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Articles 211 - 240 of 1162
Full-Text Articles in Statistics and Probability
Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha
Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha
Journal of Modern Applied Statistical Methods
A comprehensive study of graphical log-linear models for contingency tables is presented. High-dimensional contingency tables arise in many areas. Analysis of contingency tables involving several factors or categorical variables is very hard. To determine interactions among various factors, graphical and decomposable log-linear models are preferred. Connections between the conditional independence in probability and graphs are explored, followed with illustrations to describe how graphical log-linear model are useful to interpret the conditional independences between factors. The problem of estimation and model selection in decomposable models is discussed.
Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky
Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
The Mood-Westenberg and Siegel-Tukey tests were examined to determine their robustness with respect to Type-I error for detecting variance changes when their assumptions of equal means were slightly violated, a condition that approaches the Behrens-Fisher problem. Monte Carlo methods were used via 34,606 variations of sample sizes, α levels, distributions/data sets, treatments modeled as a change in scale, and treatments modeled as a shift in means. The Siegel-Tukey was the more robust, and was able to handle a more diverse set of conditions.
A New Estimator For The Pickands Dependence Function, Marta Ferreira
A New Estimator For The Pickands Dependence Function, Marta Ferreira
Journal of Modern Applied Statistical Methods
The Pickands dependence function characterizes an extreme value copula, a useful tool in the modeling of multivariate extremes. A new estimator is presented along with its convergence properties and performance through simulation.
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Journal of Modern Applied Statistical Methods
Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset.
Vol. 16, No. 1 (Full Issue), Jmasm Editors
Vol. 16, No. 1 (Full Issue), Jmasm Editors
Journal of Modern Applied Statistical Methods
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Robust Ancova: Confidence Intervals That Have Some Specified Simultaneous Probability Coverage When There Is Curvature And Two Covariates, Rand Wilcox
Journal of Modern Applied Statistical Methods
Consider the commonly occurring situation where the goal is to compare two independent groups and there are two covariates. Let Mj(X) be some conditional measure of location for the jth group associated with some random variable Y given X = (X1, X2). The goal is to H0: M1(X) = M2(X) for each X Ω in a manner that controls the probability of one or more Type I errors. An extant technique (method M1 here) addresses this goal without making any parametric assumption about Mj(X). However, a practical concern is that it does not provide enough detail regarding where the regression …
Limitations In The Systematic Analysis Of Structural Equation Model Fit Indices, Sarah A. Rose, Barry Markman, Shlomo Sawilowsky
Limitations In The Systematic Analysis Of Structural Equation Model Fit Indices, Sarah A. Rose, Barry Markman, Shlomo Sawilowsky
Journal of Modern Applied Statistical Methods
The purpose of this study was to evaluate the sensitivity of selected fit index statistics in determining model fit in structural equation modeling (SEM). The results indicated a large dependency on correlation magnitude of the input correlation matrix, with mixed results when the correlation magnitudes were low and a primary indication of good model fit. This was due to the default SEM method of Maximum Likelihood that assumes unstandardized correlation values. However, this warning is not well-known, and is only obscurely mentioned in some textbooks. Many SEM computer software programs do not give appropriate error indications that the results are …
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
A Note On Determination Of Sample Size From The Perspective Of Six Sigma Quality, Joghee Ravichandran
Journal of Modern Applied Statistical Methods
In most empirical studies (clinical, network modeling, and survey-based and aeronautical studies, etc.), sample observations are drawn from population to analyze and draw inferences about the population. Such analysis is done with reference to a measurable quality characteristic of a product or process of interest. However, fixing a sample size is an important task that has to be decided by the experimenter. One of the means in deciding an appropriate sample size is the fixation of error limit and the associated confidence level. This implies that the analysis based on the sample used must guarantee the prefixed error and confidence …
Methodology For Constructing Perceptual Maps Incorporating Measuring Error In Sensory Acceptance Tests, Elisa Norberto Ferreira Santos, Gilberto Rodrigues Liska, Marcelo Angelo Cirillo
Methodology For Constructing Perceptual Maps Incorporating Measuring Error In Sensory Acceptance Tests, Elisa Norberto Ferreira Santos, Gilberto Rodrigues Liska, Marcelo Angelo Cirillo
Journal of Modern Applied Statistical Methods
A new method is proposed based on construction of perceptual maps using techniques of correspondence analysis and interval algebra that allow specifying the measurement error expected in panel choices in the evaluation form described in unstructured 9-point hedonic scale.
Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, Kiran Ganpati Potdar, D. T. Shirke
Confidence Intervals For The Scaled Half-Logistic Distribution Under Progressive Type-Ii Censoring, Kiran Ganpati Potdar, D. T. Shirke
Journal of Modern Applied Statistical Methods
Confidence interval construction for the scale parameter of the half-logistic distribution is considered using four different methods. The first two are based on the asymptotic distribution of the maximum likelihood estimator (MLE) and log-transformed MLE. The last two are based on pivotal quantity and generalized pivotal quantity, respectively. The MLE for the scale parameter is obtained using the expectation-maximization (EM) algorithm. Performances are compared with the confidence intervals proposed by Balakrishnan and Asgharzadeh via coverage probabilities, length, and coverage-to-length ratio. Simulation results support the efficacy of the proposed approach.
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
Journal of Modern Applied Statistical Methods
An exponential-type estimator is developed for the population mean of the sensitive study variable based on various Randomized Response Techniques (RRT) using a non-sensitive auxiliary variable. The mean squared error (MSE) of the proposed estimator is derived for generalized RRT models. The proposed estimator is compared with competitors in a simulation study and an application. The proposed estimator is found to be more efficient using a non-sensitive auxiliary variable.
Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan
Plant Leaf Image Detection Method Using A Midpoint Circle Algorithm For Shape-Based Feature Extraction, B. Vijaya Lakshmi, V. Mohan
Journal of Modern Applied Statistical Methods
Shape-based feature extraction in content-based image retrieval is an important research area at present. An algorithm is presented, based on shape features, to enhance the set of features useful in a leaf identification system.
Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi
Multiple Ratio Imputation By The Emb Algorithm: Theory And Simulation, Masayoshi Takahashi
Journal of Modern Applied Statistical Methods
Although multiple imputation is the gold standard of treating missing data, single ratio imputation is often used in practice. Based on Monte Carlo simulation, the Expectation-Maximization with Bootstrapping (EMB) algorithm to create multiple ratio imputation is used to fill in the gap between theory and practice.
Jmasm45: A Computer Program For Bayesian D-Optimal Binary Repeated Measurements Designs (Matlab), Haftom Temesgen Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger
Jmasm45: A Computer Program For Bayesian D-Optimal Binary Repeated Measurements Designs (Matlab), Haftom Temesgen Abebe, Frans E. S. Tan, Gerard J. P. Van Breukelen, Martijn P. F. Berger
Journal of Modern Applied Statistical Methods
Planners of longitudinal studies of binary responses in applied sciences have not yet benefitted from optimal designs, which have been shown to improve precision of model parameter estimates, due to absence of a computer program. An interactive computer program for Bayesian optimal binary repeated measurements designs is presented for this purpose.
An Extended Weighted Exponential Distribution, Abbas Mahdavi, Leila Jabari
An Extended Weighted Exponential Distribution, Abbas Mahdavi, Leila Jabari
Journal of Modern Applied Statistical Methods
A new class of weighted distributions is proposed by incorporating an extended exponential distribution in Azzalini’s (1985) method. Several statistics and reliability properties of this new class of distribution are obtained. Maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms; they have to be obtained by solving some numerical methods. Two data sets are analyzed for illustrative purposes, and show that the proposed model can be used effectively in analyzing real data.
A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi
A Comparison Of Depth Functions In Maximal Depth Classification Rules, Olusola Samuel Makinde, Adeyinka Damilare Adewumi
Journal of Modern Applied Statistical Methods
Data depth has been described as alternative to some parametric approaches in analyzing many multivariate data. Many depth functions have emerged over two decades and studied in literature. In this study, a nonparametric approach to classification based on notions of different data depth functions is considered and some properties of these methods are studied. The performance of different depth functions in maximal depth classifiers is investigated using simulation and real data with application to agricultural industry.
