'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations,
2017
Université Laval, Quebec City
'Parallel Universe' Or 'Proven Future'? The Language Of Dependent Means T-Test Interpretations, Anthony M. Gould, Jean-Etienne Joullié
Journal of Modern Applied Statistical Methods
Of the three kinds of two-mean comparisons which judge a test statistic against a critical value taken from a Student t-distribution, one – the repeated measures or dependent-means application – is distinctive because it is meant to assess the value of a parameter which is not part of the natural order. This absence forces a choice between two interpretations of a significant test result and the meaning of the test hypothesis. The parallel universe view advances a conditional, backward-looking conclusion. The more practical proven future interpretation is a non-conditional proposition about what will happen if an intervention is (now) applied …
Unit Root Test For Panel Data Ar(1) Time Series Model With Linear Time Trend And Augmentation Term: A Bayesian Approach,
2017
Central University of Rajasthan, India
Unit Root Test For Panel Data Ar(1) Time Series Model With Linear Time Trend And Augmentation Term: A Bayesian Approach, Jitendra Kumar, Anoop Chaturvedi, Umme Afifa, Shafat Yousuf, Saurabh Kumar
Journal of Modern Applied Statistical Methods
The univariate time series models, in the case of unit root hypothesis, are more biased towards the acceptance of the Unit Root Hypothesis especially in a short time span. However, the panel data time series model is more appropriate in such situation. The Bayesian analysis of unit root testing for a panel data time series model is considered. An autoregressive panel data AR(1) model with linear time trend and augmentation term has been considered and derived the posterior odds ratio for testing the presence of unit root hypothesis under appropriate prior assumptions. A simulation study and real data analysis are …
End Matter,
2017
Wayne State University
Characterizations Of Distributions By Expected Values Of Lower Record Statistics With Spacing,
2017
Aligarh Muslim University, Aligarh, India
Characterizations Of Distributions By Expected Values Of Lower Record Statistics With Spacing, M. Faizan, Ziaul Haque, M. A. Ansari
Journal of Modern Applied Statistical Methods
The characterizations of a certain class of probability distributions are established through conditional expectation of lower record values when the conditioned record value may not be the adjacent one. Some of its important deductions are also discussed.
Using Pratt's Importance Measures In Confirmatory Factor Analyses,
2017
University of British Columbia
Using Pratt's Importance Measures In Confirmatory Factor Analyses, Amrey D. Wu, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
When running a confirmatory factor analysis (CFA), users specify and interpret the pattern (loading) matrix. It has been recommended that the structure coefficients, indicating the factors’ correlation with the observed indicators, should also be reported when the factors are correlated (Graham, Guthrie, & Thompson, 2003; Thompson, 1997). The aims of this article are: (1) to note the structure coefficient should be interpreted with caution if the factors are specified to correlate. Because the structure coefficient is a zero-order correlation, it may be partially or entirely a reflection of factor correlations. This is elucidated by the matrix algebra of the structure …
A Double Ewma Control Chart For The Individuals Based On A Linear Prediction,
2017
University of Northern California
A Double Ewma Control Chart For The Individuals Based On A Linear Prediction, Rafael Perez Abreu, Jay R. Schaffer
Journal of Modern Applied Statistical Methods
Industrial process use single and double Exponential Weighted Moving Average control charts to detect small shifts in it. Occasionally there is a need to detect small trends instead of shifts, but the effectiveness to detect small trends. A new control chart is proposed to detect a small drift.
Study Evaluating The Alterations Caused In An Exploratory Factor Analysis When Multivariate Normal Data Is Dichotomized,
2017
Federal University of Paraná, Curitiba, Brazil
Study Evaluating The Alterations Caused In An Exploratory Factor Analysis When Multivariate Normal Data Is Dichotomized, Rosilei S. Novak, Jair M. Marques
Journal of Modern Applied Statistical Methods
The relationships resulting from the dichotomization of multivariate normal data is a question that causes concern when using exploratory factor analysis. The relationships in an exploratory factor analysis are examined when multivariate normal data, generated by Monte Carlo methods, is dichotomized.
