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Articles 31 - 60 of 136
Full-Text Articles in Applied Statistics
Robust Confidence Intervals For Effect Size In The Two-Group Case, H. J. Keselman, James Algina, Katherine Fradette
Robust Confidence Intervals For Effect Size In The Two-Group Case, H. J. Keselman, James Algina, Katherine Fradette
Journal of Modern Applied Statistical Methods
The probability coverage of intervals involving robust estimates of effect size based on seven procedures was compared for asymmetrically trimming data in an independent two-groups design, and a method that symmetrically trims the data. Four conditions were varied: (a) percentage of trimming, (b) type of nonnormal population distribution, (c) population effect size, and (d) sample size. Results indicated that coverage probabilities were generally well controlled under the conditions of nonnormality. The symmetric trimming method provided excellent probability coverage. Recommendations are provided.
Estimation Of Process Variances In Robust Parameter Designs, T. K. Mak, Fassil Nebebe
Estimation Of Process Variances In Robust Parameter Designs, T. K. Mak, Fassil Nebebe
Journal of Modern Applied Statistical Methods
The modeling of variation through interactions is appealing in crossed array design as it leads to greater robustness to certain type of model misspecification. As an alternative to signal-to-noise analysis, a new, systematic method based on Taguchi type crossed array design is given. It is shown in this article that when fractional factorial design is used for the outer array, the crossed array design is not robust to the presence of noise-noise interactions and a method of rectifying the problem is suggested.
Testing Normality Against The Laplace Distribution, Taisuke Otsu
Testing Normality Against The Laplace Distribution, Taisuke Otsu
Journal of Modern Applied Statistical Methods
Some normality test statistics are proposed by testing non-nested hypotheses of the normal distribution and the Laplace distribution. If the null hypothesis is normal, the proposed non-nested tests are asymptotically equivalent to Geary’s (1935) normality test. The proposed test statistics are compared by the method of approximate slopes and Monte Carlo experiments.
Sample Size Calculation And Power Analysis Of Time-Averaged Difference, Honghu Liu, Tongtong Wu
Sample Size Calculation And Power Analysis Of Time-Averaged Difference, Honghu Liu, Tongtong Wu
Journal of Modern Applied Statistical Methods
Little research has been done on sample size and power analysis under repeated measures design. With detailed derivation, we have shown sample size calculation and power analysis equations for timeaveraged difference to allow unequal sample sizes between two groups for both continuous and binary measures and explored the relative importance of number of unique subjects and number of repeated measurements within each subject on statistical power through simulation.
Testing Goodness Of Fit Of The Geometric Distribution: An Application To Human Fecundability Data, Sudhir R. Paul
Testing Goodness Of Fit Of The Geometric Distribution: An Application To Human Fecundability Data, Sudhir R. Paul
Journal of Modern Applied Statistical Methods
A measure of reproduction in human fecundability studies is the number of menstrual cycles required to achieve pregnancy which is assumed to follow a geometric distribution with parameter p. Tests of heterogeneity in the fecundability data through goodness of fit tests of the geometric distribution are developed, along with a likelihood ratio test statistic and a score test statistic. Simulations show both are liberal, and empirical level of the likelihood ratio statistic is larger than that of the score test statistic. A power comparison shows that the likelihood ratio test has a power advantage. A bootstrap p-value procedure using the …
A Discretized Approach To Flexibly Fit Generalized Lambda Distributions To Data, Steve Su
A Discretized Approach To Flexibly Fit Generalized Lambda Distributions To Data, Steve Su
Journal of Modern Applied Statistical Methods
This article presents a flexible approach to fit statistical distribution to data. It optimizes the bin-width of data histogram to find a suitable generalized lambda distribution. In addition to the default optimization, this approach provides additional flexibility akin to the concepts of loess and kernel smoothing, which allow the users to determine the amount of details they would like to smooth over the data. The approach presented in this article will allow users to visually compare and choose the parameters of generalized lambda distribution that best suit their purposes of study.
Type I Error Of Four Pairwise Mean Comparison Procedures Conducted As Protected And Unprotected Tests, J. Jackson Barnette, James E. Mclean
Type I Error Of Four Pairwise Mean Comparison Procedures Conducted As Protected And Unprotected Tests, J. Jackson Barnette, James E. Mclean
Journal of Modern Applied Statistical Methods
Type I error control accuracy of four commonly used pairwise mean comparison procedures, conducted as protected or unprotected tests, is examined. If error control philosophy is experimentwise, Tukey’s HSD, as an unprotected test, is most accurate and if philosophy is per-experiment, Dunn-Bonferroni, conducted as an unprotected test, is most accurate.
