; Hannerz, Harald; Juhl, Mette

2013-01-01

OBJECTIVE: A number of studies examined the effects of prenatal stress on birth outcomes with diverging and inconclusive results. We aimed to examine if working with high job strain during pregnancy measured in week 16 was associated with risk of giving birth to a child born preterm or small....../large for gestational age (SGA/LGA), and second, if social support affected any associations. DESIGN: Study population was 48 890 pregnancies from the Danish National Birth Cohort. Multinomial logistic regression estimated ORs. Covariates included: maternal age, BMI, parity, exercise, smoking, alcohol and coffee...... consumption, manual work, serious maternal disease, parental height and gestational age at interview. In accordance with Good Epidemiological Practice, a protocol outlined the study design before analyses were initiated. RESULTS: High job strain was associated with significantly lower odds of being born LGA...

Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

Science.gov (United States)

Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies

Science.gov (United States)

Lhee, Min Jin; Cha, Youn Jin; Bae, Jong Man; Kim, Young Tae

2014-01-01

Purpose Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1. Materials and Methods We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number. Results An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis. Conclusion We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis". PMID:24532500

Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression

DEFF Research Database (Denmark)

Larsen, Klaus; Merlo, Juan

2005-01-01

The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However......, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure...... of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic...

Categorical regression dose-response modeling

Science.gov (United States)

The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

Science.gov (United States)

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Application of Robust Regression and Bootstrap in Poductivity Analysis of GERD Variable in EU27

Directory of Open Access Journals (Sweden)

Dagmar Blatná

2014-06-01

Full Text Available The GERD is one of Europe 2020 headline indicators being tracked within the Europe 2020 strategy. The headline indicator is the 3% target for the GERD to be reached within the EU by 2020. Eurostat defi nes “GERD” as total gross domestic expenditure on research and experimental development in a percentage of GDP. GERD depends on numerous factors of a general economic background, namely of employment, innovation and research, science and technology. The values of these indicators vary among the European countries, and consequently the occurrence of outliers can be anticipated in corresponding analyses. In such a case, a classical statistical approach – the least squares method – can be highly unreliable, the robust regression methods representing an acceptable and useful tool. The aim of the present paper is to demonstrate the advantages of robust regression and applicability of the bootstrap approach in regression based on both classical and robust methods.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

Science.gov (United States)

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Abstract Expression Grammar Symbolic Regression

Science.gov (United States)

Korns, Michael F.

This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.

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

Science.gov (United States)

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

2018-01-01

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

Pathological assessment of liver fibrosis regression

Directory of Open Access Journals (Sweden)

WANG Bingqiong

2017-03-01

Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.

American Youths' Access to Substance Abuse Treatment: Does Type of Treatment Facility Matter?

Science.gov (United States)

Lo, Celia C.; Cheng, Tyrone C.

2013-01-01

Using data from the 2007 National Survey on Drug Use and Health, this study examines whether several social exclusion and psychological factors affect adolescents' receipt of substance abuse treatment. Multinomial logistic regression techniques were used to analyze data. The study asked how the specified factors provide pathways to receipt of…

Caste, Class, and Urbanization: The Shaping of Religious Community in Contemporary India

Science.gov (United States)

Stroope, Samuel

2012-01-01

Building on the implications of qualitative work from India and urbanism theories, I aim to understand whether religious bonding social capital in contemporary India increases with greater urbanization and whether such increases are moderated by caste or social class position. Results from multinomial logistic regression on 1,417 Hindu respondents…

Logistic Regression: Concept and Application

Science.gov (United States)

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Marital status integration and suicide: A meta-analysis and meta-regression.

Science.gov (United States)

Kyung-Sook, Woo; SangSoo, Shin; Sangjin, Shin; Young-Jeon, Shin

2018-01-01

Marital status is an index of the phenomenon of social integration within social structures and has long been identified as an important predictor suicide. However, previous meta-analyses have focused only on a particular marital status, or not sufficiently explored moderators. A meta-analysis of observational studies was conducted to explore the relationships between marital status and suicide and to understand the important moderating factors in this association. Electronic databases were searched to identify studies conducted between January 1, 2000 and June 30, 2016. We performed a meta-analysis, subgroup analysis, and meta-regression of 170 suicide risk estimates from 36 publications. Using random effects model with adjustment for covariates, the study found that the suicide risk for non-married versus married was OR = 1.92 (95% CI: 1.75-2.12). The suicide risk was higher for non-married individuals aged analysis by gender, non-married men exhibited a greater risk of suicide than their married counterparts in all sub-analyses, but women aged 65 years or older showed no significant association between marital status and suicide. The suicide risk in divorced individuals was higher than for non-married individuals in both men and women. The meta-regression showed that gender, age, and sample size affected between-study variation. The results of the study indicated that non-married individuals have an aggregate higher suicide risk than married ones. In addition, gender and age were confirmed as important moderating factors in the relationship between marital status and suicide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

Science.gov (United States)

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha

2014-12-08

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha; Huang, Jianhua Z.

2014-01-01

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

Science.gov (United States)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

Vavrin, J.

1989-01-01

The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

A Multinomial Logit Approach to Estimating Regional Inventories by Product Class

Science.gov (United States)

Lawrence Teeter; Xiaoping Zhou

1998-01-01

Current timber inventory projections generally lack information on inventory by product classes. Most models available for inventory projection and linked to supply analyses are limited to projecting aggregate softwood and hardwood. The objective of this research is to develop a methodology to distribute the volume on each FIA survey plot to product classes and...

Determination of benzo(apyrene content in PM10 using regression methods

Directory of Open Access Journals (Sweden)

Jacek Gębicki

2015-12-01

Full Text Available The paper presents an attempt of application of multidimensional linear regression to estimation of an empirical model describing the factors influencing on B(aP content in suspended dust PM10 in Olsztyn and Elbląg city regions between 2010 and 2013. During this period annual average concentration of B(aP in PM10 exceeded the admissible level 1.5-3 times. Conducted investigations confirm that the reasons of B(aP concentration increase are low-efficiency individual home heat stations or low-temperature heat sources, which are responsible for so-called low emission during heating period. Dependences between the following quantities were analysed: concentration of PM10 dust in air, air temperature, wind velocity, air humidity. A measure of model fitting to actual B(aP concentration in PM10 was the coefficient of determination of the model. Application of multidimensional linear regression yielded the equations characterized by high values of the coefficient of determination of the model, especially during heating season. This parameter ranged from 0.54 to 0.80 during the analyzed period.

Trends in popularity of some morphological traits of purebred dogs in Australia.

Science.gov (United States)

Teng, Kendy T; McGreevy, Paul D; Toribio, Jenny-Ann L M L; Dhand, Navneet K

2016-01-01

The morphology of dogs can provide information about their predisposition to some disorders. For example, larger breeds are predisposed to hip dysplasia and many neoplastic diseases. Therefore, longitudinal trends in popularity of dog morphology can reveal potential disease pervasiveness in the future. There have been reports on the popularity of particular breeds and behavioural traits but trends in the morphological traits of preferred breeds have not been studied. This study investigated trends in the height, dog size and head shape (cephalic index) of Australian purebred dogs. One hundred eighty-one breeds derived from Australian National Kennel Council (ANKC) registration statistics from 1986 to 2013 were analysed. Weighted regression analyses were conducted to examine trends in the traits by using them as outcome variables, with year as the explanatory variable and numbers of registered dogs as weights. Linear regression investigated dog height and cephalic index (skull width/skull length), and multinomial logistic regression studied dog size. The total number of ANKC registration had decreased gradually from 95,792 in 1986 to 66,902 in 2013. Both weighted minimal height (p = 0.014) and weighted maximal height (p popular over time. Mean cephalic index has increased, which indicates that Australians have gradually favoured breeds with shorter and wider heads (brachycephalic). These significant trends indicate that the dog morphological traits reported here may potentially influence how people select companion dogs in Australia and provide valuable predictive information on the pervasiveness of diseases in dogs.

Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

2012-01-01

Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin

2017-01-19

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

2017-01-01

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

Science.gov (United States)

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Determinants of Agricultural Diversification in a Hotspot Area: Evidence from Colonist and Indigenous Communities in the Sumaco Biosphere Reserve, Ecuadorian Amazon

Directory of Open Access Journals (Sweden)

Bolier Torres

2018-05-01

Full Text Available With data from a household survey covering migrant settlers and indigenous (Kichwa communities in the Sumaco Biosphere Reserve (SBR, this study analyses the drivers of agricultural diversification/specialisation, focusing on the role of ethnicity and the livelihood strategies (LS they follow. Data were collected using the Poverty and Environment Network methodology of the Center for International Forestry Research (CIFOR-PEN. In order to establish the drivers of agricultural diversification, the number of crops and the Shannon index of crops areas were used as the dependent variables in ordinary least square (OLS models, while a multinomial logit model (MLM was used to assess a household’s degree of diversification. The results of the OLS regression provides evidence supporting the notion that households, with Livestock-based and Wage-based livelihood strategies (LS are less diversified and more specialized than households with Crop-based LS. Ethnicity has a positive and significant effect on agricultural diversification, with Kichwa farms more diversified than those of their migrant colonist counterparts. The results of the multinomial logit model (MLM show that large Kichwa households, with Crop-based and Forest-based LS are more likely to adopt a highly diversified agricultural strategy. Based on these findings, we recommend a redirection of agricultural incentives, towards the adoption of diversified agricultural systems, as a strategy to promote more sustainable production systems in the Ecuadorian Amazon Region.