The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel
The Double Prior Selection For The Parameter Of Exponential Life Time Model Under Type Ii Censoring, Ronak M. Patel, Achyut C. Patel
Journal of Modern Applied Statistical Methods
A comparison of double informative priors assumed for the parameter of exponential life time model is considered. Three different sets of double priors are included, and the results are compared with a forth single prior. The data is Type II censored and Bayes estimators for the parameter and reliability are carried out under a squared error loss function in the cases of the four different sets of prior distributions. The predictive distribution was derived for future failure time and also for the remaining ordered failure times after the first r failure times have been observed. Corresponding Bayes credible equal tail …
Jmasm43: Teereg: Trimmed Elemental Estimation (R), Wei Jiang, Matthew S. Mayo
Jmasm43: Teereg: Trimmed Elemental Estimation (R), Wei Jiang, Matthew S. Mayo
Journal of Modern Applied Statistical Methods
Trimmed elemental regression is robust to outliers and violations of model assumptions. Its properties and statistical inference were evaluated using bias-corrected and accelerated bootstrap confidence intervals. An R package named TEEReg is developed to compute the trimmed elemental estimates and the corresponding bootstrap confidence intervals. Two examples are provided to demonstrate its usage.
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Outlier Impact And Accommodation On Power, Hongjing Liao, Yanju Li, Gordon P. Brooks
Journal of Modern Applied Statistical Methods
The outliers’ influence on power rates in ANOVA and Welch tests at various conditions was examined and compared with the effectiveness of nonparametric methods and Winsorizing in minimizing the impact of outliers. Results showed that, considering both power and Type I error, a nonparametric test is the safest choice to control the inflation of Type I error with a decent sample size and yield relatively high power.
Book Review: Multivariate Statistical Methods, A Primer, C. R. Rao
Book Review: Multivariate Statistical Methods, A Primer, C. R. Rao
Journal of Modern Applied Statistical Methods
Multivariate Statistical Methods, A Primer, 4th Ed. Bryan F. J. Manly and Jorge A. Navarro Alberto. NY: Chapman & Hall / CRC Press. 2016. 264 p. ISBN 10: 1498728960 / ISBN 13: 978-1498728966
Jmasm44: Implementing Multiple Ratio Imputation By The Emb Algorithm (R), Masayoshi Takahashi
Jmasm44: Implementing Multiple Ratio Imputation By The Emb Algorithm (R), Masayoshi Takahashi
Journal of Modern Applied Statistical Methods
Although single ratio imputation is often used to deal with missing values in practice, there is a paucity of discussion regarding multiple ratio imputation. Code in the R statistical environment is presented to execute multiple ratio imputation by the Expectation-Maximization with Bootstrapping (EMB) algorithm.
A Review Of The Multiple-Sample Tests For The Continuous-Data Type, Dewi Rahardja
A Review Of The Multiple-Sample Tests For The Continuous-Data Type, Dewi Rahardja
Journal of Modern Applied Statistical Methods
For continuous data, various statistical hypotheses testing methods have been extensively discussed in the literature. In this article a review is provided of the multiple-sample continuous-data testing methods. It includes traditional methods, such as the two-sample t-test, Welch ANOVA test, etc., as well as newly-developed ones, such as the various Multiple Comparison Procedure (MCP). A roadmap is provided in a figure or diagram format as to which methods are available in the literature. Additionally, the implementation of these methods in popular statistical software packages such as SAS is also presented. This review will be helpful to determine which continuous-data testing …
Factor Analysis By Limited Scales: Which Factors To Analyze?, Stan Lipovetsky
Factor Analysis By Limited Scales: Which Factors To Analyze?, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
Factor Analysis (FA) and Principal Component Analysis (PCA) are well-known main tools of the multivariate statistics for data analysis, reduction, and visualization. Commonly, the analysis and interpretation of their solutions is performed for each of several main eigenvectors with variances explaining a big part of the total variability in data. The recommendation is to determine if all the main vectors are really needed in the analysis, or some of them should be skipped if they correspond to the absence of the analyzing features. A simple criterion for identifying redundant vectors of loadings is their negative correlation with the vector of …
Prediction Of Percent Change In Linear Regression By Correlated Variables, Stan Lipovetsky
