Statistics and Probability Commons^™

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, 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, 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, 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, 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, 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, Rand R. Wilcox

Journal of Modern Applied Statistical Methods

Consider three random variables, Y , X₁ and X₂, having some unknown trivariate distribution and let n²_j (j = 1, 2) be some measure of the strength of association between Y and X_j. When n²_j is taken to be Pearson’s correlation numerous methods for testing H_o : 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, 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 10000_HEX to 1FFFF_HEX, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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 RMSSE_R 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, 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, 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 …

Using Finite Mixture Modeling To Deal With Systematic Measurement Error: A Case Study, Min Liu, Gregory R. Hancock, Jeffrey R. Harring

Journal of Modern Applied Statistical Methods

Conventional methods and analyses view measurement error as random. A scenario is presented where a variable was measured with systematic error. Mixture models with systematic parameter constraints were used to test hypotheses in the context of general linear models; this accommodated the heterogeneity arising due to systematic measurement error.

Estimating Internal Consistency Using Bayesian Methods, Miguel A. Padilla, Guili Zhang

Journal of Modern Applied Statistical Methods

Bayesian internal consistency and its Bayesian credible interval (BCI) are developed and Bayesian internal consistency and its percentile and normal theory based BCIs were investigated in a simulation study. Results indicate that the Bayesian internal consistency is relatively unbiased under all investigated conditions and the percentile based BCIs yielded better coverage performance.

Factors That Inhibit Or Enhance Maternal Coping With Stillbirth In Chhattisgarh, India, Lisa R. Roberts

Loma Linda University Electronic Theses, Dissertations & Projects

Background: Over half of the known stillbirths occur in four highly populated countries—India among them. While acknowledged as a significant public health issue in western societies, little is known about maternal coping with stillbirth in developing countries. The purpose of this mixed methods study is to explore how issues of gender and power, social support, coping efforts, and religious beliefs influence perinatal grief outcomes among poor women in rural Chhattisgarh, India.

Methods: In Phase 1 of this mixed methods study, grounded theory methods were used to explore perceptions regarding stillbirth. A de-identified medical records review of 536 deliveries at Christian …

Examining Child Sexual Abuse And Future Parenting: An Application Of Latent Class Modeling, Kimberly W. D'Zatko

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This study was designed to empirically derive latent classes of mothers who were sexually abused during childhood and to assess the association between depression, alcohol/drug use, supportive intimate partner, and specific classes.

One hundred six women between the ages of 20 and 44 years (M = 27) who reported having been sexually abused during childhood (CSA) and 158 non-CSA mothers between the ages of 20 and 43 years (M = 23) were interviewed and assessed along six parenting dimensions. Logistic regression models evaluated the association between psychoemotional variables and specific classes.

The final model consisted of three classes—53.2%, …

Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 4: Predictive Analysis - Regression Modeling, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.

Risk Auto Theft: Predicting Spatial Distributions Of Crime Events, Tana J. Gurule, Tamara D. Madensen

Graduate Research Symposium (2010 - 2017)

Police typically rely on retrospective hotspot maps to informe prevention strategies aimed at reducing future crime. The current study reviews environmental crime theories that help to identify casual factors associated with rish of auto theft. Map layers are created from data that operationalize these risk factors. These layers are combined using spatial analysis techniques to produce a "risk density" map. Analysis of crime data are used to determing wheter our "risk density" map better predicts subsequetnt theft events than a traditional retrospective hotspot map.

Putting Artists On The Map: A Five Part Study Of Greater Cleveland Artists' Location Decisions - Part 3: Attitudinal Analysis - Artist Housing And Space Survey, Mark Salling, Gregory Soltis, Charles Post, Sharon Bliss, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

A series of reports detailing the residential and work space location preferences of Cuyahoga county's artists.

Analysis Of Morris Water Maze Data With Bayesian Statistical Methods, Maxym V. Myroshnychenko, Anton Westveld, Jefferson Kinney

Festival of Communities: UG Symposium (Posters)

Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to …