A Bayesian Subset Analysis Of Sensory Evaluation Data, Balgobin Nandram
A Bayesian Subset Analysis Of Sensory Evaluation Data, Balgobin Nandram
Journal of Modern Applied Statistical Methods
In social sciences it is easy to carry out sensory experiments using say a J-point hedonic scale. One major problem with the J-point hedonic scale is that a conversion from the category scales to numeric scores might not be sensible because the panelists generally view increments on the hedonic scale as psychologically unequal. In the current problem several products are rated by a set of panelists on the J-point hedonic scale. One objective is to select the best subset of products and to assess the quality of the products by estimating the mean and standard deviation response …
Estimating The Slope Of Simple Linear Regression In The Presence Of Outliers, Mohammed Al-Haj Ebrahem, Amjad D. Al-Nasser
Estimating The Slope Of Simple Linear Regression In The Presence Of Outliers, Mohammed Al-Haj Ebrahem, Amjad D. Al-Nasser
Journal of Modern Applied Statistical Methods
In this article, an estimation procedure to simple linear regression in the presence of outliers is proposed. The performance of the proposed estimator, the AM estimator, is compared with other traditional estimators: least squares, Theil type repeated median, and geometric mean. A numerical example is given to illustrate the proposed estimator. Simulation results indicate that the proposed estimator is accurate and has a high precision in the presence of outliers.
Testing For Aptitude-Treatment Interactions In Analysis Of Covariance And Randomized Block Designs Under Assumption Violations, Tim Moses, Alan Klockars
Testing For Aptitude-Treatment Interactions In Analysis Of Covariance And Randomized Block Designs Under Assumption Violations, Tim Moses, Alan Klockars
Journal of Modern Applied Statistical Methods
This study compared the robustness of two analysis strategies designed to detect Aptitude-Treatment Interactions to two of their similarly-held assumptions, normality and residual variance homogeneity. The analysis strategies were the test of slope differences in analysis of covariance and the test of the Block-by- Treatment interaction in randomized block analysis of variance. With equal sample sizes in the treatment groups the results showed that residual variance heterogeneity has little effect on either strategy but nonnormality makes the test of slope differences liberal and the test of the Block-by-Treatment interaction conservative. With unequal sample sizes in the treatment groups the often-reported …
Selection Of Independent Binary Features Using Probabilities: An Example From Veterinary Medicine, Ludmila I. Kuncheva, Zoë S.J. Hoare, Peter D. Cockcroft
Selection Of Independent Binary Features Using Probabilities: An Example From Veterinary Medicine, Ludmila I. Kuncheva, Zoë S.J. Hoare, Peter D. Cockcroft
Journal of Modern Applied Statistical Methods
Supervised classification into c mutually exclusive classes based on n binary features is considered. The only information available is an n×c table with probabilities. Knowing that the best d features are not the d best, simulations were run for 4 feature selection methods and an application to diagnosing BSE in cattle and Scrapie in sheep is presented.