Public funding of pharmaceuticals in The Netherlands: investigating the effect of evidence, process and context on CVZ decision-making.

Science.gov (United States)

Cerri, Karin H; Knapp, Martin; Fernandez, Jose-Luis

2014-09-01

The College Voor Zorgverzekeringen (CVZ) provides guidance to the Dutch healthcare system on funding and use of new pharmaceutical technologies. This study examined the impact of evidence, process and context factors on CVZ decisions in 2004-2009. A data set of CVZ decisions pertaining to pharmaceutical technologies was created, including 29 variables extracted from published information. A three-category outcome variable was used, defined as the decision to 'recommend', 'restrict' or 'not recommend' a technology. Technologies included in list 1A/1B or on the expensive drug list were considered recommended; those included in list 2 or for which patient co-payment is required were considered restricted; technologies not included on any reimbursement list were classified as 'not recommended'. Using multinomial logistic regression, the relative contribution of explanatory variables on CVZ decisions was assessed. In all, 244 technology appraisals (256 technologies) were analysed, with 51%, of technologies recommended, 33% restricted and 16% not recommended by CVZ for funding. The multinomial model showed significant associations (p ≤ 0.10) between CVZ outcome and several variables, including: (1) use of an active comparator and demonstration of statistical superiority of the primary endpoint in clinical trials, (2) pharmaceutical budget impact associated with introduction of the technology, (3) therapeutic indication and (4) prevalence of the target population. Results confirm the value of a comprehensive and multivariate approach to understanding CVZ decision-making.

On Solving Lq-Penalized Regressions

Directory of Open Access Journals (Sweden)

Tracy Zhou Wu

2007-01-01

Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

Science.gov (United States)

Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

2008-02-18

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

Testing Heteroscedasticity in Robust Regression

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions

Energy Technology Data Exchange (ETDEWEB)

Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.

2016-07-01

Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)

Spontaneous regression of a congenital melanocytic nevus

Directory of Open Access Journals (Sweden)

Amiya Kumar Nath

2011-01-01

Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.

An Analysis of Losses to the Southern Commercial Timberland Base

Science.gov (United States)

Ian A. Munn; David Cleaves

1998-01-01

Demographic and physical factors influencing the conversion of commercial timberland iu the south to non-forestry uses between the last two Forest Inventory Analysis (FIA) surveys were investigated. GIS techniques linked Census data and FIA plot level data. Multinomial logit regression identified factors associated with losses to the timberland base. Conversion to...

Browse Title Index

African Journals Online (AJOL)

Items 151 - 200 of 399 ... Vol 3, No 3 (2014), Effects of Variety and Fermentation Time on the Quality of Rice Wine, Abstract PDF .... Vol 4, No 4 (2015), Factors Associated with Attitudes of Men towards Gender and Intimate Partner Violence Against Women in Eastern Ethiopia: A Multinomial Logistic Regression Analysis ...

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

Is past life regression therapy ethical?

Science.gov (United States)

Andrade, Gabriel

2017-01-01

Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.

Regression Models for Market-Shares

DEFF Research Database (Denmark)

Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue

2005-01-01

On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....

The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration with an Application to Annual Mean Temperature and Sea Level

DEFF Research Database (Denmark)

Johansen, Søren

There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference. Finally we analyse some data on annual mean temperature...... and sea level, by applying the cointegrated vector autoregressive model, which explicitly takes into account the nonstationarity of the variables....

Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

Science.gov (United States)

Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

2017-01-01

Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Recursive and non-linear logistic regression: moving on from the original EuroSCORE and EuroSCORE II methodologies.

Science.gov (United States)

Poullis, Michael

2014-11-01

EuroSCORE II, despite improving on the original EuroSCORE system, has not solved all the calibration and predictability issues. Recursive, non-linear and mixed recursive and non-linear regression analysis were assessed with regard to sensitivity, specificity and predictability of the original EuroSCORE and EuroSCORE II systems. The original logistic EuroSCORE, EuroSCORE II and recursive, non-linear and mixed recursive and non-linear regression analyses of these risk models were assessed via receiver operator characteristic curves (ROC) and Hosmer-Lemeshow statistic analysis with regard to the accuracy of predicting in-hospital mortality. Analysis was performed for isolated coronary artery bypass grafts (CABGs) (n = 2913), aortic valve replacement (AVR) (n = 814), mitral valve surgery (n = 340), combined AVR and CABG (n = 517), aortic (n = 350), miscellaneous cases (n = 642), and combinations of the above cases (n = 5576). The original EuroSCORE had an ROC below 0.7 for isolated AVR and combined AVR and CABG. None of the methods described increased the ROC above 0.7. The EuroSCORE II risk model had an ROC below 0.7 for isolated AVR only. Recursive regression, non-linear regression, and mixed recursive and non-linear regression all increased the ROC above 0.7 for isolated AVR. The original EuroSCORE had a Hosmer-Lemeshow statistic that was above 0.05 for all patients and the subgroups analysed. All of the techniques markedly increased the Hosmer-Lemeshow statistic. The EuroSCORE II risk model had a Hosmer-Lemeshow statistic that was significant for all patients (P linear regression failed to improve on the original Hosmer-Lemeshow statistic. The mixed recursive and non-linear regression using the EuroSCORE II risk model was the only model that produced an ROC of 0.7 or above for all patients and procedures and had a Hosmer-Lemeshow statistic that was highly non-significant. The original EuroSCORE and the EuroSCORE II risk models do not have adequate ROC and Hosmer

SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

Science.gov (United States)

Chu, Annie; Cui, Jenny; Dinov, Ivo D

2009-03-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most

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

Science.gov (United States)

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

2014-02-01

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

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

Science.gov (United States)

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Modelling the risk of Pb and PAH intervention value exceedance in allotment soils by robust logistic regression

International Nuclear Information System (INIS)

Papritz, A.; Reichard, P.U.

2009-01-01

Soils of allotments are often contaminated by heavy metals and persistent organic pollutants. In particular, lead (Pb) and polycyclic aromatic hydrocarbons (PAHs) frequently exceed legal intervention values (IVs). Allotments are popular in European countries; cities may own and let several thousand allotment plots. Assessing soil contamination for all the plots would be very costly. Soil contamination in allotments is often linked to gardening practice and historic land use. Hence, we predict the risk of IV exceedance from attributes that characterize the history and management of allotment areas (age, nearby presence of pollutant sources, prior land use). Robust logistic regression analyses of data of Swiss allotments demonstrate that the risk of IV exceedance can be predicted quite precisely without costly soil analyses. Thus, the new method allows screening many allotments at small costs, and it helps to deploy the resources available for soil contamination surveying more efficiently. - The contamination of allotment soils, expressed as frequency of intervention value exceedance, depends on the age and further attributes of the allotments and can be predicted by logistic regression.

Brief Report: Sexual Orientation in Individuals with Autistic Traits--Population Based Study of 47,000 Adults in Stockholm County

Science.gov (United States)

Rudolph, Christiane E. S.; Lundin, Andreas; Åhs, Jill W.; Dalman, Christina; Kosidou, Kyriaki

2018-01-01

We examined the association between autistic traits and sexual orientation in a general adult population (N = 47,356). Autistic traits were measured with the ten items Autistic Quotient questionnaire using a cut-off score of = 6. Sexual orientation was assessed by self-report. Multinomial logistic regression was used to estimate odds ratios (ORs)…

Bullying and PTSD Symptoms

Science.gov (United States)

Idsoe, Thormod; Dyregrov, Atle; Idsoe, Ella Cosmovici

2012-01-01

PTSD symptoms related to school bullying have rarely been investigated, and never in national samples. We used data from a national survey to investigate this among students from grades 8 and 9 (n = 963). The prevalence estimates of exposure to bullying were within the range of earlier research findings. Multinomial logistic regression showed that…

An Exploration of Teacher Attrition and Mobility in High Poverty Racially Segregated Schools

Science.gov (United States)

Djonko-Moore, Cara M.

