Statistical Theory Commons^™

Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan

Journal of Modern Applied Statistical Methods

The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.

Method Of Estimation In The Presence Of Non-Response And Measurement Errors Simultaneously, Rajesh Singh Singh, Prayas Sharma

Journal of Modern Applied Statistical Methods

The problem of estimating the finite population mean of in simple random sampling in the presence of non-response and response error was considered. The estimators use auxiliary information to improve efficiency, assuming non–response and measurement error are present in both the study and auxiliary variables. A class of estimators was proposed and its properties studied in the simultaneous presence of non-response and response errors. It was shown that the proposed class of estimators is more efficient than the usual unbiased estimator, ratio and product estimators under non-response and response error together. A numerical study was carried out to compare its …

Pseudo-Random Number Generators For Vector Processors And Multicore Processors, Agner Fog

Journal of Modern Applied Statistical Methods

Large scale Monte Carlo applications need a good pseudo-random number generator capable of utilizing both the vector processing capabilities and multiprocessing capabilities of modern computers in order to get the maximum performance. The requirements for such a generator are discussed. New ways of avoiding overlapping subsequences by combining two generators are proposed. Some fundamental philosophical problems in proving independence of random streams are discussed. Remedies for hitherto ignored quantization errors are offered. An open source C++ implementation is provided for a generator that meets these needs.

Estimating The Accuracy Of Automated Classification Systems Using Only Expert Ratings That Are Less Accurate Than The System, Paul E. Lehner

Journal of Modern Applied Statistical Methods

A method is presented to estimate the accuracy of an automated classification system based only on expert ratings on test cases, where the system may be substantially more accurate than the raters. In this method an estimate of overall rater accuracy is derived from the level of inter-rater agreement, Bayesian updating based on estimated rater accuracy is applied to estimate a ground truth probability for each classification on each test case, and then overall system accuracy is estimated by comparing the relative frequency that the system agrees with the most probable classification at different probability levels. A simulation analysis provides …

Modeling Probability Of Causal And Random Impacts, Stan Lipovetsky, Igor Mandel

Journal of Modern Applied Statistical Methods

The method of the estimation of the probability of an event occurring under the influence of the causal and random effects is considered. Epistemological differences from the traditional approaches to causality are discussed, and a new model of the statistical estimation of the parameters of each effect is proposed. The simple and effective algorithms of the model parameters estimation are presented, and numerical simulations are performed. A practical marketing example is analyzed. The results support the validity of the estimation procedure and open the perspective for the application of the method for various decision making problems, where different causes can …

Estimation For The Parameters Of The Exponentiated Exponential Distribution Using A Median Ranked Set Sampling, Monjed H. Samuh, Areen Qtait

Journal of Modern Applied Statistical Methods

The method of maximum likelihood estimation based on Median Ranked Set Sampling (MRSS) was used to estimate the shape and scale parameters of the Exponentiated Exponential Distribution (EED). They were compared with the conventional estimators. The relative efficiency was used for comparison. The amount of information (in Fisher's sense) available from the MRSS about the parameters of the EED were be evaluated. Confidence intervals for the parameters were constructed using MRSS.

Estimating The Strength Of An Association Based On A Robust Smoother, Rand Wilcox

Journal of Modern Applied Statistical Methods

It is known that the more obvious parametric approaches to fitting a regression line to data are often not flexible enough to provide an adequate approximation of the true regression line. Many nonparametric regression estimators, often called smoothers, have been derived that are aimed at dealing with this problem. The paper deals with the issue of estimating the strength of an association based on the fit obtained by a robust smoother. A simple approach, already known, is to estimate explanatory power in a fairly obvious manner. This approach has been found to perform reasonably well when using the smoother LOESS. …

Per Family Or Familywise Type I Error Control: "Eether, Eyether, Neether, Nyther, Let's Call The Whole Thing Off!", H. J. Keselman

Journal of Modern Applied Statistical Methods

Frane (2015) pointed out the difference between per-family and familywise Type I error control and how different multiple comparison procedures control one method but not necessarily the other. He then went on to demonstrate in the context of a two group multivariate design containing different numbers of dependent variables and correlations between variables how the per-family rate inflates beyond the level of significance. In this article I reintroduce other newer better methods of Type I error control. These newer methods provide more power to detect effects than the per-family and familywise techniques of control yet maintain the overall rate of …

Comparison Of Bayesian Credible Intervals To Frequentist Confidence Intervals, Kathy Gray, Brittany Hampton, Tony Silveti-Falls, Allison Mcconnell, Casey Bausell

Journal of Modern Applied Statistical Methods

Frequentist confidence intervals were compared with Bayesian credible intervals under a variety of scenarios to determine when Bayesian credible intervals outperform frequentist confidence intervals. Results indicated that Bayesian interval estimation frequently produces results with precision greater than or equal to the frequentist method.

Special Education Distributions And Analysis, Valerie Felder, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

Micceri (1989) examined the distributional characteristics of 440 large sample general education achievement and psychometric measures. All the distributions were found to be statistically significantly different from the normal distribution. In this study, 395 special education datasets were examined. Although there were some normally distributed datasets, most were not, and some were markedly different in shape from those found by Micceri (1989). Implications for statistical testing and making special education policy decisions were given.

Vol. 14, No. 1 (Full Issue), Jmasm Editors

Journal of Modern Applied Statistical Methods

.

A Comparison Of Semi-Parametric And Nonparametric Methods For Estimating Mean Time To Event For Randomly Left Censored Data, Farzana Chowdhury, Jahida Gulshan, Syed Shahadat Hossain

Journal of Modern Applied Statistical Methods

The aim of this study was to make a comparison among existing estimation methods (Kaplan-Meier, Nelson-Aalen and Regression on Ordered Statistics (ROS)) for randomly left censored time to event data under selected distributions and for different level of censoring and sample sizes in order to determine the strength of these methods based on simulated data. Comparisons among the methods are made on the basis of unbiasedness and Monte Carlo Standard Error of the summary statistics (mean time to event) obtained by those methods under different conditions.

Bootstrapping Vs. Asymptotic Theory In Property And Casualty Loss Reserving, Andrew J. Difronzo Jr.

Honors Projects in Mathematics

One of the key functions of a property and casualty (P&C) insurance company is loss reserving, which calculates how much money the company should retain in order to pay out future claims. Most P&C insurance companies use non-stochastic (non-random) methods to estimate these future liabilities. However, future loss data can also be projected using generalized linear models (GLMs) and stochastic simulation. Two simulation methods that will be the focus of this project are: bootstrapping methodology, which resamples the original loss data (creating pseudo-data in the process) and fits the GLM parameters based on the new data to estimate the sampling …

Best Practice Recommendations For Data Screening, Justin A. Desimone, Peter D. Harms, Alice J. Desimone

Department of Management: Faculty Publications

Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has …

A Generalized Inflated Geometric Distribution, Ram Datt Joshi

Theses, Dissertations and Capstones

A count data that have excess number of zeros, ones, twos or threes are commonplace in experimental studies. But these inflated frequencies at particular counts may lead to over dispersion and thus may cause difficulty in data analysis. So, to get appropriate results from them and to overcome the possible anomalies in parameter estimation, we may need to consider suitable inflated distribution.

In this thesis, we have considered a Swedish fertility dataset with inflated values at some particular counts. Generally, Inflated Poisson or Inflated Negative Binomial distribution are the most common distributions for analyzing such data. Geometric distribution can be …