Nonparametric Pooling And Testing Of Preference Ratings For Full-Profile Conjoint Analysis Experiments, Rosa Arboretti G., Marco Marozzi, Luigi Salmaso
Nonparametric Pooling And Testing Of Preference Ratings For Full-Profile Conjoint Analysis Experiments, Rosa Arboretti G., Marco Marozzi, Luigi Salmaso
Journal of Modern Applied Statistical Methods
The problem of pooling customer preference ratings within a conjoint analysis experiment has been addressed. A method based on the nonparametric combination of rankings has been proposed to compete with the usual method based on the arithmetic mean. This method is nonparametric with respect to the underlying dependence structure and so no dependence model must be assumed. The two methods have been compared using Spearman’s rank correlation coefficient and related test. Moreover, a further nonparametric testing method has been considered and proposed; this method takes both correlation and distance between ranks into account. By means of a simulation study it …
Kim And Warde’S Mixed Randomized Response Technique For Complex Surveys, Amitava Saha
Kim And Warde’S Mixed Randomized Response Technique For Complex Surveys, Amitava Saha
Journal of Modern Applied Statistical Methods
The randomized response (RR) technique introduced by Warner (1965) was found to be an effective method for reducing answer bias and ensuring better respondent cooperation in estimating the proportion of people in a community bearing a sensitive attribute. Chaudhuri (2001a, 2001b, 2002, 2003) extended Warner’s method and several other well-known RR devices to complex surveys adopting a varying probability sampling design. Kim and Warde (2004) proposed an RR model assuming that the sample is selected with simple random sampling (SRS) with replacement (SRSWR). Here, the method of estimation is presented when sample is chosen with varying selection probabilities and Kim …
A Nonrigorous Approach Of Incorporating Sensitizing Rules Into Multivariate Control Charts, Michael B. C. Khoo
A Nonrigorous Approach Of Incorporating Sensitizing Rules Into Multivariate Control Charts, Michael B. C. Khoo
Journal of Modern Applied Statistical Methods
Multivariate control charts are becoming more important in the monitoring of processes in manufacturing industries because the quality of a process is usually determined by several correlated variables (quality characteristics). The most popular multivariate process control procedure is based on the Hotelling control chart. It is used to monitor the mean vector of a process. A nonrigorous approach of using four sensitizing rules is introduced to improve the performance of a conventional Hotelling chart. The use of these rules on a conventional Hotelling chart do not require a transformation of the T2 statistics into normal random variables. Thus, the …
Inference On (Y < X) In A Pareto Distribution, M. Masoom Ali, Jungsoo Woo
Inference On (Y < X) In A Pareto Distribution, M. Masoom Ali, Jungsoo Woo
Journal of Modern Applied Statistical Methods
Inference on the reliability R = P(Y < X) in a Pareto distribution with a known scale parameter is considered. Point estimates and confidence intervals of R are obtained a test of hypothesis is also considered.
Power Of The T Test For Normal And Mixed Normal Distributions, Marilyn S. Thompson, Samuel B. Green, Yi-Hsin Chen, Shawn Stockford, Wen-Juo Lo
Power Of The T Test For Normal And Mixed Normal Distributions, Marilyn S. Thompson, Samuel B. Green, Yi-Hsin Chen, Shawn Stockford, Wen-Juo Lo
Journal of Modern Applied Statistical Methods
Previous research suggests that the power of the independent-samples t test decreases when population distributions are mixed normal rather than normal, and that robust methods have superior power under these conditions. However, under some conditions, the power for the independent-samples t test can be greater when the population distributions for the independent groups are mixed normal rather than normal. The implications of these results are discussed.
Sample Size Selection For Pair-Wise Comparisons Using Information Criteria, Xuemei Pan, C. Mitchell Dayton
Sample Size Selection For Pair-Wise Comparisons Using Information Criteria, Xuemei Pan, C. Mitchell Dayton
Journal of Modern Applied Statistical Methods
This article provides results for rates of correct identifications of paired-comparison information criteria and Tukey HSD as functions of the pattern of mean differences and of sample size. Therefore, the tables provided are useful for selecting sample sizes in real world applications.
Statistical Pronouncements Iv, Jmasm Editors
Statistical Pronouncements Iv, Jmasm Editors
Journal of Modern Applied Statistical Methods
No abstract provided.
Jmasm21: Pcic_Sas: Best Subsets Using Information Criteria, C. Mitchell Dayton, Xuemei Pan
Jmasm21: Pcic_Sas: Best Subsets Using Information Criteria, C. Mitchell Dayton, Xuemei Pan
Journal of Modern Applied Statistical Methods
PCIC_SAS is a SAS program for identifying optimal subsets of means based on independent groups. All possible configurations of ordered subsets of groups are considered and a best model is identified using both the AIC and BIC information criteria. Results for models with homogeneous variances as well as models with heterogeneity of variance in the same pattern as the means are reported.