2016-01-01

The purpose of this study was to examine the mobility (movement to a new school) and attrition (quitting teaching) patterns of teachers in high poverty, racially segregated (HPRS) schools in the US. Using 2007-9 survey data from the National Center for Education Statistics, a multi-level multinomial logistic regression was performed to examine the…

A Multilevel Study of Students' Motivations of Studying Accounting: Implications for Employers

Science.gov (United States)

Law, Philip; Yuen, Desmond

2012-01-01

Purpose: The purpose of this study is to examine the influence of factors affecting students' choice of accounting as a study major in Hong Kong. Design/methodology/approach: Multinomial logistic regression and Hierarchical Generalized Linear Modeling (HGLM) are used to analyze the survey data for the level one and level two data, which is the…

So Close, yet So Far Away: Early vs. Late Dropouts

Science.gov (United States)

Ma, Yanli; Cragg, Kristina M.

2013-01-01

While some students drop out early in their academic career, others drop out close to completion. What similarities and differences exist between these early and late dropouts? Using a sample 3,520 first-time, full-time (FTFT) students seeking a bachelor's degree at a state university, this study employs multinomial logistic regression to model…

Regression Analysis

CERN Document Server

Freund, Rudolf J; Sa, Ping

2006-01-01

The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Hydrologic and Hydraulic Analyses of Selected Streams in Lorain County, Ohio, 2003

Science.gov (United States)

Jackson, K. Scott; Ostheimer, Chad J.; Whitehead, Matthew T.

2003-01-01

Hydrologic and hydraulic analyses were done for selected reaches of nine streams in Lorain County Ohio. To assess the alternatives for flood-damage mitigation, the Lorain County Engineer and the U.S. Geological Survey (USGS) initiated a cooperative study to investigate aspects of the hydrology and hydraulics of the nine streams. Historical streamflow data and regional regression equations were used to estimate instantaneous peak discharges for floods having recurrence intervals of 2, 5, 10, 25, 50, and 100 years. Explanatory variables used in the regression equations were drainage area, main-channel slope, and storage area. Drainage areas of the nine stream reaches studied ranged from 1.80 to 19.3 square miles. The step-backwater model HEC-RAS was used to determine water-surface-elevation profiles for the 10-year-recurrence-interval (10-year) flood along a selected reach of each stream. The water-surface pro-file information was used then to generate digital mapping of flood-plain boundaries. The analyses indicate that at the 10-year flood elevation, road overflow results at numerous hydraulic structures along the nine streams.

Polygenic scores via penalized regression on summary statistics.

Science.gov (United States)

Mak, Timothy Shin Heng; Porsch, Robert Milan; Choi, Shing Wan; Zhou, Xueya; Sham, Pak Chung

2017-09-01

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred. © 2017 WILEY PERIODICALS, INC.

Regression analysis using dependent Polya trees.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

A hydrologic regression sediment-yield model for two ungaged watershed outlet stations in Africa

International Nuclear Information System (INIS)

Moussa, O.M.; Smith, S.E.; Shrestha, R.L.

1991-01-01

A hydrologic regression sediment-yield model was established to determine the relationship between water discharge and suspended sediment discharge at the Blue Nile and the Atbara River outlet stations during the flood season. The model consisted of two main submodels: (1) a suspended sediment discharge model, which was used to determine suspended sediment discharge for each basin outlet; and (2) a sediment rating model, which related water discharge and suspended sediment discharge for each outlet station. Due to the absence of suspended sediment concentration measurements at or near the outlet stations, a minimum norm solution, which is based on the minimization of the unknowns rather than the residuals, was used to determine the suspended sediment discharges at the stations. In addition, the sediment rating submodel was regressed by using an observation equations procedure. Verification analyses on the model were carried out and the mean percentage errors were found to be +12.59 and -12.39, respectively, for the Blue Nile and Atbara. The hydrologic regression model was found to be most sensitive to the relative weight matrix, moderately sensitive to the mean water discharge ratio, and slightly sensitive to the concentration variation along the River Nile's course

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

Science.gov (United States)

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

Impact of leukoaraiosis on parenchymal hemorrhage in elderly patients treated with thrombolysis

Energy Technology Data Exchange (ETDEWEB)

Nighoghossian, Norbert; Cho, Tae-Hee; Cottaz, Vincent; Mechtouff, Laura; Derex, Laurent [Universite Lyon 1, Department of Stroke, Neurological Hospital, Lyon (France); Abbas, Fatima; Schott, Anne Marie [Hospices Civils de Lyon, Pole Information Medicale Evaluation Recherche, Lyon (France); Geraldo, Ana Filipa; Janecek, Elie; Hermier, Marc; Tisserand, Louis Guy; Amelie, Roxana; Chamard, Leila; Berthezene, Yves [Universite Lyon 1, Department of Neuroradiology, Neurological Hospital, Bron, Lyon (France); Bischoff, Magali; El Khoury, Carlos [RESUVAL Stroke Network, Lyon (France)

2016-10-15

Severity of vascular damage of white matter may predict hemorrhagic transformation (HT). We assess the relationship between leukoaraiosis (LA) severity and the type of hemorrhagic transformation in elderly patients treated with thrombolysis. We retrospectively analyzed the clinical data and pretreatment magnetic resonance imaging (MRI) of 180 consecutive ischemic stroke patients aged over 75 years. LA severity was graded according to the Fazekas scale, and acute diffusion-weighted-imaging (DWI) lesion volumes were semi-automatically outlined. Predictors of hemorrhagic infarction (HI) and parenchymal hemorrhage (PH) were identified using logistic regression analysis and exact multinomial logistic analysis. HT occurred in 31 patients (17 %). Baseline National Institute of Health Stroke Score (NIHSS; p = 0.008), severe LA (p = 0.02), and diffusion lesion volume (p = 0.02) were predictors of HT in univariable logistic regression. Adjusted to lesion volume and baseline NIHSS score, exact multinomial logistic analysis showed that severe LA was the only independent predictor of parenchymal hemorrhage (p = 0.03). In elderly patients, LA severity better predicts parenchymal hemorrhage than infarct size. (orig.)

Regressive Imagery in Creative Problem-Solving: Comparing Verbal Protocols of Expert and Novice Visual Artists and Computer Programmers

Science.gov (United States)

Kozbelt, Aaron; Dexter, Scott; Dolese, Melissa; Meredith, Daniel; Ostrofsky, Justin

2015-01-01

We applied computer-based text analyses of regressive imagery to verbal protocols of individuals engaged in creative problem-solving in two domains: visual art (23 experts, 23 novices) and computer programming (14 experts, 14 novices). Percentages of words involving primary process and secondary process thought, plus emotion-related words, were…

Son Preference and Family Limitation in Pakistan: A Parity- and Contraceptive Method-Specific Analysis.

Science.gov (United States)

Channon, Melanie Dawn

2017-09-01

Son preference exerts a strong influence over contraceptive and fertility decisions in many South Asian countries. In Pakistan, where fertility remains high and contraceptive use low, research on son preference has been limited. Data from Pakistan Demographic and Health Surveys conducted in 1990-1991, 2006-2007 and 2012-2013 were used to examine potential indicators and outcomes of son preference. Descriptive analyses looked at sex composition preferences of men and women, as well as the sex ratio at last birth. Multivariate logistic regression analyses examined parity progression by birth order, while multinomial logistic regression was used to identify associations between sex composition and use of permanent, temporary and traditional contraceptive methods. Parity progression and choice of contraceptive method are increasingly associated with the sex composition of children. Many respondents wanted at least two sons, though most also wanted at least one daughter. Analyses suggest that the prevalence of modern contraceptive use among parous women would have been 19% higher in 2012-2013 in the absence of son preference. Permanent method use was extremely low among women with no sons and increased significantly with number of sons. The association between number of sons and use of temporary methods was weaker, while son preference had little relationship with traditional method use. The association of son preference with parity progression and modern contraceptive use has become stronger in Pakistan. Continuation of the fertility transition may be difficult unless the degrees of differential stopping behavior and differential contraceptive use decline.

Association between total serum cholesterol and suicide attempts in subjects with major depressive disorder: Exploring the role of clinical and biochemical confounding factors.

Science.gov (United States)

Bartoli, Francesco; Crocamo, Cristina; Dakanalis, Antonios; Riboldi, Ilaria; Miotto, Alessio; Brosio, Enrico; Clerici, Massimo; Carrà, Giuseppe

2017-04-01

We tested whether serum total cholesterol levels might be associated with recent suicide attempts in subjects with major depressive disorder, after controlling for relevant individual characteristics. We conducted a comparative cross-sectional study including consecutive inpatients with major depressive disorder. We differentiated subjects admitted for a recent serious (violent or non-violent) suicide attempt and those without such recent history. Total cholesterol was measured from fasting blood tests. At univariate analyses, suicide attempters had levels of total cholesterol (174.0±45.7mg/dL) lower than non-attempters (193.9±42.6mg/dL) (p=0.004). This was confirmed among both violent (174.1±46.2mg/dL) and non-violent (173.8±46.1mg/dL) suicide attempters (p=0.035 and 0.016, respectively). However, logistic regression analyses, sequentially including demographic, clinical (comorbid alcohol and personality disorders), and biochemical factors, did not show any association between serum cholesterol and recent suicide attempts (p=0.172). Similar findings were observed in multinomial logistic regression analyses, for both violent (p=0.512) and non-violent (p=0.157) suicide attempts. Our findings do not support the hypothesis that serum cholesterol and suicide attempts are associated among subjects with major depressive disorder. The identification of valid and accessible biological markers of suicidal behaviors still represents a challenge for future research. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Big Five personality characteristics are associated with depression subtypes and symptom dimensions of depression in older adults.

