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Articles 961 - 990 of 1091
Full-Text Articles in Statistical Theory
Jmasm11: Comparing Two Small Binomial Proportions, James F. Reed Iii
Jmasm11: Comparing Two Small Binomial Proportions, James F. Reed Iii
Journal of Modern Applied Statistical Methods
A large volume of research has focused on comparing the difference between two small binomial proportions. Statisticians recognize that Fisher’s Exact test and Yates chi-square test are excessively conservative. Likewise, many statisticians feel that Pearson’s Chi-square or the likelihood statistic may be inappropriate for small samples. Viable alternatives exist.
A Test-Retest Transition Matrix: A Modification Of Mcnemar’S Test, J. Wanzer Drane, W. Gregory Thatcher
A Test-Retest Transition Matrix: A Modification Of Mcnemar’S Test, J. Wanzer Drane, W. Gregory Thatcher
Journal of Modern Applied Statistical Methods
McNemar introduced what is known today as a test for symmetry in a two by two contingency tables. The logic of the test is based on a sample of matched pairs with a dichotomous response. In our example, the sample consists of the scores before and after an education program and the responses before and after the program. Each pair of scores is from only one person. The pretest divides the group of responders according to their answers to a dichotomous question. The posttest divides the two groups into two groups of like labels. The result is a two by …
On The Reporting Of Reliability In Content Analysis, Patric R. Spence
On The Reporting Of Reliability In Content Analysis, Patric R. Spence
Journal of Modern Applied Statistical Methods
This article explores one type of misreporting of reliability that has been seen in recent conference papers and articles using the method of content analysis. The reporting of reliability is central to the validity of claims made using this method. A brief overview of content analysis is offered, followed by the exploration of one type of misreporting of reliability. Suggestions are offered to address the problem.
Validation Studies: Matters Of Dimensionality, Accuracy, And Parsimony With Predictive Discriminant Analysis And Factor Analysis, David A. Walker
Validation Studies: Matters Of Dimensionality, Accuracy, And Parsimony With Predictive Discriminant Analysis And Factor Analysis, David A. Walker
Journal of Modern Applied Statistical Methods
Two studies were used as examples that examined issues of dimensionality, accuracy, and parsimony in educational research via the use of predictive discriminant analysis and factor analysis. Using a two-group problem, study 1 looked at how accurately group membership could be predicted from subjects’ test scores. Study 2 looked at the dimensionality structure of an instrument and if it developed constructs that would measure theorized domains.
Stratified Extreme Ranked Set Sample With Application To Ratio Estimators, Hani M. Samawai, Laith J. Saeid
Stratified Extreme Ranked Set Sample With Application To Ratio Estimators, Hani M. Samawai, Laith J. Saeid
Journal of Modern Applied Statistical Methods
Stratified extreme ranked set sample (SERSS) is introduced. The performance of the combined and separate ratio estimates using SERSS is investigated. Theoretical and simulation study are presented. Results indicate that using SERSS for estimating the ratios is more efficient than using stratified simple random sample (SSRS) and simple random sample (SRS). In some cases it is more efficient than ranked set sample (RSS) and stratified ranked set sample (SRSS), when the underlying distribution is symmetric. An application to real data on the bilirubin level in jaundice babies is introduced to illustrate the method.
Depth Based Permutation Test For General Differences In Two Multivariate Populations, Yonghong Gao
Depth Based Permutation Test For General Differences In Two Multivariate Populations, Yonghong Gao
Journal of Modern Applied Statistical Methods
For two p-dimensional data sets, interest exists in testing if they come from the common population distribution. Proposed is a practical, effective and easy to implement procedure for the testing problem. The proposed procedure is a permutation test based on the concept of the depth of one observation relative to some population distribution. The proposed test is demonstrated to be consistent. A small Monte Carlo simulation was conducted to evaluate the power of the proposed test. The proposed test is applied to some numerical examples.
