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 Surveys (2)
 Nonnormality (2)
 Monte Carlo (2)
 Permutation test (2)
 Missing data (2)

 Bayes (2)
 Kernel density estimation (2)
 Anchoring (2)
 Covariance heterogeneity (2)
 Recall (2)
 Bootstrap (2)
 Generalized linear models (2)
 Clifford Blair (1)
 Bayesian statistics (1)
 ANOVA (1)
 Censored studies (1)
 Average attributable fraction (1)
 Burr type X distribution (1)
 Bracketing (1)
 Accuracy (1)
 Attributable fraction (1)
 Bayesian (1)
 Clinical research (1)
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Articles 1  30 of 52
FullText Articles in Social and Behavioral Sciences
On Comparison Of Hypothesis Tests In The Bayesian Framework Without Loss Function, Vladimir Gercsik, Mark Kelbert
On Comparison Of Hypothesis Tests In The Bayesian Framework Without Loss Function, Vladimir Gercsik, Mark Kelbert
Journal of Modern Applied Statistical Methods
The problem is how to compare the quality of different hypothesis tests in a Bayesian framework without introducing a loss function. Three different linear orders on the set of all possible hypothesis tests are studied. The most natural order estimates the Fisher information between indicators of event and decision.
Confidence Intervals On Subsets May Be Misleading, Juliet Popper Shaffer
Confidence Intervals On Subsets May Be Misleading, Juliet Popper Shaffer
Journal of Modern Applied Statistical Methods
A combination of hypothesis testing and confidence interval construction is often used in social and behavioral science studies. Sometimes confidence intervals are computed or reported only if a null hypothesis is rejected, perhaps to see whether the range of values is of practical importance. Sometimes they are constructed or reported only if a null hypothesis is accepted, in order to assess the range of plausible nonnull values due to inadequate power to detect them. Even if always computed, they are interpreted differently, depending on whether the null value is or is not included. Furthermore, many studies in which the null ...
Modeling Incomplete Longitudinal Data, Hakan Demirtas
Modeling Incomplete Longitudinal Data, Hakan Demirtas
Journal of Modern Applied Statistical Methods
This article presents a review of popular parametric, semiparametric and adhoc approaches for analyzing incomplete longitudinal data.
Assessing Treatment Effects In Randomized Longitudinal TwoGroup Designs With Missing Observations, James Algina, H. J. Keselman
Assessing Treatment Effects In Randomized Longitudinal TwoGroup Designs With Missing Observations, James Algina, H. J. Keselman
Journal of Modern Applied Statistical Methods
SAS’s PROC MIXED can be problematic when analyzing data from randomized longitudinal twogroup designs when observations are missing over time. Overall (1996, 1999) and colleagues found a number of procedures that are effective in controlling the number of false positives (Type I errors) and are yet sensitive (powerful) to detect treatment effects. Two favorable methods incorporate time in study and baseline scores to model the missing data mechanism; one method was a singlestage PROC MIXED ANCOVA solution and the other was a twostage endpoint analysis using the change scores as dependent scores. Because the twostage approach can lack sensitivity ...
An Overview Of The RespondentGenerated Intervals (Rgi) Approach To Sample Surveys, S. James Press, Judith M. Tanur
An Overview Of The RespondentGenerated Intervals (Rgi) Approach To Sample Surveys, S. James Press, Judith M. Tanur
Journal of Modern Applied Statistical Methods
This article brings together many years of research on the RespondentGenerated Intervals (RGI) approach to recall in factual sample surveys. Additionally presented is new research on the use of RGI in opinion surveys and the use of RGI with gammadistributed data. The research combines Bayesian hierarchical modeling with various cognitive aspects of sample surveys.
Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations, Lisa M. Lix, Aynslie M. Hinds
Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations, Lisa M. Lix, Aynslie M. Hinds
Journal of Modern Applied Statistical Methods
Conventional and approximate degrees of freedom procedures for testing multivariate interaction contrasts in groups by trials repeated measures designs were compared under assumption violation conditions. Procedures were based on either leastsquares or robust estimators. Power generally favored test procedures based on robust estimators for nonnormal distributions, but was influenced by the degree of departure from nonnormality, definition of power, and magnitude of the multivariate effect size.