Science.gov (United States)

Koorevaar, A M L; Hegeman, J M; Lamers, F; Dhondt, A D F; van der Mast, R C; Stek, M L; Comijs, H C

2017-12-01

This study examined the associations of personality characteristics with both subtypes and symptom dimensions of depression in older adults. Three hundred and seventy-eight depressed older adults participated in the Netherlands Study of Depression in Older Persons. Personality characteristics were assessed by the NEO-Five Factor Inventory. Subtypes and symptom dimensions of depression were determined using the Composite International Diagnostic Interview and the Inventory of Depressive Symptomatology (IDS). Multinomial logistic regression analyses were performed to examine the associations between personality and atypical, melancholic, and unspecified subtypes of major depression. Linear regression analyses examined the associations between personality and the IDS mood, somatic, and motivation symptom dimensions. The analyses were adjusted for confounders and additionally adjusted for depression severity. Neuroticism, Extraversion, Conscientiousness, and Agreeableness were associated with specified (atypical or melancholic) major depression compared with unspecified major depression in the bivariate analyses but lost their significance after adjustments for functional limitations and severity of depression. Neuroticism was positively associated with the IDS mood and motivation symptom dimensions, also in the adjusted models. Further, Extraversion and Agreeableness were negatively associated with the IDS mood symptom dimension, and Extraversion and Conscientiousness were negatively associated with the IDS motivation symptom dimension. None was associated with the IDS somatic symptom dimension. This study demonstrated the association of personality characteristics with mood and motivational symptoms of late-life depression. The lacking ability of personality to differentiate between melancholic and atypical depression seems to be largely explained by severity of depressive symptoms. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

Regression of environmental noise in LIGO data

International Nuclear Information System (INIS)

Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

2015-01-01

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

Temporal trends in sperm count: a systematic review and meta-regression analysis.

Science.gov (United States)

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

2017-11-01

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because

Is antipsychotic polypharmacy associated with metabolic syndrome even after adjustment for lifestyle effects?: a cross-sectional study

Directory of Open Access Journals (Sweden)

Okumura Yasuyuki

2011-07-01

Full Text Available Abstract Background Although the validity and safety of antipsychotic polypharmacy remains unclear, it is commonplace in the treatment of schizophrenia. This study aimed to investigate the degree that antipsychotic polypharmacy contributed to metabolic syndrome in outpatients with schizophrenia, after adjustment for the effects of lifestyle. Methods A cross-sectional survey was carried out between April 2007 and October 2007 at Yamanashi Prefectural KITA hospital in Japan. 334 patients consented to this cross-sectional study. We measured the components consisting metabolic syndrome, and interviewed the participants about their lifestyle. We classified metabolic syndrome into four groups according to the severity of metabolic disturbance: the metabolic syndrome; the pre-metabolic syndrome; the visceral fat obesity; and the normal group. We used multinomial logistic regression models to assess the association of metabolic syndrome with antipsychotic polypharmacy, adjusting for lifestyle. Results Seventy-four (22.2% patients were in the metabolic syndrome group, 61 (18.3% patients were in the pre-metabolic syndrome group, and 41 (12.3% patients were in visceral fat obesity group. Antipsychotic polypharmacy was present in 167 (50.0% patients. In multinomial logistic regression analyses, antipsychotic polypharmacy was significantly associated with the pre-metabolic syndrome group (adjusted odds ratio [AOR], 2.348; 95% confidence interval [CI], 1.181-4.668, but not with the metabolic syndrome group (AOR, 1.269; 95%CI, 0.679-2.371. Conclusions These results suggest that antipsychotic polypharmacy, compared with monotherapy, may be independently associated with an increased risk of having pre-metabolic syndrome, even after adjusting for patients' lifestyle characteristics. As metabolic syndrome is associated with an increased risk of cardiovascular mortality, further studies are needed to clarify the validity and safety of antipsychotic polypharmacy.

Associations between the home environment and children's sweet beverage consumption at 2-year follow-up: the 'Be active, eat right' study.

Science.gov (United States)

van Grieken, A; Renders, C M; van de Gaar, V M; Hirasing, R A; Raat, H

2015-04-01

This study evaluates the association between home environmental characteristics and sweet beverage consumption (i.e. beverages that contain sugar) of 7-year-old children. The population for analysis consisted of n = 2047 parents and their children from the population-based 'Be active, eat right' study. Data on sociodemographic characteristics, parental beliefs, parenting practices and child's sweet beverage consumption were obtained by parental report with questionnaires. We performed linear and multinomial regression analyses evaluating associations between characteristics at age 5 years and (i) consumption at 7 years and (ii) consumption patterns between age 5 and 7 years with reference category 'low consumption'. Based on the report from their parents, 5-year-old children drank on average 3.0 (SD:1.4) sweet beverage per day. Children consumed less sweet beverages at age 7 years (beta -0.16, 95% confidence interval [CI] -0.24 to -0.09) when there were less sweet beverages available at home. The multinomial regression model showed that children with parents who discouraged sweet beverage consumption were more likely to decrease their sweet beverage consumption over the study period (odds ratio: 1.24, 95% CI 1.07 to 1.43). Moreover, when there were less sweet beverages available at home, children were less likely to increase their consumption or have a high consumption over the study period (odds ratio: 0.70, 95% CI 0.59 to 0.83 and 0.61, 95% CI 0.54 to 0.70, respectively). The results showed that characteristics of the home environment are associated with the consumption of sweet beverages among children. Specifically, the availability of sweet beverages at home is associated with the child's sweet beverage consumption. © 2014 The Authors. Pediatric Obesity © 2014 World Obesity.

Influence of quartz exposure on lung cancer types in cases of lymph node-only silicosis and lung silicosis in German uranium miners.

Science.gov (United States)

Mielke, Stefan; Taeger, Dirk; Weitmann, Kerstin; Brüning, Thomas; Hoffmann, Wolfgang

2018-05-04

Inhaled crystalline quartz is a carcinogen. Analyses show differences in the distribution of lung cancer types depending on the status of silicosis. Using 2,524 lung tumor cases from the WISMUT autopsy repository database, silicosis was differentiated into cases without silicosis in lung parenchyma and its lymph nodes, with lymph node-only silicosis, or with lung silicosis including lymph node silicosis. The proportions of adenocarcinoma, squamous cell carcinoma, and small-cell lung carcinoma mortality for increasing quartz exposures were estimated in a multinomial logistic regression model. The relative proportions of the lung cancer subtypes in lymph node-only silicosis were more similar to lung silicosis than without any silicosis. The results support the hypothesis that quartz-related carcinogenesis in case of lymph node-only silicosis is more similar to that in lung silicosis than in without silicosis.

Relationships between self-reported unfair treatment and prescription medication use, illicit drug use, and alcohol dependence among Filipino Americans.

Science.gov (United States)

Gee, Gilbert C; Delva, Jorge; Takeuchi, David T

2007-05-01

We examined associations between self-reported unfair treatment and prescription medication use, illicit drug use, and alcohol dependence. We used data from the Filipino American Community Epidemiological Survey, a cross-sectional investigation involving 2217 Filipino Americans interviewed in 1998-1999. Multinomial logistic and negative binomial regression analyses were used in assessing associations between unfair treatment and the substance use categories. Reports of unfair treatment were associated with prescription drug use, illicit drug use, and alcohol dependence after control for age, gender, location of residence, employment status, educational level, ethnic identity level, nativity, language spoken, marital status, and several health conditions. Unfair treatment may contribute to illness and subsequent use of prescription medications. Furthermore, some individuals may use illicit drugs and alcohol to cope with the stress associated with such treatment. Addressing the antecedents of unfair treatment may be a potential intervention route.