Accurate Binary Decisions For Assessing Coronary Artery Disease, Mehmet Ali Cengiz
Accurate Binary Decisions For Assessing Coronary Artery Disease, Mehmet Ali Cengiz
Journal of Modern Applied Statistical Methods
Generalized linear models offer convenient and highly applicable tools for modeling and predicting the behavior of random variables in terms of observable factors and covariates. This paper investigates applications of a special case of generalized linear model to improve the accuracy of predictions and decisions adopting Bayesian methods, in the specific context of assessing coronary artery disease. The basic model is developed for this application using binary response. The results clearly demonstrate the potential advantages offered by this approach.
Statistical Pronouncements Iii, Jmasm Editors
Statistical Pronouncements Iii, Jmasm Editors
Journal of Modern Applied Statistical Methods
No abstract provided.
Meta-Analysis Of Results And Individual Patient Data In Epidemiologal Studies, Aurelio Tobías, Marc Saez, Manolis Kogevinas
Meta-Analysis Of Results And Individual Patient Data In Epidemiologal Studies, Aurelio Tobías, Marc Saez, Manolis Kogevinas
Journal of Modern Applied Statistical Methods
Epidemiological information can be aggregated by combining results through a meta-analysis technique, or by pooling and analyzing primary data. Common approaches to analyzing pooled studies through an example on the effect of occupational exposure to wood dust on sinonasal cancer are described. Results were combined applying a meta-analysis technique. Alternatively, primary data from all studies were pooled and re-analyzed using mixed effect models. The combination of individual information rather than results is desirable to facilitate interpretations of epidemiological findings, leading also to more precise estimations and more powerful statistical tests for study heterogeneity.
Multivariate Location: Robust Estimators And Inference, Rand R. Wilcox, H. J. Keselman
Multivariate Location: Robust Estimators And Inference, Rand R. Wilcox, H. J. Keselman
Journal of Modern Applied Statistical Methods
The sample mean can have poor efficiency relative to various alternative estimators under arbitrarily small departures from normality. In the multivariate case, (affine equivariant) estimators have been proposed for dealing with this problem, but a comparison of various estimators by Massé and Plante (2003) indicated that the small-sample efficiency of some recently derived methods is rather poor. This article reports that a skipped mean, where outliers are removed via a projection-type outlier detection method, is found to be more satisfactory. The more obvious method for computing a confidence region based on the skipped estimator (using a slight modification of the …
A Power Comparison Of Robust Test Statistics Based On Adaptive Estimators, H. J. Keselman, Rand R. Wilcox, James Algina, Abdul R. Othman
A Power Comparison Of Robust Test Statistics Based On Adaptive Estimators, H. J. Keselman, Rand R. Wilcox, James Algina, Abdul R. Othman
Journal of Modern Applied Statistical Methods
Seven test statistics known to be robust to the combined effects of nonnormality and variance heterogeneity were compared for their sensitivity to detect treatment effects in a one-way completely randomized design containing four groups. The six Welch-James-type heteroscedastic tests adopted either symmetric or asymmetric trimmed means, were transformed for skewness, and used a bootstrap method to assess statistical significance. The remaining test, due to Wilcox and Keselman (2003), used a modification of the well-known one-step M-estimator of central tendency rather than trimmed means. The Welch-James-type test is recommended because for nonnormal data likely to be encountered in applied research settings …
A Rank-Based Estimation Procedure For Linear Models With Clustered Data, Suzanne R. Dubnicka
A Rank-Based Estimation Procedure For Linear Models With Clustered Data, Suzanne R. Dubnicka
Journal of Modern Applied Statistical Methods
A rank method is presented for estimating regression parameters in the linear model when observations are correlated. This correlation is accounted for by including a random effect term in the linear model. A method is proposed that makes few assumptions about the random effect and error distribution. The main goal of this article is to determine the distributions for which this method performs well relative to existing methods.