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations,
2017
University of Alberta, Edmonton
Power And Sample Size Estimation For Nonparametric Composite Endpoints: Practical Implementation Using Data Simulations, Paul M. Brown, Justin A. Ezekowitz
Journal of Modern Applied Statistical Methods
Composite endpoints are a popular outcome in controlled studies. However, the required sample size is not easily obtained due to the assortment of outcomes, correlations between them and the way in which the composite is constructed. Data simulations are required. A macro is developed that enables sample size and power estimation.
The Regression Smoother Lowess: A Confidence Band That Allows Heteroscedasticity And Has Some Specified Simultaneous Probability Coverage,
2017
University of Southern California
The Regression Smoother Lowess: A Confidence Band That Allows Heteroscedasticity And Has Some Specified Simultaneous Probability Coverage, Rand Wilcox
Journal of Modern Applied Statistical Methods
Many nonparametric regression estimators (smoothers) have been proposed that provide a more flexible method for estimating the true regression line compared to using some of the more obvious parametric models. A basic goal when using any smoother is computing a confidence band for the true regression line. Let M(Y|X) be some conditional measure of location associated with the random variable Y, given X and let x be some specific value of the covariate. When using the LOWESS estimator, an extant method that assumes homoscedasticity can be used to compute a confidence interval for M(Y|X = x). A trivial way of …
Robust Measures Of Variable Importance For Multivariate Group Designs,
2017
University of Calgary, AB, Canada
Robust Measures Of Variable Importance For Multivariate Group Designs, Tolulope T. Sajobi, Lisa M. Lix
Journal of Modern Applied Statistical Methods
Variable importance measures based on discriminant analysis and multivariate analysis of variance are useful for identifying variables that discriminate between two groups in multivariate group designs. Variable importance measures are developed based on trimmed and Winsorized estimators for describing group differences in multivariate non-normal populations.
Effectively Comparing Differences In Proportions,
2017
McNeese State University
Effectively Comparing Differences In Proportions, Lonnie Turpin Jr.
Journal of Modern Applied Statistical Methods
A single framework of developing and implementing tests about proportions is outlined. It avoids some of the pitfalls of methods commonly put forward in an introductory data analysis course.
Missing Data In Longitudinal Surveys: A Comparison Of Performance Of Modern Techniques,
2017
University College London
Missing Data In Longitudinal Surveys: A Comparison Of Performance Of Modern Techniques, Paola Zaninotto, Amanda Sacker
Journal of Modern Applied Statistical Methods
Using a simulation study, the performance of complete case analysis, full information maximum likelihood, multivariate normal imputation, multiple imputation by chained equations and two-fold fully conditional specification to handle missing data were compared in longitudinal surveys with continuous and binary outcomes, missing covariates, and an interaction term.
Around Gamma Lindley Distribution,
2017
Badji-Mokhtar University, Annaba, Algeria
Around Gamma Lindley Distribution, Hamouda Messaadia, Halim Zeghdoudi
Journal of Modern Applied Statistical Methods
Some remarks and correction on a new distribution, Gamma Lindley, of which the Lindley distribution is a particular case, are given pertaining to its parameter space.