Análise de fatores e regressão bissegmentada em estudos de estratificação ambiental e adaptabilidade em milho Factor analysis and bissegmented regression for studies about environmental stratification and maize adaptability

Directory of Open Access Journals (Sweden)

Deoclécio Domingos Garbuglio

2007-02-01

Full Text Available O objetivo deste trabalho foi verificar possíveis divergências entre os resultados obtidos nas avaliações da adaptabilidade de 27 genótipos de milho (Zea mays L., e na estratificação de 22 ambientes no Estado do Paraná, por meio de técnicas baseadas na análise de fatores e regressão bissegmentada. As estratificações ambientais foram feitas por meio do método tradicional e por análise de fatores, aliada ao porcentual da porção simples da interação GxA (PS%. As análises de adaptabilidade foram realizadas por meio de regressão bissegmentada e análise de fatores. Pela análise de regressão bissegmentada, os genótipos estudados apresentaram alta performance produtiva; no entanto, não foi constatado o genótipo considerado como ideal. A adaptabilidade dos genótipos, analisada por meio de plotagens gráficas, apresentou respostas diferenciadas quando comparada à regressão bissegmentada. A análise de fatores mostrou-se eficiente nos processos de estratificação ambiental e adaptabilidade dos genótipos de milho.The objective of this work was to verify possible divergences among results obtained on adaptability evaluations of 27 maize genotypes (Zea mays L., and on stratification of 22 environments on Paraná State, Brazil, through techniques of factor analysis and bissegmented regression. The environmental stratifications were made through the traditional methodology and by factor analysis, allied to the percentage of the simple portion of GxE interaction (PS%. Adaptability analyses were carried out through bissegmented regression and factor analysis. By the analysis of bissegmented regression, studied genotypes had presented high productive performance; however, it was not evidenced the genotype considered as ideal. The adaptability of the genotypes, analyzed through graphs, presented different answers when compared to bissegmented regression. Factor analysis was efficient in the processes of environment stratification and

Forecasting with Dynamic Regression Models

CERN Document Server

Pankratz, Alan

2012-01-01

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

The analysis of nonstationary time series using regression, correlation and cointegration – with an application to annual mean temperature and sea level

DEFF Research Database (Denmark)

Johansen, Søren

There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference. Finally we analyse some data on annual mean temperature...... and sea level, by applying the cointegrated vector autoregressive model, which explicitly takes into account the nonstationarity of the variables....

Application of nonlinear regression analysis for ammonium exchange by natural (Bigadic) clinoptilolite

International Nuclear Information System (INIS)

Gunay, Ahmet

2007-01-01

The experimental data of ammonium exchange by natural Bigadic clinoptilolite was evaluated using nonlinear regression analysis. Three two-parameters isotherm models (Langmuir, Freundlich and Temkin) and three three-parameters isotherm models (Redlich-Peterson, Sips and Khan) were used to analyse the equilibrium data. Fitting of isotherm models was determined using values of standard normalization error procedure (SNE) and coefficient of determination (R 2 ). HYBRID error function provided lowest sum of normalized error and Khan model had better performance for modeling the equilibrium data. Thermodynamic investigation indicated that ammonium removal by clinoptilolite was favorable at lower temperatures and exothermic in nature

Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

Science.gov (United States)

Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

2014-01-01

Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

Predicting Taxi-Out Time at Congested Airports with Optimization-Based Support Vector Regression Methods

Directory of Open Access Journals (Sweden)

Guan Lian

2018-01-01

Full Text Available Accurate prediction of taxi-out time is significant precondition for improving the operationality of the departure process at an airport, as well as reducing the long taxi-out time, congestion, and excessive emission of greenhouse gases. Unfortunately, several of the traditional methods of predicting taxi-out time perform unsatisfactorily at congested airports. This paper describes and tests three of those conventional methods which include Generalized Linear Model, Softmax Regression Model, and Artificial Neural Network method and two improved Support Vector Regression (SVR approaches based on swarm intelligence algorithm optimization, which include Particle Swarm Optimization (PSO and Firefly Algorithm. In order to improve the global searching ability of Firefly Algorithm, adaptive step factor and Lévy flight are implemented simultaneously when updating the location function. Six factors are analysed, of which delay is identified as one significant factor in congested airports. Through a series of specific dynamic analyses, a case study of Beijing International Airport (PEK is tested with historical data. The performance measures show that the proposed two SVR approaches, especially the Improved Firefly Algorithm (IFA optimization-based SVR method, not only perform as the best modelling measures and accuracy rate compared with the representative forecast models, but also can achieve a better predictive performance when dealing with abnormal taxi-out time states.

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Directory of Open Access Journals (Sweden)

Qiutong Jin

2016-06-01

Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

Emergency Department Use by Nursing Home Residents: Effect of Severity of Cognitive Impairment

Science.gov (United States)

Stephens, Caroline E.; Newcomer, Robert; Blegen, Mary; Miller, Bruce; Harrington, Charlene

2012-01-01

Purpose: To examine the 1-year prevalence and risk of emergency department (ED) use and ambulatory care-sensitive (ACS) ED use by nursing home (NH) residents with different levels of severity of cognitive impairment (CI). Design and Methods: We used multinomial logistic regression to estimate the effect of CI severity on the odds of any ED visit…

Non-proportional odds multivariate logistic regression of ordinal family data.

Science.gov (United States)

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gibrat’s law and quantile regressions

DEFF Research Database (Denmark)

Distante, Roberta; Petrella, Ivan; Santoro, Emiliano

2017-01-01

The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...

ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES

NARCIS (Netherlands)

RUSCHENDORF, L; DEVALK, [No Value

We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive

Introduction to the use of regression models in epidemiology.

Science.gov (United States)

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Emotionally enhanced memory for negatively arousing words: storage or retrieval advantage?

Science.gov (United States)

Nadarevic, Lena

2017-12-01

People typically remember emotionally negative words better than neutral words. Two experiments are reported that investigate whether emotionally enhanced memory (EEM) for negatively arousing words is based on a storage or retrieval advantage. Participants studied non-word-word pairs that either involved negatively arousing or neutral target words. Memory for these target words was tested by means of a recognition test and a cued-recall test. Data were analysed with a multinomial model that allows the disentanglement of storage and retrieval processes in the present recognition-then-cued-recall paradigm. In both experiments the multinomial analyses revealed no storage differences between negatively arousing and neutral words but a clear retrieval advantage for negatively arousing words in the cued-recall test. These findings suggest that EEM for negatively arousing words is driven by associative processes.

From Rasch scores to regression

DEFF Research Database (Denmark)

Christensen, Karl Bang

2006-01-01

Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....

Producing The New Regressive Left

DEFF Research Database (Denmark)

Crone, Christine

members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...

Mixture of Regression Models with Single-Index

OpenAIRE

Xiang, Sijia; Yao, Weixin

2016-01-01

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

Local bilinear multiple-output quantile/depth regression

Czech Academy of Sciences Publication Activity Database

Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

2015-01-01

Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

Science.gov (United States)

Enders, Felicity

2013-12-01

Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

The MIDAS Touch: Mixed Data Sampling Regression Models

OpenAIRE

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

2004-01-01

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Suppression Situations in Multiple Linear Regression

Science.gov (United States)

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Significance testing in ridge regression for genetic data

Directory of Open Access Journals (Sweden)

De Iorio Maria

2011-09-01

Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor

2012-06-29

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.

2012-01-01

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Cross-sectional time series analysis of associations between education and girl child marriage in Bangladesh, India, Nepal and Pakistan, 1991-2011.

Science.gov (United States)

Raj, Anita; McDougal, Lotus; Silverman, Jay G; Rusch, Melanie L A

2014-01-01

Girl education is believed to be the best means of reducing girl child marriage (marriage girl child marriages occur, substantial improvements in girl education have not corresponded to equivalent reductions in child marriage. This study examines the levels of education associated with female age at marriage over the previous 20 years across four South Asian nations with high rates (>20%) of girl child marriage- Bangladesh, India, Nepal and Pakistan. Cross-sectional time series analyses were conducted on Demographic and Health Surveys (DHS) from 1991 to 2011 in the four focal nations. Analyses were restricted to ever-married women aged 20-24 years. Multinomial logistic regression models were used to assess the effect of highest level of education received (none, primary, secondary or higher) on age at marriage (girl child marriage; in Nepal, it was protective against marriage at girl child marriage in South Asia, outside of India. Secondary education may be a better protective strategy against this practice for the region, but may be less effective for prevention of marriage among older relative to younger adolescents.

Few crystal balls are crystal clear : eyeballing regression

International Nuclear Information System (INIS)

Wittebrood, R.T.

1998-01-01

The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

Meta-analyses of the 5-HTTLPR polymorphisms and post-traumatic stress disorder.