A Generalized Quasi-Likelihood Model Application To Modeling Poverty Of Asian American Women, Jeffrey R. Wilson
A Generalized Quasi-Likelihood Model Application To Modeling Poverty Of Asian American Women, Jeffrey R. Wilson
Journal of Modern Applied Statistical Methods
A generalized quasi-likelihood function that does not require the assumption of an underlying distribution when modeling jointly the mean and the variance, is introduced to examine poverty of Asian American women living in the West coast of the United States, using data from U.S. Census Bureau.
Estimation Of Multiple Linear Functional Relationships, Amjad D. Al-Nasser
Estimation Of Multiple Linear Functional Relationships, Amjad D. Al-Nasser
Journal of Modern Applied Statistical Methods
This article deals with multiple linear functional relationships models. Two robust estimations procedure are proposed to estimate the model, based on Generalized Maximum Entropy and Partial Least Square. They are distribution free and do not rely (so much) on classical assumptions. The experiments showed that the GME approach outperforms the PLS in terms of mean squares of errors (MSE). Empirical examples are studied.
Teaching Random Assignment: Do You Believe It Works?, Shlomo S. Sawilowsky
Teaching Random Assignment: Do You Believe It Works?, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
Textbook authors admonish students to check on the comparability of two randomly assigned groups by conducting statistical tests on pretest means to determine if randomization worked. A Monte Carlo study was conducted on a sample of n = 2 per group, where each participant’s personality profile was represented by 7,500 randomly selected and assigned scores. Independent samples t tests were conducted and the results demonstrated that random assignment was successful in equating the two groups on 7,467 variables. The students’ focus is redirected from the ability of random assignment to create comparable groups to the testing of the claims of …
A Comparison Of Bayesian And Frequentist Statistics As Applied In A Simple Repeated Measures Example, Jan Perkins, Daniel Wang
A Comparison Of Bayesian And Frequentist Statistics As Applied In A Simple Repeated Measures Example, Jan Perkins, Daniel Wang
Journal of Modern Applied Statistical Methods
Clinicians see Bayesian and frequentist analysis in published research papers, and need a basic understanding of both. A repeated measures data set was analyzed using both approaches. Assumptions underlying each method and conclusions reached were contrasted. The Bayesian approach is a viable alternative to frequentist statistical analysis for many clinical projects.
Jmasm10: A Fortran Routine For Sieve Bootstrap Prediction Intervals, Andrés M. Alonso
Jmasm10: A Fortran Routine For Sieve Bootstrap Prediction Intervals, Andrés M. Alonso
Journal of Modern Applied Statistical Methods
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear processes is described. The approach uses the sieve bootstrap procedure of Bühlmann (1997) based on residual resampling from an autoregressive approximation to the given process.
A Comparison Of Methods For Longitudinal Analysis With Missing Data, James Algina, H. J. Keselman
A Comparison Of Methods For Longitudinal Analysis With Missing Data, James Algina, H. J. Keselman
Journal of Modern Applied Statistical Methods
In a longitudinal two-group randomized trials design, also referred to as randomized parallel-groups design or split-plot repeated measures design, the important hypothesis of interest is whether there are differential rates of change over time, that is, whether there is a group by time interaction. Several analytic methods have been presented in the literature for testing this important hypothesis when data are incomplete. We studied these methods for the case in which the missing data pattern is non-monotone. In agreement with earlier work on monotone missing data patterns, our results on bias, sampling variability, Type I error and power support the …
On Polynomial Transformations For Simulating Multivariate Non-Normal Distributions, Todd C. Headrick
On Polynomial Transformations For Simulating Multivariate Non-Normal Distributions, Todd C. Headrick
Journal of Modern Applied Statistical Methods
Procedures are introduced and discussed for increasing the computational and statistical efficiency of polynomial transformations used in Monte Carlo or simulation studies. Comparisons are also made between polynomials of order three and five in terms of (a) computational and statistical efficiency, (b) the skew and kurtosis boundary, and (c) boundaries for Pearson correlations. It is also shown how ranked data can be simulated for specified Spearman correlations and sample sizes. Potential consequences of nonmonotonic transformations on rank correlations are also discussed.