Monte Carlo Evaluation Of Ordinal D With Improved Confidence Interval, Du Feng, Norman Cliff
Monte Carlo Evaluation Of Ordinal D With Improved Confidence Interval, Du Feng, Norman Cliff
Journal of Modern Applied Statistical Methods
This article reports a Monte Carlo evaluation of ordinal statistic d with modified confidence intervals (CI) for location comparison of two independent groups under various conditions. Type I error rate, power, and coverage of CI of d were compared to those of the Welch's ttest.
Size And Power Of The Reset Test As Applied To Systems Of Equations: A Bootstrap Approach, Ghazi Shukur, Panagiotis Mantalos
Size And Power Of The Reset Test As Applied To Systems Of Equations: A Bootstrap Approach, Ghazi Shukur, Panagiotis Mantalos
Journal of Modern Applied Statistical Methods
The size and power of various generalization of the RESET test for functional misspecification are investigated, using the “Bootsrap critical values”, in systems ranging from one to ten equations. The properties of 8 versions of the test are studied using Monte Carlo methods. The results are then compared with another study of Shukur and Edgerton (2002), in which they used the asymptotic critical values instead and found that in general only one version of the tests works well regarding size properties. In our study, when applying the bootstrap critical values, we find that all the tests exhibits correct size even ...
Variance Stabilizing Power Transformation For Time Series, Victor M. Guerrero, Rafael Perera
Variance Stabilizing Power Transformation For Time Series, Victor M. Guerrero, Rafael Perera
Journal of Modern Applied Statistical Methods
A confidence interval was derived for the index of a power transformation that stabilizes the variance of a timeseries. The process starts from a modelindependent procedure that minimizes a coefficient of variation to yield a point estimate of the transformation index. The confidence coefficient of the interval is calibrated through a simulation.
On A Simple Method For Analyzing Multivariate Survival Data Using Sample Survey Methods, Pingfu Fu, J. Sunil Rao
On A Simple Method For Analyzing Multivariate Survival Data Using Sample Survey Methods, Pingfu Fu, J. Sunil Rao
Journal of Modern Applied Statistical Methods
A simple technique is illustrated for analyzing multivariate survival data. The data situation arises when an individual records multiple survival events, or when individuals recording single survival events are grouped into clusters. Past work has focused on developing new methods to handle such data. Here, we use a connection between Poisson regression and survival modeling and a cluster sampling approach to adjust the variance estimates. The approach requires parametric assumption for the marginal hazard function, but avoids specification of a joint multivariate survival distribution. A simulation study demonstrates the proposed approach is a competing method of recent developed marginal approaches ...
Interval Estimation For The Scale Parameter Of Burr Type X Distribution Based On Grouped Data, Amjad D. AlNasser, Ayman Baklizi
Interval Estimation For The Scale Parameter Of Burr Type X Distribution Based On Grouped Data, Amjad D. AlNasser, Ayman Baklizi
Journal of Modern Applied Statistical Methods
The application of some bootstrap type intervals for the scale parameter of the Burr type X distribution with grouped data is proposed. The general asymptotic confidence interval procedure (Chen & Mi, 2001) is studied. The performance of these intervals is investigated and compared. Some of the bootstrap intervals give better performance for situations of small sample size and heavy censoring.
Confidence Elicitation And Anchoring In The RespondentGenerated Intervals (Rgi) Protocol, Liping Chu, S. James Press, Judith M. Tanur
Confidence Elicitation And Anchoring In The RespondentGenerated Intervals (Rgi) Protocol, Liping Chu, S. James Press, Judith M. Tanur
Journal of Modern Applied Statistical Methods
The RespondentGenerated Intervals protocol (RGI) has been used to have respondents recall the answer to a factual question by giving not only a point estimate but also bounds within which they feel it is almost certain that the true value of the quantity being reported upon falls. The RGI protocol is elaborated in this article with the goal of improving the accuracy of the estimators by introducing cueing mechanisms to direct confident (and thus presumably accurate) respondents to give shorter intervals and less confident (and thus presumably less accurate) respondents to give longer ones.