Prediction Of Percent Change In Linear Regression By Correlated Variables, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
Multiple linear regression can be applied for predicting an individual value of dependent variable y by the given values of independent variables x. But it is not immediately clear how to estimate percent change in y due to changes in predictors, especially when those are correlated. This work considers several approaches to this problem, including its formulation via predictors adjusted by their correlation structure. Ordinary least squares regression is used, together with Shapley value regression and another model based on solving some system of differential equations. Numerical estimations performed for a real marketing research data demonstrate meaningful results. The considered …
The In Vivo Effect Of Oil Palm Phenolics (Opp) In Atherogenic Diet Induced Rats Model Of Alzheimer’S Disease (Ad), Yan Wu
Wayne State University Dissertations
Alzheimer’s disease (AD) is the most common cause of dementia in the aging population. It is characterized by cognitive decline and deposition of ß-amyloid plaques in the hippocampus. It has been shown that hypercholesterolemia induced by high cholesterol diet is associated with AD development. Increased level of oxidative stress has also been observed in AD patients. An important strategy to treat or delay the impairment is based on dietary modification, using food supplements. OPP, a water soluble fraction from oil palm fruit, rich in phenolics has been found to possess significant antioxidant activities. Its beneficial effects on cardiovascular diseases, diabetes …
Stochastic Processes And Integrals, Jose L. Menaldi
Stochastic Processes And Integrals, Jose L. Menaldi
Mathematics Faculty Research Publications
Stochastic integrals with respect to Wiener process and Poisson measures are discusses, beginning from stochastic processes.
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Wayne State University Theses
The identification of pathways that are involved in a particular phenotype helps us understand the underlying biological processes. Traditional pathway analysis techniques aim to infer the impact on individual pathways using only mRNA levels. However, recent studies showed that gene expression alone is unable to capture the whole picture of biological phenomena. At the same time, MicroRNAs (miRNAs) are newly discovered gene regulators that have shown to play an important role in diagnosis, and prognosis for different types of diseases. Current pathway analysis techniques do not take miRNAs into consideration. In this project, we investigate the effect of integrating miRNA …
An Empirical Demonstration Of The Need For Exact Tests, Vance W. Berger
An Empirical Demonstration Of The Need For Exact Tests, Vance W. Berger
Journal of Modern Applied Statistical Methods
The robustness of parametric analyses is rarely questioned or qualified. Robustness, generally understood, means the exact and approximate p-values will lie on the same side of alpha for any reasonable data set; and 1) any data set would qualify as reasonable and 2) robustness holds universally, for all alpha levels and approximations. For this to be true, the approximation would need to be perfect all of the time. Any discrepancy between the approximation and the exact p-value, for any combination of alpha level and data set, would constitute a violation. Clearly, this is not true, and when confronted with this …
Robustness And Power Of The Student T, Welch-Aspin, Yuen, Tukey Quick, And Haga Tests, Dong Li
Robustness And Power Of The Student T, Welch-Aspin, Yuen, Tukey Quick, And Haga Tests, Dong Li
Wayne State University Dissertations
Classical parametric statistic procedures are widely used in the research community. However, for classical tests to produce accurate results, the assumptions underlying them must be sufficiently satisfied. When the assumptions are not met, the results of the analysis may be due to the violation of the assumptions, instead of the true pattern of the data. The assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions may lead to substantive errors in the interpretation of data. As an alternative to normal theory statistics, nonparametric statistical procedures do not make assumptions about the underlying …
Longitudinal Stability Of Effect Sizes In Education Research, Joshua Stephens
Longitudinal Stability Of Effect Sizes In Education Research, Joshua Stephens
Journal of Modern Applied Statistical Methods
Educators use meta-analyses to decide best practices. It has been suggested that effect sizes have declined over time due to various biases. This study applies an established methodological framework to educational meta-analyses and finds that effect sizes have increased from 1970–present. Potential causes for this phenomenon are discussed.