The Impact Of Inappropriate Modeling Of Cross-Classified Data Structures On Random-Slope Models,
2017
University of Pittsburgh
The Impact Of Inappropriate Modeling Of Cross-Classified Data Structures On Random-Slope Models, Feifei Ye, Laura Daniel
Journal of Modern Applied Statistical Methods
Previous studies that explored the impact of misspecification of cross-classified data structure as strictly hierarchical are limited to random intercept models. This study examined the effects of misspecification of a two-level, cross-classified, random effect model (CCREM) where both the level-1 intercept and slope were allowed to vary randomly. Results suggest that ignoring one of the crossed factors produced considerably underestimated standard errors for: 1) the regression coefficients of the level-1 predictor; 2) the inappropriately modeled predictor associated with the misspecified crossed factor; and 3) and their interaction. This misspecification also resulted in a significant inflation of the level-1 residual variances …
Experimental Design And Data Analysis In Computer Simulation Studies In The Behavioral Sciences,
2017
University of Minnesota - Twin Cities
Experimental Design And Data Analysis In Computer Simulation Studies In The Behavioral Sciences, Michael Harwell, Nidhi Kohli, Yadira Peralta
Journal of Modern Applied Statistical Methods
Treating computer simulation studies as statistical sampling experiments subject to established principles of experimental design and data analysis should further enhance their ability to inform statistical practice and a program of statistical research. Latin hypercube designs to enhance generalizability and meta-analytic methods to analyze simulation results are presented.
On Variance Balanced Designs,
2017
Saurashtra University Rajkot, Gujarat, India
On Variance Balanced Designs, Dilip Kumar Ghosh, Sangeeta Ahuja
Journal of Modern Applied Statistical Methods
Balanced incomplete block designs are not always possible to construct because of their parametric relations. In such a situation another balanced design, the variance balanced design, is required. This construction of binary, equal replicated variance balanced designs are discussed using the half fraction of the 2n factorial designs with smaller block sizes. This method was also extended to construct another variance balanced design by deleting the last block of the resulting variance balanced designs. Its efficiency factor compared with randomized block designs was compared and found to be highly efficient.
Performance Evaluation Of Confidence Intervals For Ordinal Coefficient Alpha,
2017
Dallas Independent School District
Performance Evaluation Of Confidence Intervals For Ordinal Coefficient Alpha, Heather J. Turner, Prathiba Natesan, Robin K. Henson
Journal of Modern Applied Statistical Methods
The aim of this study was to investigate the performance of the Fisher, Feldt, Bonner, and Hakstian and Whalen (HW) confidence intervals methods for the non-parametric reliability estimate, ordinal alpha. All methods yielded unacceptably low coverage rates and potentially increased Type-I error rates.
A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient,
2017
University of Tabuk, Saudi Arabia
A Monte Carlo Study Of The Effects Of Variability And Outliers On The Linear Correlation Coefficient, Hussein Yousif Eledum
Journal of Modern Applied Statistical Methods
Monte Carlo simulations are used to investigate the effect of two factors, the amount of variability and an outlier, on the size of the Pearson correlation coefficient. Some simulation algorithms are developed, and two theorems for increasing or decreasing the amount of variability are suggested.
Parameter Estimation In Weighted Rayleigh Distribution,
2017
Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
Parameter Estimation In Weighted Rayleigh Distribution, M. Ajami, S. M. A. Jahanshahi
Journal of Modern Applied Statistical Methods
A weighted model based on the Rayleigh distribution is proposed and the statistical and reliability properties of this model are presented. Some non-Bayesian and Bayesian methods are used to estimate the β parameter of proposed model. The Bayes estimators are obtained under the symmetric (squared error) and the asymmetric (linear exponential) loss functions using non-informative and reciprocal gamma priors. The performance of the estimators is assessed on the basis of their biases and relative risks under the two above-mentioned loss functions. A simulation study is constructed to evaluate the ability of considered estimation methods. The suitability of the proposed model …
Jmasm 46: Algorithm For Comparison Of Robust Regression Methods In Multiple Linear Regression By Weighting Least Square Regression (Sas),
2017
Universiti Sains Malaysia, Kelantan, Malaysia
Jmasm 46: Algorithm For Comparison Of Robust Regression Methods In Multiple Linear Regression By Weighting Least Square Regression (Sas), Mohamad Shafiq, Wan Muhamad Amir, Nur Syabiha Zafakali
Journal of Modern Applied Statistical Methods
The aim of this study is to compare different robust regression methods in three main models of multiple linear regression and weighting multiple linear regression. An algorithm for weighting multiple linear regression by standard deviation and variance for combining different robust method is given in SAS along with an application.