Type I Error Rates For A One Factor WithinSubjects Design With Missing Values, Miguel A. Padilla, James Algina
Type I Error Rates For A One Factor WithinSubjects Design With Missing Values, Miguel A. Padilla, James Algina
Journal of Modern Applied Statistical Methods
Missing data are a common problem in educational research. A promising technique, that can be implemented in SAS PROC MIXED and is therefore widely available, is to use maximum likelihood to estimate model parameters and base hypothesis tests on these estimates. However, it is not clear which test statistic in PROC MIXED performs better with missing data. The performance of the Hotelling LawleyMcKeon and KenwardRoger omnibus test statistics on the means for a single factor withinsubject ANOVA are compared. The results indicate that the KenwardRoger statistic performed better in terms of keeping the Type I error close to the nominal ...
Multivariate And Multistrata Nonparametric Tests: The Nonparametric Combination Method, Livio Corain, Luigi Salmaso
Multivariate And Multistrata Nonparametric Tests: The Nonparametric Combination Method, Livio Corain, Luigi Salmaso
Journal of Modern Applied Statistical Methods
Researchers and practitioners in many scientific disciplines and industrial fields are often faced with complex problems when dealing with comparisons between two or more groups using classical parametric methods. The data arising from real problems rarely are in agreement with stringent parametric assumptions. The NonParametric Combination (NPC) methodology frees the researcher from stringent assumptions of parametric methods and allows a more flexible analysis, both in terms of specification of multivariate hypotheses and in terms of the nature of the variables involved in the analysis. An outline of NPC methodology is given, along with case studies.
A Note On Extending Scheffé’S Modified MultipleComparison Procedure To Other Analysis Situations, Xinyue Zhou, Joel R. Levin
A Note On Extending Scheffé’S Modified MultipleComparison Procedure To Other Analysis Situations, Xinyue Zhou, Joel R. Levin
Journal of Modern Applied Statistical Methods
This article extends Scheffé’s modified (sequential) multiplecomparison procedure in oneway analysisof variance to other analysis situations, including interaction comparisons in factorial ANOVA designs, tests of partial regression coefficients in multipleregression analysis, and comparisons of means in onefactor multivariate analyses of variance. Researchers who are concerned with maintaining familywise Type I error rates while increasing statistical power relative to the original (simultaneous) Scheffébased procedures are encouraged to consider these improved multiplecomparison methods.
Aligned Rank Tests As Robust Alternatives For Testing Interactions In Multiple Group Repeated Measures Designs With Heterogeneous Covariances, Xiaosheng Lei, Janet K. Holt, T. Mark Beasley
Aligned Rank Tests As Robust Alternatives For Testing Interactions In Multiple Group Repeated Measures Designs With Heterogeneous Covariances, Xiaosheng Lei, Janet K. Holt, T. Mark Beasley
Journal of Modern Applied Statistical Methods
Data simulation was used to investigate whether tests performed on aligned ranks (Beasley, 2002) could be used as robust alternatives to parametric methods for testing a splitplot interaction with nonnormal data and heterogeneous covariance matrices. Results indicated the aligned rank method do not have any distinct advantage over parametric methods in this situation.
The President’S Problem, JannHuei Jinn
The President’S Problem, JannHuei Jinn
Journal of Modern Applied Statistical Methods
A solution is offered in response to a complex combination problem challenged by Blom, Englund, and Sandell (1998). The problem is to determine the probability that a random permutation of the word BILLCLINTON has no equal neighbors.
An Algorithm And Code For Computing Exact Critical Values For The KruskalWallis Nonparametric OneWay Anova, Sikha Bagui, Subhash Bagui
An Algorithm And Code For Computing Exact Critical Values For The KruskalWallis Nonparametric OneWay Anova, Sikha Bagui, Subhash Bagui
Journal of Modern Applied Statistical Methods
In this article, an algorithm and code to compute exact critical values (or percentiles) for KruskalWallis test on k independent treatment populations with equal or unequal sample sizes using Visual Basic (VB.NET) is provided. This program has the ability to calculate critical values for any k , sample sizes (n_{i} ) , and significance level (α ) . An exact critical value table for k = 4 is also developed. The table will be useful to practitioners since it is not available in standard nonparametric statistics texts. The program can also be used to compute any other critical values.