Directory of Open Access Journals (Sweden)

Fernando Navarro-Mateu

Full Text Available OBJECTIVE: To conduct a meta-analysis of all published genetic association studies of 5-HTTLPR polymorphisms performed in PTSD cases. METHODS DATA SOURCES: Potential studies were identified through PubMed/MEDLINE, EMBASE, Web of Science databases (Web of Knowledge, WoK, PsychINFO, PsychArticles and HuGeNet (Human Genome Epidemiology Network up until December 2011. STUDY SELECTION: Published observational studies reporting genotype or allele frequencies of this genetic factor in PTSD cases and in non-PTSD controls were all considered eligible for inclusion in this systematic review. DATA EXTRACTION: Two reviewers selected studies for possible inclusion and extracted data independently following a standardized protocol. STATISTICAL ANALYSIS: A biallelic and a triallelic meta-analysis, including the total S and S' frequencies, the dominant (S+/LL and S'+/L'L' and the recessive model (SS/L+ and S'S'/L'+, was performed with a random-effect model to calculate the pooled OR and its corresponding 95% CI. Forest plots and Cochran's Q-Statistic and I(2 index were calculated to check for heterogeneity. Subgroup analyses and meta-regression were carried out to analyze potential moderators. Publication bias and quality of reporting were also analyzed. RESULTS: 13 studies met our inclusion criteria, providing a total sample of 1874 patients with PTSD and 7785 controls in the biallelic meta-analyses and 627 and 3524, respectively, in the triallelic. None of the meta-analyses showed evidence of an association between 5-HTTLPR and PTSD but several characteristics (exposure to the same principal stressor for PTSD cases and controls, adjustment for potential confounding variables, blind assessment, study design, type of PTSD, ethnic distribution and Total Quality Score influenced the results in subgroup analyses and meta-regression. There was no evidence of potential publication bias. CONCLUSIONS: Current evidence does not support a direct effect of 5-HTTLPR

Meta-analyses of the 5-HTTLPR polymorphisms and post-traumatic stress disorder.

Science.gov (United States)

Navarro-Mateu, Fernando; Escámez, Teresa; Koenen, Karestan C; Alonso, Jordi; Sánchez-Meca, Julio

2013-01-01

To conduct a meta-analysis of all published genetic association studies of 5-HTTLPR polymorphisms performed in PTSD cases. Potential studies were identified through PubMed/MEDLINE, EMBASE, Web of Science databases (Web of Knowledge, WoK), PsychINFO, PsychArticles and HuGeNet (Human Genome Epidemiology Network) up until December 2011. Published observational studies reporting genotype or allele frequencies of this genetic factor in PTSD cases and in non-PTSD controls were all considered eligible for inclusion in this systematic review. Two reviewers selected studies for possible inclusion and extracted data independently following a standardized protocol. A biallelic and a triallelic meta-analysis, including the total S and S' frequencies, the dominant (S+/LL and S'+/L'L') and the recessive model (SS/L+ and S'S'/L'+), was performed with a random-effect model to calculate the pooled OR and its corresponding 95% CI. Forest plots and Cochran's Q-Statistic and I(2) index were calculated to check for heterogeneity. Subgroup analyses and meta-regression were carried out to analyze potential moderators. Publication bias and quality of reporting were also analyzed. 13 studies met our inclusion criteria, providing a total sample of 1874 patients with PTSD and 7785 controls in the biallelic meta-analyses and 627 and 3524, respectively, in the triallelic. None of the meta-analyses showed evidence of an association between 5-HTTLPR and PTSD but several characteristics (exposure to the same principal stressor for PTSD cases and controls, adjustment for potential confounding variables, blind assessment, study design, type of PTSD, ethnic distribution and Total Quality Score) influenced the results in subgroup analyses and meta-regression. There was no evidence of potential publication bias. Current evidence does not support a direct effect of 5-HTTLPR polymorphisms on PTSD. Further analyses of gene-environment interactions, epigenetic modulation and new studies with large samples

Regression methods for medical research

CERN Document Server

Tai, Bee Choo

2013-01-01

Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

Dairy shows different associations with abdominal and BMI-defined overweight: Cross-sectional analyses exploring a variety of dairy products.

Science.gov (United States)

Brouwer-Brolsma, E M; Sluik, D; Singh-Povel, C M; Feskens, E J M

2018-05-01

Previous studies have suggested weight-regulatory properties for several dairy nutrients, but population-based studies on dairy and body weight are inconclusive. We explored cross-sectional associations between dairy consumption and indicators of overweight. We included 114,682 Dutch adults, aged ≥18 years. Dairy consumption was quantified by a food frequency questionnaire. Abdominal overweight was defined as waist circumference (WC) ≥88 cm (women) or ≥102 cm (men) (n = 37,391), overweight as BMI ≥25-30 kg/m 2 (n = 44,772) and obesity as BMI ≥30 kg/m 2 (n = 15,339). Associations were quantified by logistic (abdominal overweight, no/yes), multinomial logistic (BMI-defined overweight and obesity) and linear regression analyses (continuous measures of WC and BMI), and they were adjusted for relevant covariates. Total dairy showed a positive association with abdominal overweight (OR Q1 ref vs. Q5: 1.09; 95% CI: 1.04-1.14) and with BMI-defined overweight (OR Q5 1.13; 95% CI: 1.08-1.18) and obesity (OR Q5 1.09; 95% CI: 1.02-1.16). Skimmed, semi-skimmed and non-fermented dairy also showed positive associations with overweight categories. Full-fat dairy showed an inverse association with overweight and obesity (OR Q5 for obesity: 0.78; 95% CI: 0.73-0.83). Moreover, inverse associations were observed for yoghurt and custard and positive associations for milk, buttermilk, flavoured yoghurt drinks, cheese and cheese snacks. Fermented dairy, curd cheese and Dutch cheese did not show a consistent association with overweight categories. Total, skimmed, semi-skimmed and non-fermented dairy; milk; buttermilk; flavoured yoghurt drinks; total cheese and cheese snacks showed a positive association with overweight categories, whereas full-fat dairy, custard and yoghurt showed an inverse association with overweight categories. Copyright © 2018 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human

Should metacognition be measured by logistic regression?

Science.gov (United States)

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Anatomical characterization of central, apical and minimal corneal thickness

Directory of Open Access Journals (Sweden)

Federico Saenz-Frances

2014-08-01

Full Text Available AIM: To anatomically locate the points of minimum corneal thickness and central corneal thickness (pupil center in relation to the corneal apex.METHODS: Observational, cross-sectional study, 299 healthy volunteers. Thickness at the corneal apex (AT, minimum corneal thickness (MT and corneal thickness at the pupil center (PT were determined using the pentacam. Distances from the corneal apex to MT (MD and PT (PD were calculated and their quadrant position (taking the corneal apex as the reference determined:point of minimum thickness (MC and point of central thickness (PC depending on the quadrant position. Two multivariate linear regression models were constructed to examine the influence of age, gender, power of the flattest and steepest corneal axes, position of the flattest axis, corneal volume (determined using the Pentacam and PT on MD and PD. The effects of these variables on MC and PC were also determined in two multinomial regression models.RESULTS: MT was located at a mean distance of 0.909 mm from the apex (79.4% in the inferior-temporal quadrant. PT was located at a mean distance of 0.156 mm from the apex. The linear regression model for MD indicated it was significantly influenced by corneal volume (B=-0.024; 95%CI:-0.043 to -0.004. No significant relations were identified in the linear regression model for PD or the multinomial logistic regressions for MC and PC.CONCLUSION: MT was typically located at the inferior-temporal quadrant of the cornea and its distance to the corneal apex tended to decrease with the increment of corneal volume.

BOX-COX REGRESSION METHOD IN TIME SCALING

Directory of Open Access Journals (Sweden)

ATİLLA GÖKTAŞ

2013-06-01

Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Regression Analysis and the Sociological Imagination

Science.gov (United States)

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Gender aspects of the relationship between migraine and cardiovascular risk factors: A cross-sectional evaluation in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Science.gov (United States)

Goulart, Alessandra C; Santos, Itamar S; Lotufo, Paulo A; Benseñor, Isabela M

2015-10-01

The relationship between cardiovascular risk factors (CVRF) and migraine is controversial and might be different in both genders. These associations were evaluated in Brazilian middle-aged men and women from the Longitudinal Study of Adult Health (ELSA-Brasil). The cross-sectional relationship between our main outcome, which was migraine headache (definite, probable and overall), and CVRF was evaluated in the total sample and according to gender. We calculated frequencies and odds ratios (95% CI) for this relationship using binary and multinomial logistic regression analyses in crude, age-adjusted and multivariable models adjusted by potential confounders. Of 14,953 individuals who completed the data about headache and CVRF, the frequency of one-year migraine was of 29.5% (22.5% in women and 7.0% in men). In the multivariable-adjusted regression analyses, an inverse association between hypertension (OR, 0.53; 95% CI, 0.36-0.79), metabolic syndrome (OR, 0.65; 95% CI, 0.43-0.99) and definite migraine were confirmed for men, but not for women. In the opposite direction, a positive association between migraine headaches (definite, probable and overall) and dyslipidemia (overall migraine OR, 1.25; 95% CI, 1.13-1.38) was observed only for women, but not for men. A gender influence on the relationship between migraine and CVRF was verified in the ELSA-Brasil. © International Headache Society 2015.

Determinants of agricultural micro-credit repayment-evidence from ...

African Journals Online (AJOL)

Agro-Science ... for the study while descriptive statistics and multinomial binary logit model were employed for data analyses. ... on long term funds (without prejudice to the existing revolving loan mechanism) such as the pension contributions, ...

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

[Prevalence and factors associated with intimate partner abuse in female users of public health services in Mexico: a comparative analyses].

