Factors Influencing The Mixture Index Of Model Fit In Contingency Tables Showing Independence,
2011
IBM Global Business Services
Factors Influencing The Mixture Index Of Model Fit In Contingency Tables Showing Independence, Xuemei Pan, C. Mitchell Dayton
Journal of Modern Applied Statistical Methods
Several competing computational techniques for dealing with sampling zeros were evaluated when estimating the two-point mixture model index, π* , in contingency tables under an independence assumption. Also, the performance of the estimate and associated standard errors were studied under various combinations of conditions.
Bayesian Regression Analysis With Examples In S-Plus And R,
2011
University of Kashmir, Srinagar, India
Bayesian Regression Analysis With Examples In S-Plus And R, Sheikh P. Ahmad, A. A. Khan, A. Ahmed
Journal of Modern Applied Statistical Methods
An extended version of normal theory Bayesian regression models, including extreme-value, logistic and normal regression models is examined. Methods proposed are illustrated numerically; the regression coefficient of pH on electrical conductivity (EC) of soil data is analyzed using both S-PLUS and R software.
Bayesian Threshold Moving Average Models,
2011
Jordan University of Science and Technology
Bayesian Threshold Moving Average Models, Mahmoud M. Smadi, M. T. Alodat
Journal of Modern Applied Statistical Methods
A Bayesian approach in threshold moving average model for time series with two regimes is provided. The posterior distribution of the delay and threshold parameters are used to examine and investigate the intrinsic characteristics of this nonlinear time series model. The proposed approach is applied to both simulated data and a real data set obtained from a chemical system. Key words: Threshold time series, moving average model, Bayesian
A Robust One-Sided Variability Control Chart,
2011
University of North Florida
A Robust One-Sided Variability Control Chart, P. Borysov, Ping Sa
Journal of Modern Applied Statistical Methods
A new control charting technique to monitor the variability of any distribution is proposed. The simulation study shows that the new method outperforms all the existing methods in controlling the Type I error rates and it also has good power performance for all distributions considered in the study.
Empirical Methods For Predicting Student Retention- A Summary From The Literature,
2011
Western Kentucky University
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Economics Faculty Publications
The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella, 1983; Pascarell and Ternzini, 1978; St. John and Cabrera, 2000; Tinto, 1975) This body of research provides a starting point for the consideration of which explanatory variables to include in any model specification, as well as identifying possible data sources. The literature separates itself into two major camps including research related to the hypothesis testing …
Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning,
2011
Western Kentucky University
Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning, Matt Bogard
Economics Faculty Publications
This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.
A Test That Combines Frequency And Quantitative Information,
2011
University of Southern California
A Test That Combines Frequency And Quantitative Information, Norman Cliff
Journal of Modern Applied Statistical Methods
In many simple designs, observed frequencies in subclasses defined by a qualitative variable are compared to the frequencies expected on the basis of population proportions, design parameters or models. Often there is a quantitative variable which may be affected in the same way as the frequencies. Its differences among the groups may also be analyzed. A simple test is described that combines the effects on the frequencies and on the quantitative variable based on comparing the sums of the values for the quantitative value within each group to the random expectation. The sampling variance of the difference is derived and …
Comparing The Strength Of Association Of Two Predictors Via Smoothers Or Robust Regression Estimators,
2011
University of Southern California
Comparing The Strength Of Association Of Two Predictors Via Smoothers Or Robust Regression Estimators, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Consider three random variables, Y , X1 and X2, having some unknown trivariate distribution and let n2j (j = 1, 2) be some measure of the strength of association between Y and Xj. When n2j is taken to be Pearson’s correlation numerous methods for testing Ho : n21 = n22 have been proposed. However, Pearson’s correlation is not robust and the methods for testing H0 are not level robust in general. This article examines methods for testing H0 based on a robust fit. The …
Bias In Monte Carlo Simulations Due To Pseudo-Random Number Generator Initial Seed Selection,
2011
Beaumont Health System
Bias In Monte Carlo Simulations Due To Pseudo-Random Number Generator Initial Seed Selection, Jack C. Hill, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
Pseudo-random number generators can bias Monte Carlo simulations of the standard normal probability distribution function with initial seeds selection. Five generator designs were initial-seeded with values from 10000HEX to 1FFFFHEX, estimates of the mean were calculated for each seed, the distribution of mean estimates was determined for each generator and simulation histories were graphed for selected seeds.
New Perspectives In Applying The Regression-Discontinuity Design For Program Evaluation: A Simulation Analysis,
2011
Central Connecticut State University
New Perspectives In Applying The Regression-Discontinuity Design For Program Evaluation: A Simulation Analysis, Sally A. Lesik
Journal of Modern Applied Statistical Methods
Evaluating educational programs is a core component of assessment. One challenge occurs because participants often enter into programs with diverse skills and backgrounds. The regression-discontinuity design has been used to evaluate programs amongst a diverse group, but noncompliance is a limitation. A simulation analysis illustrates the impact of noncompliance.
Information Technology For Increasing Qualitative Information Processing Efficiency,
2011
Vladivostok State University of Economics and Services, Vladivostok, Russia
Information Technology For Increasing Qualitative Information Processing Efficiency, S. N. Martyshenko, E. A. Egorov
Journal of Modern Applied Statistical Methods
The problem of qualitative information processing in questionnaires is considered and a solution for this problem is offered. The computer technology developed by the authors to automate the offered decision is described.