An Alternative Q Chart Incorporating A Robust Estimator Of Scale, Michael B. C. Khoo
An Alternative Q Chart Incorporating A Robust Estimator Of Scale, Michael B. C. Khoo
Journal of Modern Applied Statistical Methods
In overcoming the shortcomings of the classical control charts in a short runs production, Quesenberry (1991 & 1995a – d) proposed Q charts for attributes and variables data. An approach to enhance the performance of a variable Q chart based on individual measurements using a robust estimator of scale is proposed. Monte carlo simulations are conducted to show that the proposed robust Q chart is superior to the present Q chart.
Quantifying The Proportion Of Cases Attributable To An Exposure, Camil Fuchs, Vance W. Berger
Quantifying The Proportion Of Cases Attributable To An Exposure, Camil Fuchs, Vance W. Berger
Journal of Modern Applied Statistical Methods
The attributable fraction and the average attributable fractions, which are commonly used to assess the relative effect of several exposures to the prevalence of a disease, do not represent the proportion of cases caused by each exposure. Furthermore, the sum of attributable fractions over all exposures generally exceeds not only the attributable fraction for all exposures taken together, but also 100%. Other measures are discussed here, including the directly attributable fraction and the confounding fraction, that may be more suitable in defining the fraction directly attributable to an exposure.
Model Comparisons Using Information Measures, C. Mitchell Dayton
Model Comparisons Using Information Measures, C. Mitchell Dayton
Journal of Modern Applied Statistical Methods
Methodologists have criticized the use of significance tests in the behavioral sciences but have failed to provide alternative data analysis strategies that appeal to applied researchers. For purposes of comparing alternate models for data, information-theoretic measures such as Akaike AIC have advantages in comparison with significance tests. Model-selection procedures based on a min(AIC) strategy, for example, are holistic rather than dependent upon a series of sometimes contradictory binary (accept/reject) decisions.
Using Zero-Inflated Count Regression Models To Estimate The Fertility Of U. S. Women, Dudley L. Poston Jr., Sherry L. Mckibben
Using Zero-Inflated Count Regression Models To Estimate The Fertility Of U. S. Women, Dudley L. Poston Jr., Sherry L. Mckibben
Journal of Modern Applied Statistical Methods
In the modeling of count variables there is sometimes a preponderance of zero counts. This article concerns the estimation of Poisson regression models (PRM) and negative binomial regression models (NBRM) to predict the average number of children ever born (CEB) to women in the U.S. The PRM and NBRM will often under-predict zeros because they do not consider zero counts of women who are not trying to have children. The fertility of U.S. white and Mexican-origin women show that zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models perform better in many respects than the Poisson and negative binomial models. …
Test Of Homogeneity For Umbrella Alternatives In Dose-Response Relationship For Poisson Variables, Chengjie Xiong, Yan Yan, Ming Ji
Test Of Homogeneity For Umbrella Alternatives In Dose-Response Relationship For Poisson Variables, Chengjie Xiong, Yan Yan, Ming Ji
Journal of Modern Applied Statistical Methods
This article concerns the testing and estimation of a dose-response effect in medical studies. We study the statistical test of homogeneity against umbrella alternatives in a sequence of Poisson distributions associated with an ordered dose variable. We propose a test similar to Cochran-Armitage’s trend test and study the asymptotic null distribution and the power of the test. We also propose an estimator to the vertex point when the umbrella pattern is confirmed and study the performance of the estimator. A real data set pertaining to the number of visible revertant colonies associated with different doses of test agents in an …
Alphabet Letter Recognition And Emergent Literacy Abilities Of Rising Kindergarten Children Living In Low-Income Families, Stephanie Wehry
Alphabet Letter Recognition And Emergent Literacy Abilities Of Rising Kindergarten Children Living In Low-Income Families, Stephanie Wehry
Journal of Modern Applied Statistical Methods
Alphabet letter recognition item responses from 1,299 rising kindergarten children from low-income families were used to determine the dimensionality of letter recognition ability. The rising kindergarteners were enrolled in preschool classrooms implementing a research-based early literary curriculum. Item responses from the TERA-3 subtests were also analyzed. Results indicated alphabet letter recognition was unitary. The ability of boys and younger children was less than girls and older children. Child-level letter recognition was highly associated with TERA-3 measures of letter knowledge and conventions of print. Classroom-level mean letter recognition ability accounted for most of variance in classroom mean TERA-3 scores.