“Teaching” In Honor Of Cliff Blair, Howard Stoker
“Teaching” In Honor Of Cliff Blair, Howard Stoker
Journal of Modern Applied Statistical Methods
In this article, I conceptualize teaching as the profession of facilitating and stimulating learning. As “teachers”, we help students acquire learning skills that they may expand on later in their life. I review fifteen principles that facilitate effective learning.
Mentoring Doctoral Students: A Personal Perspective, Bruce W. Hall
Mentoring Doctoral Students: A Personal Perspective, Bruce W. Hall
Journal of Modern Applied Statistical Methods
In this brief essay, I reflect on the mentoring process based on advising over thirty doctoral students in measurement, evaluation, and research. There is considerable cause for optimism, and it is among the professors’ highest honor to mentor the doctoral student.
A Conversation With R. Clifford Blair On The Occasion Of His Retirement, Shlomo S. Sawilowsky
A Conversation With R. Clifford Blair On The Occasion Of His Retirement, Shlomo S. Sawilowsky
Journal of Modern Applied Statistical Methods
An interview was conducted on 23 November 2003 with R. Clifford Blair on the occasion on his retirement from the University of South Florida. This article is based on that interview. Biographical sketches and images of members of his academic genealogy are provided.
A Modification Of The Em Algorithm To Estimate An AndersenGill Gamma Frailty Model For Multivariate Failure Time Data, Maria Antònia Barceló, Marc Saez
A Modification Of The Em Algorithm To Estimate An AndersenGill Gamma Frailty Model For Multivariate Failure Time Data, Maria Antònia Barceló, Marc Saez
Journal of Modern Applied Statistical Methods
A modification of the AndersenGill gamma shared frailty model is presented. The variance of the frailty is directly modeled by means of a generalized linear model, the EM algorithm is modified in order to simultaneously estimate a semiparametric model for the failure times and a model for the variance of the frailty. A simulation study is conducted to evaluate the performance of the proposed algorithm (EMB algorithm) and compared with other methods, a marginal model, and a conditional model. Multivariate data from a nosocomial infection study is used to illustrate the methods. The EMB fit turned out to be better ...
PseudoRandom Number Generation In R For Commonly Used Multivariate Distributions, Hakan Demirtas
PseudoRandom Number Generation In R For Commonly Used Multivariate Distributions, Hakan Demirtas
Journal of Modern Applied Statistical Methods
An increasing number of practitioners and applied statisticians have started using the R programming system in recent years for their computing and data analysis needs. As far as pseudorandom number generation is concerned, the builtin generator in R does not contain multivariate distributions. In this article, R routines for widely used multivariate distributions are presented.
Statistics And Technology: Reflections On 35 Years Of Change, James J. Higgins
Statistics And Technology: Reflections On 35 Years Of Change, James J. Higgins
Journal of Modern Applied Statistical Methods
From the days when statistical calculations were done on mechanical calculators to today, technology has transformed the discipline of statistics. More than just giving statisticians the power to crunch numbers, it has fundamentally changed the way we teach, do research, and consult. In this article, I give some examples of this from my 35 years as an academic statistician.
A New GoodnessOfFit Test For Item Response Theory, John H. Neel
A New GoodnessOfFit Test For Item Response Theory, John H. Neel
Journal of Modern Applied Statistical Methods
Chisquare techniques for testing goodnessoffit in item response theory are shown to give incorrect results. A new measure, CB, based on cumulants is proposed which avoids the arbitrary nature of interval creation found in chisquare techniques. The distribution of CB is estimated using Monte Carlo techniques and critical values for testing goodnessoffit are given.
A RankBased Estimation Procedure For Linear Models With Clustered Data, Suzanne R. Dubnicka
A RankBased 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.
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.
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.
MetaAnalysis Of Results And Individual Patient Data In Epidemiologal Studies, Aurelio Tobías, Marc Saez, Manolis Kogevinas
MetaAnalysis 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 metaanalysis 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 metaanalysis technique. Alternatively, primary data from all studies were pooled and reanalyzed 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 smallsample efficiency of some recently derived methods is rather poor. This article reports that a skipped mean, where outliers are removed via a projectiontype 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 ...