Science.gov (United States)

Ávila-Burgos, Leticia; Valdez-Santiagob, Rosario; Barroso-Quiab, Abigail; Híjar, Martha; Rojas, Rosalba; Del Río-Zolezzi, Aurora

2014-01-01

To analyze the evolution of the prevalence in intimate partner violence during the years 2003 and 2006 in Mexico, identifying factors associated with its severity, comparing our results with findings from 2003. Data from the Encuesta Nacional de Violencia contra las Mujeres (ENVIM 2006) was used; it has urban-rural national representation of female users of Mexican public health services. A total of 22,318 women above 14 years of age were interviewed. A multinomial logistic regression model was adjusted. The dependent variable was the Index of Intimate Partner Abuse. Intimate partner abuse increased 17% in comparison to the year 2003. Women's personal history of childhood abuse (ORA= 5.12, 95% CI4.15-6.30) and rape (ORA = 3.5, 95% CI = 2.66-4.62) were the most important women's factors that were found associated with severe violence. Male partner's daily alcohol consumption increased eleven fold the possibility of severe violence; higher disagreement with traditional female gender roles and higher education of both partners were protective factors. Factors associated with violence and their severities were consistent with findings reported in 2003. Intimate partner violence is a highly prevalent social problem which requires comprehensive strategies supporting empowerment of women through higher education, early detection and care of those battered, as well as structured interventions to prevent violence in future generations.

Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution

International Nuclear Information System (INIS)

Shaby, Benjamin A.; Field, Christopher B.

2006-01-01

In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Science.gov (United States)

Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

2016-02-01

The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Directory of Open Access Journals (Sweden)

Saro Lee

2016-02-01

Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

A new approach to analyse longitudinal epidemiological data with an excess of zeros.

Science.gov (United States)

Spriensma, Alette S; Hajos, Tibor R S; de Boer, Michiel R; Heymans, Martijn W; Twisk, Jos W R

2013-02-20

Within longitudinal epidemiological research, 'count' outcome variables with an excess of zeros frequently occur. Although these outcomes are frequently analysed with a linear mixed model, or a Poisson mixed model, a two-part mixed model would be better in analysing outcome variables with an excess of zeros. Therefore, objective of this paper was to introduce the relatively 'new' method of two-part joint regression modelling in longitudinal data analysis for outcome variables with an excess of zeros, and to compare the performance of this method to current approaches. Within an observational longitudinal dataset, we compared three techniques; two 'standard' approaches (a linear mixed model, and a Poisson mixed model), and a two-part joint mixed model (a binomial/Poisson mixed distribution model), including random intercepts and random slopes. Model fit indicators, and differences between predicted and observed values were used for comparisons. The analyses were performed with STATA using the GLLAMM procedure. Regarding the random intercept models, the two-part joint mixed model (binomial/Poisson) performed best. Adding random slopes for time to the models changed the sign of the regression coefficient for both the Poisson mixed model and the two-part joint mixed model (binomial/Poisson) and resulted into a much better fit. This paper showed that a two-part joint mixed model is a more appropriate method to analyse longitudinal data with an excess of zeros compared to a linear mixed model and a Poisson mixed model. However, in a model with random slopes for time a Poisson mixed model also performed remarkably well.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

riskRegression

DEFF Research Database (Denmark)

Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

2017-01-01

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Expert Elicitation of Multinomial Probabilities for Decision-Analytic Modeling: An Application to Rates of Disease Progression in Undiagnosed and Untreated Melanoma.

Science.gov (United States)

Wilson, Edward C F; Usher-Smith, Juliet A; Emery, Jon; Corrie, Pippa G; Walter, Fiona M

2018-06-01

Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Efficiency of insurance companies: Application of DEA and Tobit analyses

Directory of Open Access Journals (Sweden)

Eva Grmanová

2017-10-01

Full Text Available The aim of this paper is to determine the relationship between technical efficiency and profitability of insurance companies. The profitability of insurance companies was expressed by such indicators as ROA, ROE and the size of assets. We analysed 15 commercial insurance companies in Slovakia in the period of 2013-2015. Technical efficiency scores were expressed using DEA models. The relationship between the technical efficiency score and the indicators of profitability was expressed using censored regression, i.e. the Tobit regression model and the Mann-Whitney U-test. The relationship between the technical efficiency score in the CCR and BCC models and all the groups formed on the basis of the return on assets and the group formed basing on the return on equity was not confirmed. Statistically significant difference between average technical efficiency score in the CCR model in the group of insurance companies with ROA

Computing multiple-output regression quantile regions

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2012-01-01

Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf

Preface to Berk's "Regression Analysis: A Constructive Critique"

OpenAIRE

de Leeuw, Jan

2003-01-01

It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

Work-Family Conflict and Retirement Preferences

OpenAIRE

Raymo, James M.; Sweeney, Megan M

2005-01-01

Objectives: This study investigates relationships between perceived levels of work-family conflict and retirement preferences. Methods: Using the large sample of 52-54 year-old respondents to the 1992 Wisconsin Longitudinal Study, we estimate multinomial logistic regression models of preferences for partial and full retirement within the next ten years. We examine the association between preferences for retirement and perceived work-family conflict...

Five cases of caudal regression with an aberrant abdominal umbilical artery: Further support for a caudal regression-sirenomelia spectrum.

Science.gov (United States)

Duesterhoeft, Sara M; Ernst, Linda M; Siebert, Joseph R; Kapur, Raj P

2007-12-15

Sirenomelia and caudal regression have sparked centuries of interest and recent debate regarding their classification and pathogenetic relationship. Specific anomalies are common to both conditions, but aside from fusion of the lower extremities, an aberrant abdominal umbilical artery ("persistent vitelline artery") has been invoked as the chief anatomic finding that distinguishes sirenomelia from caudal regression. This observation is important from a pathogenetic viewpoint, in that diversion of blood away from the caudal portion of the embryo through the abdominal umbilical artery ("vascular steal") has been proposed as the primary mechanism leading to sirenomelia. In contrast, caudal regression is hypothesized to arise from primary deficiency of caudal mesoderm. We present five cases of caudal regression that exhibit an aberrant abdominal umbilical artery similar to that typically associated with sirenomelia. Review of the literature identified four similar cases. Collectively, the series lends support for a caudal regression-sirenomelia spectrum with a common pathogenetic basis and suggests that abnormal umbilical arterial anatomy may be the consequence, rather than the cause, of deficient caudal mesoderm. (c) 2007 Wiley-Liss, Inc.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia; Rue, Haavard

2018-01-01

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

Latitudinal variation in the symbiotic dinoflagellateSymbiodiniumof the common reef zoantharianPalythoa tuberculosaon the Saudi Arabian coast of the Red Sea

KAUST Repository

Reimer, James D.

2016-08-08

Aim The Red Sea presents an ideal setting to explore the variability of Symbiodinium over environmental, latitudinal and geographical gradients. We used sequences from two molecular markers to examine genetic diversity of Symbiodinium associated with the widely distributed zoantharian Palythoa tuberculosa in the northern and central Red Sea. Location Northern and central Red Sea. Methods Specimens (n = 329) were collected from 15 locations. Sequence data from nuclear ribosomal ITS2 (n = 269) and chloroplast minicircle psbAncr (n = 173) were phylogenetically analysed (maximum likelihood, neighbour joining), and Symbiodinium types identified for each P. tuberculosa colony. To establish whether environment was a strong predictor of Symbiodinium psbAncr lineage, SST, chlorophyll-a, salinity, and depth data were fit into a multinomial logistic regression using the package VGAM in the R statistical environment. Results Based on ITS2 and psbAncr results, P. tuberculosa colonies were shown to be in symbioses with Symbiodinium clade C (n = 172) and clade D (n = 1). Within clade C, four psbAncr lineages were observed; closely related lineages designated Pt-1-a and Pt-1-b, and closely related lineages Pt-3-a and Pt-3-b. By location, Pt-1-a dominated the sites within the Gulf of Aqaba (c. 86%, 37/43 colonies). At the entrance to the Gulf of Aqaba, Pt-3-a dominated (c. 88%, 15/17), while the more southern remaining sites in the Red Sea were dominated by Pt-3-b (c. 78%, 89/113). Main conclusions Multinomial logistic regression analyses established that predictions based on the combination of temperature, chlorophyll-a and salinity accurately reflected symbiont distributions in the central and northern Red Sea. Palythoa tuberculosa host Pt-1-a in the coldest region, the Gulf of Aqaba (annual average SST = 24.5–25.0 °C), while immediately to the south Pt-3-a dominates (SST = 26.0–26.5 °C), with warmest southern sites dominated by Pt-3-b (SST > 26.5 °C). The Gulf of Aqaba is

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Transcriptome analysis of spermatogenically regressed, recrudescent and active phase testis of seasonally breeding wall lizards Hemidactylus flaviviridis.