A Note On Mles For Normal Distribution Parameters Based On Disjoint Partial Sums Of A Random Sample, W. J. Hurley
A Note On Mles For Normal Distribution Parameters Based On Disjoint Partial Sums Of A Random Sample, W. J. Hurley
Journal of Modern Applied Statistical Methods
Maximum likelihood estimators are computed for the parameters of a normal distribution based on disjoint partial sums of a random sample. It has application in the disaggregation of financial data.
Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data, Armin Gemperli, Penelope Vounatsou
Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data, Armin Gemperli, Penelope Vounatsou
Journal of Modern Applied Statistical Methods
Non-Gaussian point-referenced spatial data are frequently modeled using generalized linear mixed models (GLMM) with location-specific random effects. Spatial dependence can be introduced in the covariance matrix of the random effects. Maximum likelihood-based or Bayesian estimation implemented via Markov chain Monte Carlo (MCMC) for such models is computationally demanding especially for large sample sizes because of the large number of random effects and the inversion of the covariance matrix involved in the likelihood. We review three fitting procedures, the Penalized Quasi Likelihood method, the MCMC, and the Sampling-Importance-Resampling method. They are assessed in terms of estimation accuracy, ease of implementation, and …
Conventional And Robust Paired And Independent-Samples T Tests: Type I Error And Power Rates, Katherine Fradette, H. J. Keselman, Lisa Lix, James Algina, Rand R. Wilcox
Conventional And Robust Paired And Independent-Samples T Tests: Type I Error And Power Rates, Katherine Fradette, H. J. Keselman, Lisa Lix, James Algina, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Monte Carlo methods were used to examine Type I error and power rates of 2 versions (conventional and robust) of the paired and independent-samples t tests under nonnormality. The conventional (robust) versions employed least squares means and variances (trimmed means and Winsorized variances) to test for differences between groups.
Jmasm9: Converting Kendall’S Tau For Correlational Or Meta-Analytic Analyses, David A. Walker
Jmasm9: Converting Kendall’S Tau For Correlational Or Meta-Analytic Analyses, David A. Walker
Journal of Modern Applied Statistical Methods
Expanding on past research, this study provides researchers with a detailed table for use in meta-analytic applications when engaged in assorted examinations of various r-related statistics, such as Kendall’s tau (τ) and Cohen’s d, that estimate the magnitude of experimental or observational effect. A program to convert from the lesser-used tau coefficient to other effect size indices when conducting correlational or meta-analytic analyses is presented.
P* Index Of Segregation: Distribution Under Reassignment, Charles F. Bond, F. D. Richard
P* Index Of Segregation: Distribution Under Reassignment, Charles F. Bond, F. D. Richard
Journal of Modern Applied Statistical Methods
Students of intergroup relations have measured segregation with a P* index. In this article, we describe the distribution of this index under a stochastic model. We derive exact, closed-form expressions for the mean, variance, and skewness of P* under random segregation. These yield equivalent expressions for a second segregation index: η2. Our analytic results reveal some of the distributional properties of these indices, inform new standardizations of the indices, and enable small-sample significance testing. Two illustrative examples are presented.