General Piecewise Growth Mixture Model: Word Recognition Development For Different Learners In Different Phases,
2011
University of British Columbia
General Piecewise Growth Mixture Model: Word Recognition Development For Different Learners In Different Phases, Amery D. Wu, Bruno D. Zumbo, Linda S. Siegel
Journal of Modern Applied Statistical Methods
The General Piecewise Growth Mixture Model (GPGMM), without losing generality to other fields of study, can answer six crucial research questions regarding children’s word recognition development. Using child word recognition data as an example, this study demonstrates the flexibility and versatility of the GPGMM in investigating growth trajectories that are potentially phasic and heterogeneous. The strengths and limitations of the GPGMM and lessons learned from this hands-on experience are discussed.
A Simulation Study Of The Relative Efficiency Of The Minimized Integrated Square Error Estimator (L2e) For Phase I Control Charting,
2011
Georgia Southern University
A Simulation Study Of The Relative Efficiency Of The Minimized Integrated Square Error Estimator (L2e) For Phase I Control Charting, John N. Dyer
Journal of Modern Applied Statistical Methods
Parameter estimates used in control charting, the sample mean and variance, are based on maximum likelihood estimation (MLE). Unfortunately, MLEs are not robust to contaminated data and can lead to improper conclusions regarding parameter values. This article proposes a more robust estimation technique; the minimized integrated square error estimator (L2E).
Maximum Likelihood Solution For The Linear Structural Relationship With Three Parameters Known,
2011
Middlesex University Business School
Maximum Likelihood Solution For The Linear Structural Relationship With Three Parameters Known, Androulla Michaeloudis
Journal of Modern Applied Statistical Methods
A maximum likelihood solution is obtained for the simple linear structural relation model where the underlying incidental distribution and one error variance are assumed known. Expressions for the asymptotic standard errors of the maximum likelihood estimates are obtained and these are verified using a simulation study.
Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data,
2011
Ministry of Finance, Dhaka, Bangladesh
Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data, Narayan Chanra Sinha, M. Ataharul Islam, Kazi Saleh Ahamed
Journal of Modern Applied Statistical Methods
Logistic regression models for transition probabilities of higher order Markov models are developed for the sequence of chain dependent repeated observations. To identify the significance of these models and their parameters a test procedure for a likelihood ratio criterion is developed. A method of model selection is suggested on the basis of AIC and BIC procedures. The proposed models and test procedures are applied to analyze the occurrences of daily rainfall data for selected stations in Bangladesh. Based on results from these models, the transition probabilities of first order Markov model for temperature and humidity provided the most suitable option …
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies,
2011
New Mexico State University
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies, Daniel J. Mundform, Jay Schaffer, Myoung-Jin Kim, Dale Shaw, Ampai Thongteeraparp, Pornsin Supawan
Journal of Modern Applied Statistical Methods
Monte Carlo simulations are used extensively to study the performance of statistical tests and control charts. Researchers have used various numbers of replications, but rarely provide justification for their choice. Currently, no empirically-based recommendations regarding the required number of replications exist. Twenty-two studies were re-analyzed to determine empirically-based recommendations.
Matched-Pair Studies With Misclassified Ordinal Data,
2011
Western Illinois University
Matched-Pair Studies With Misclassified Ordinal Data, Tze-San Lee
Journal of Modern Applied Statistical Methods
The problem of matched-pair studies with misclassified ordinal data is considered. Misclassification is assumed to occur only between the adjacent columns/rows. Bias-adjusted generalized odds ratio and a test for marginal homogeneity are presented to account for misclassification bias. Data from lambing records of 227 Merino ewes are used to illustrate how to calculate these bias-adjusted estimators and – because validation data are not available – a sensitivity analysis is conducted.
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova,
2011
East Carolina University
A Robust Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
A robust Root Mean Square Standardized Effect Size (RMSSER) was developed to address the unsatisfactory performance of the Root Mean Square Standardized Effect Size. The coverage performances of the confidence intervals (CI) for RMSSER were investigated. The coverage probabilities of the non-central F distribution-based CI for RMSSER were adequate.
The Overall F-Tests For Seasonal Unit Roots Under Nonstationary Alternatives: Some Theoretical Results And A Monte Carlo Investigation,
2011
Manouba University, École Superieure de Commerce de Tunis, Tunisia
The Overall F-Tests For Seasonal Unit Roots Under Nonstationary Alternatives: Some Theoretical Results And A Monte Carlo Investigation, Ghassen El Montasser
Journal of Modern Applied Statistical Methods
In many empirical studies concerning seasonal time series, it has been shown that the whole set of unit roots associated with seasonal random walks are not present. This article focuses on the overall F-tests for seasonal unit roots under some nonstationary alternatives different from the seasonal random walk. The asymptotic theory of these tests is established for these cases using a new approach based on circulant matrix concepts. The simulation results joined to this theoretic analysis showed that the overall F-tests, as well as their augmented versions, maintained high power against the nonstationary alternatives.
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method,
2011
University of Guelph
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method, Sara Mann, James Chowhan
Journal of Modern Applied Statistical Methods
Using the Canadian Workplace and Employee Survey (WES), three variance estimation methods for weighting large datasets with complex sampling designs are compared: simple final weighting, standard bootstrapping and mean bootstrapping. Using a logit analysis, it is shown - depending on which weighting method is used - different predictor variables are significant. The potential lack of independence inherent in a multi-stage cluster sample design, as in the WES, results in a downward bias in the variance when conducting statistical inference (using the simple final weight), which in turn results in increased Type I errors. Bootstrap methods can account for the survey’s …
