Applied Statistics Commons^™

Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane

Journal of Modern Applied Statistical Methods

An SPSS script previously presented in this journal contained nontrivial flaws. The script should not be used as written. A call is renewed for validation of new software.

An Empirical Comparison Between Robust Estimation And Robust Optimization To Mean-Variance Portfolio, Epha Diana Supandi, Dedi Rosadi, Abdurakhman

Journal of Modern Applied Statistical Methods

Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the out-of-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and …

Guidelines For Generating Right-Censored Outcomes From A Cox Model Extended To Accommodate Time-Varying Covariates, Maria E. Montez-Rath, Kristopher Kapphahn, Maya B. Mathur, Aya A. Mitani, David J. Hendry, Manisha Desai

Journal of Modern Applied Statistical Methods

Simulating studies with right-censored outcomes as functions of time-varying covariates is discussed. Guidelines on the use of an algorithm developed by Zhou and implemented by Hendry are provided. Through simulation studies, the sensitivity of the method to user inputs is considered.

A Reinterpretation And Extension Of Mcnemar’S Test, Chauncey M. Dayton

Journal of Modern Applied Statistical Methods

The McNemar test is extended to multiple groups based on a latent class model incorporating classes representing consistent responders and a single latent error rate. The method is illustrated with data from a CDC survey of immunizations for flu and pneumonia for which a part-heterogeneous model is selected for interpretation.

In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker

Journal of Modern Applied Statistical Methods

A rebuttal to Frane's letter to the Editor in this issue.

Experiment-Wise Type I Error Rates In Nested (Hierarchical) Study Designs, Jack Sawilowsky, Barry Markman

Journal of Modern Applied Statistical Methods

When conducting a statistical test one of the initial risks that must be considered is a Type I error, also known as a false positive. The Type I error rate is set by nominal alpha, assuming all underlying conditions of the statistic are met. Experiment-wise Type I error inflation occurs when multiple tests are conducted overall for a single experiment. There is a growing trend in the social and behavioral sciences utilizing nested designs. A Monte Carlo study was conducted using a two-layer design. Five theoretical distributions and four real datasets taken from Micceri (1989) were used, each with five …

Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar

Journal of Modern Applied Statistical Methods

Traditionally, quality control methodology is based on the assumption that serially-generated data are independent and normally distributed. On the basis of these assumptions the operating characteristic (OC) function of the control chart is derived after setting the control limits. But in practice, many of the basic industrial variables do not satisfy both the assumptions and hence one may doubt the validity of the inferences drawn from the control charts. In this paper the power of the control chart for the mean is examined when both the assumptions of independence and normality are not tenable. The OC function is calculated and …

Multivariate Rank Outlyingness And Correlation Effects, Olusola Samuel Makinde

Journal of Modern Applied Statistical Methods

The effect of correlation on multivariate rank outlyingness, a result of deviation of multivariate rank functions from property of spherical symmetry, is examined. Possible affine invariant versions of this multivariate rank are surveyed, and outlyingness of affine invariant and non-invariant spatial rank functions under general affine transformation are compared.

A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding

Journal of Modern Applied Statistical Methods

The performance of several models under different conditions of zero-inflation and dispersion are evaluated. Results from simulated and real data showed that the zero-altered or zero-inflated negative binomial model were preferred over others (e.g., ordinary least-squares regression with log-transformed outcome, Poisson model) when data have excessive zeros and over-dispersion.

Test Statistics For The Comparison Of Means For Two Samples That Include Both Paired And Independent Observations, Ben Derrick, Bethan Russ, Deirdre Toher, Paul White

Journal of Modern Applied Statistical Methods

Standard approaches for analyzing the difference in two means, where partially overlapping samples are present, are less than desirable. Here are introduced two test statistics, making reference to the t-distribution. It is shown that these test statistics are Type I error robust, and more powerful than standard tests.

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

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

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

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

Journal of Modern Applied Statistical Methods

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Comparison Of Survival Curves Between Cox Proportional Hazards, Random Forests, And Conditional Inference Forests In Survival Analysis, Brandon Weathers

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Survival analysis methods are a mainstay of the biomedical fields but are finding increasing use in other disciplines including finance and engineering. A widely used tool in survival analysis is the Cox proportional hazards regression model. For this model, all the predicted survivor curves have the same basic shape, which may not be a good approximation to reality. In contrast the Random Survival Forests does not make the proportional hazards assumption and has the flexibility to model survivor curves that are of quite different shapes for different groups of subjects. We applied both techniques to a number of publicly available …

Statistical Methods For Assessing Individual Oocyte Viability Through Gene Expression Profiles, Michael O. Bishop

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Abstract

Statistical Methods for Assessing Individual Oocyte Viability Through Gene Expression Profiles

By

Michael O. Bishop

Utah State University, 2017

Major Professor: Dr. John R. Stevens

Department: Mathematics and Statistics

Oocytes are the precursor cells to the female gamete, or egg. While reproduction may vary from species to species, within humans and most domesticated animals, the oocyte maturation process is fairly similar. As an oocyte matures, there are various processes that take place, all of which have an effect on the viability of the individual oocyte. Barring outside damage that may come to the oocyte, one of the primary reasons …

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

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

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 …