Directory of Open Access Journals (Sweden)

Mukesh Gautam

Full Text Available Reptiles are phylogenically important group of organisms as mammals have evolved from them. Wall lizard testis exhibits clearly distinct morphology during various phases of a reproductive cycle making them an interesting model to study regulation of spermatogenesis. Studies on reptile spermatogenesis are negligible hence this study will prove to be an important resource.Histological analyses show complete regression of seminiferous tubules during regressed phase with retracted Sertoli cells and spermatognia. In the recrudescent phase, regressed testis regain cellular activity showing presence of normal Sertoli cells and developing germ cells. In the active phase, testis reaches up to its maximum size with enlarged seminiferous tubules and presence of sperm in seminiferous lumen. Total RNA extracted from whole testis of regressed, recrudescent and active phase of wall lizard was hybridized on Mouse Whole Genome 8×60 K format gene chip. Microarray data from regressed phase was deemed as control group. Microarray data were validated by assessing the expression of some selected genes using Quantitative Real-Time PCR. The genes prominently expressed in recrudescent and active phase testis are cytoskeleton organization GO 0005856, cell growth GO 0045927, GTpase regulator activity GO: 0030695, transcription GO: 0006352, apoptosis GO: 0006915 and many other biological processes. The genes showing higher expression in regressed phase belonged to functional categories such as negative regulation of macromolecule metabolic process GO: 0010605, negative regulation of gene expression GO: 0010629 and maintenance of stem cell niche GO: 0045165.This is the first exploratory study profiling transcriptome of three drastically different conditions of any reptilian testis. The genes expressed in the testis during regressed, recrudescent and active phase of reproductive cycle are in concordance with the testis morphology during these phases. This study will pave

A method to determine the necessity for global signal regression in resting-state fMRI studies.

Science.gov (United States)

Chen, Gang; Chen, Guangyu; Xie, Chunming; Ward, B Douglas; Li, Wenjun; Antuono, Piero; Li, Shi-Jiang

2012-12-01

In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures. Copyright © 2012 Wiley Periodicals, Inc.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Demonstration of a Fiber Optic Regression Probe

Science.gov (United States)

Korman, Valentin; Polzin, Kurt A.

2010-01-01

The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

Caudal regression syndrome : a case report

International Nuclear Information System (INIS)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun

1998-01-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging

Caudal regression syndrome : a case report

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)

1998-07-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.

Correlation and simple linear regression.

Science.gov (United States)

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

2003-06-01

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

bayesQR: A Bayesian Approach to Quantile Regression

Directory of Open Access Journals (Sweden)

Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

Eimeriosis in Danish Dairy Calves – Correlation between Species, Oocyst Excretion and Diarrhoea

DEFF Research Database (Denmark)

Enemark, Heidi L.; Dahl, Jan; Enemark, Jörg M Dehn

2013-01-01

The study collected up-to-date data on prevalence and importance of Eimeria infections in Danish dairy calves with suspected clinical eimeriosis and analysed correlation between Eimeria spp., oocyst excretion and diarrhoea. From October 2010 through August 2011, veterinarians collected faecal...... determined, along with opg values for the specific Eimeria spp. Association between opg and faeces consistency was evaluated in a multinomial, logistic regression model. Overall prevalence of Eimeria spp. was 96.2 % with a prevalence of 60.9 % in individual calves. E. zuernii and/or E. bovis were detected...... in 88.5 % of herds and 41.5 % of the calves. Mean opg was 2,040 (range 0–114,000) in the calves, of which 18.1 % had opg values ≥ 1,000. A total of 12 Eimeria spp. was found with the following calf prevalences: E. ellipsoidalis (37 %), E. zuernii (32 %), E. bovis (28 %), E. cylindrica (23 %), E...

Social skills as precursors of cannabis use in young adolescents: a TRAILS study.

Science.gov (United States)

Griffith-Lendering, Merel F H; Huijbregts, Stephan C J; Huizink, Anja C; Ormel, Hans; Verhulst, Frank C; Vollebergh, Wilma A M; Swaab, Hanna

2011-01-01

Social skills (cooperation, assertion, and self-control) were assessed by teachers for a longitudinal cohort of (pre)adolescents, with measurements at average ages 11.1 (baseline) and 16.3 years (follow-up). Prospective associations with participants' self-reported use of cannabis, (age of) onset of cannabis use, and frequency of use at follow-up were examined using multinomial logistic regression analyses. Teacher-reported social skills predicted different aspects of cannabis use independent of better known factors such as presence of externalizing behavior and use of other substances. The direction of associations depended on the type of social skill. Good cooperation skills during early adolescence were associated with a reduced risk of lifetime cannabis use and a reduced risk of using cannabis on a regular basis. On the other hand, assertion at age 11 increased the risk of lifetime cannabis use and of using cannabis on an experimental basis.

The Great Recession, Life Events, and Mental Health of Older Adults.

Science.gov (United States)

Pruchno, Rachel; Heid, Allison R; Wilson-Genderson, Maureen

2017-03-01

Historical events and personal experiences have the potential to alter the way people age. Using a life-course model, we examined how the Economic Recession of 2008 and experienced life events affected the mental health of 3,393 older adults in New Jersey. Data collected between 2006 and 2012 revealed a significant increase in mean depressive symptoms. Multinomial logistic regression analyses indicated that people with incident depression were more likely to have lost a job, become a caregiver, experienced a major illness, or have a family member with a major illness than people with no depression. Compared with the incident depression group, those with remitted depression were less likely to report having lost a job or experienced a major illness. Modeling the effects of individual life events and the economic recession on depression enriches understanding about the association between macro socioeconomic events, life events, and the mental health of older adults.

Risk Factors for Knee Injuries in Children 8-15 Years

DEFF Research Database (Denmark)

Junge, Tina; Runge, Lisbeth; Juul-Kristensen, Birgit

2016-01-01

.14). For overuse knee injuries, intrinsic risk factors were sex (girls OR 1.38), and previous knee injury (OR 1.78), while participation in soccer (OR 1.64), handball (OR 1.95), basket (OR 2.07), rhythmic (OR 1.98), and tumbling gymnastics (OR 1.74) were additional risk factors. For both injury types, sport...... and participation in soccer, handball, basket, rhythmic and tumbling gymnastics. Further risk factors for both types of injury were participation in sports above two times/week. Although growth-related overuse knee injuries are a self-limiting condition, a major part of children are affected by these injuries...... intrinsic and extrinsic factors for risk of these injuries. METHODS: Weekly musculoskeletal pain, sport participation and sports type were reported by 1326 school children (8-15 years). Knee injuries were classified as traumatic or overuse. Multinomial logistic regression was used for analyses. RESULTS...

The public understanding of nanotechnology in the food domain: the hidden role of views on science, technology, and nature.

Science.gov (United States)

Vandermoere, Frederic; Blanchemanche, Sandrine; Bieberstein, Andrea; Marette, Stephan; Roosen, Jutta

2011-03-01

In spite of great expectations about the potential of nanotechnology, this study shows that people are rather ambiguous and pessimistic about nanotechnology applications in the food domain. Our findings are drawn from a survey of public perceptions about nanotechnology food and nanotechnology food packaging (N = 752). Multinomial logistic regression analyses further reveal that knowledge about food risks and nanotechnology significantly influences people's views about nanotechnology food packaging. However, knowledge variables were unrelated to support for nanofood, suggesting that an increase in people's knowledge might not be sufficient to bridge the gap between the excitement some business leaders in the food sector have and the restraint of the public. Additionally, opposition to nanofood was not related to the use of heuristics but to trust in governmental agencies. Furthermore, the results indicate that public perceptions of nanoscience in the food domain significantly relate to views on science, technology, and nature.

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

Science.gov (United States)

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

2017-12-01

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

Estimated prevalence of halitosis: a systematic review and meta-regression analysis.

Science.gov (United States)

Silva, Manuela F; Leite, Fábio R M; Ferreira, Larissa B; Pola, Natália M; Scannapieco, Frank A; Demarco, Flávio F; Nascimento, Gustavo G

2018-01-01

This study aims to conduct a systematic review to determine the prevalence of halitosis in adolescents and adults. Electronic searches were performed using four different databases without restrictions: PubMed, Scopus, Web of Science, and SciELO. Population-based observational studies that provided data about the prevalence of halitosis in adolescents and adults were included. Additionally, meta-analyses, meta-regression, and sensitivity analyses were conducted to synthesize the evidence. A total of 584 articles were initially found and considered for title and abstract evaluation. Thirteen articles met inclusion criteria. The combined prevalence of halitosis was found to be 31.8% (95% CI 24.6-39.0%). Methodological aspects such as the year of publication and the socioeconomic status of the country where the study was conducted seemed to influence the prevalence of halitosis. Our results demonstrated that the estimated prevalence of halitosis was 31.8%, with high heterogeneity between studies. The results suggest a worldwide trend towards a rise in halitosis prevalence. Given the high prevalence of halitosis and its complex etiology, dental professionals should be aware of their roles in halitosis prevention and treatment.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

Regression: The Apple Does Not Fall Far From the Tree.

Science.gov (United States)

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.