Jmasm20: Exact Permutation Critical Values For The Kruskal-Wallis One-Way Anova, Justice I. Odiase, Sunday M. Ogbonmwan
Jmasm20: Exact Permutation Critical Values For The Kruskal-Wallis One-Way Anova, Justice I. Odiase, Sunday M. Ogbonmwan
Journal of Modern Applied Statistical Methods
The exhaustive enumeration of all the permutations of the observations in an experiment is the only possible way of truly constructing exact tests of significance. The permutation paradigm requires no distributional assumptions and works well with values that are normal, almost normal and non-normally distributed. The Kruskal-Wallis test does not require the assumptions that the samples are from normal populations and that the samples have the same standard deviation. In this article, the exact permutation distribution of the Kruskal-Wallis test statistic is generated empirically by actually obtaining all the distinct permutations of an experiment. The tables of exact critical values …
Quasi-Maximum Likelihood Estimation For Latent Variable Models With Mixed Continuous And Polytomous Data, Jens C. Eickhoff
Quasi-Maximum Likelihood Estimation For Latent Variable Models With Mixed Continuous And Polytomous Data, Jens C. Eickhoff
Journal of Modern Applied Statistical Methods
Latent variable modeling is a multivariate technique commonly used in the social and behavioral sciences. The models used in such analysis relate all observed variables to latent common factors. In many situations, however, some outcome variables are in polytomous form while other outcomes are measured on a continuous scale. Maximum likelihood estimation for latent variable models with mixed polytomous and continuous outcomes is computationally intensive and may become difficult to implement in many applications. In this article, a computationally practical, yet efficient, Quasi- Maximum Likelihood approach for latent variable models with mixed continuous and polytomous variables is proposed. Asymptotic properties …
End Matter, Jmasm Editors
End Matter, Jmasm Editors
Journal of Modern Applied Statistical Methods
No abstract provided.
A Review Of Stata 9.0, Joseph Hilbe
A Single, Powerful, Nonparametric Statistic For Continuous-Data Telecommunications Parity Testing, J. D. Opdyke
A Single, Powerful, Nonparametric Statistic For Continuous-Data Telecommunications Parity Testing, J. D. Opdyke
Journal of Modern Applied Statistical Methods
Since the enactment of the Telecommunications Act of 1996, extensive expert testimony has justified use of the modified t statistic (Brownie et al., 1990) for performing two-sample hypothesis tests comparing Bell companies’ CLEC and ILEC performance measurement data (known as parity testing). However, Opdyke (Telecommunications Policy, 2004) demonstrated this statistic to be potentially manipulable and to have literally zero power to detect inferior CLEC service provision under a wide range of relevant data conditions. This article develops a single, nonparametric statistic that is easily implemented (i.e., not computationally intensive) and typically provides dramatic power gains over the modified t while …
An Estimator Of Intervention Effect On Disease Severity, David Siev
An Estimator Of Intervention Effect On Disease Severity, David Siev
Journal of Modern Applied Statistical Methods
When a medical intervention prevents a dichotomous outcome, the size of its effect is often estimated with the prevented fraction. Some interventions may reduce the severity of an outcome without entirely preventing it. To quantify the effect of a severity-moderating intervention, a measure termed the mitigated fraction (MF) is proposed. MF has broad applicability, because it measures the overlap of two empirical distributions based on their stochastic ordering. It is also useful in the specific context of medical interventions, because it shares certain structural and functional features with the prevented fraction. The two measures may be applied together …
Comparison Of Statistical Tests In Logistic Regression: The Case Of Hypernatreamia, Stylianos Katsaragakis, Christos Koukouvinos, Stella Stylianou, Eleni-Maria Theodoraki, Eleni-Maria Theodoraki
Comparison Of Statistical Tests In Logistic Regression: The Case Of Hypernatreamia, Stylianos Katsaragakis, Christos Koukouvinos, Stella Stylianou, Eleni-Maria Theodoraki, Eleni-Maria Theodoraki
Journal of Modern Applied Statistical Methods
The logistic regression has become an integral component of any medical data analysis concerning binary responses. The main issue rising after the adaptation of the final model is its goodness-of-fit. The fit of the model is assessed via the overall measures and summary statistics and comparing them in the case of hypernateamia.
Simulation Procedure In Periodic Cancer Screening Trials, Ioana Barnicescu, Ricolindo L. Cariño
Simulation Procedure In Periodic Cancer Screening Trials, Ioana Barnicescu, Ricolindo L. Cariño
Journal of Modern Applied Statistical Methods
A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from diseasefree state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood …
Statistical Model And Estimation Of The Optimum Price For A Chain Of Price Setting Firms, Chengjie Xiong, Kejun Zhu
Statistical Model And Estimation Of The Optimum Price For A Chain Of Price Setting Firms, Chengjie Xiong, Kejun Zhu
Journal of Modern Applied Statistical Methods
A stochastic approach is used to model the economics of a chain of price setting firms. It is assumed that these firms have fixed capacities in their products, but random demands for their products. The optimum price, the optimum revenue, and the expected marginal revenue at a given price are investigated. The method of maximum likelihood is used to provide both point and confidence interval estimates. The coverage probabilities of confidence interval estimates based on a simulation study are presented.
Training Statisticians To Be Alert To The Dangers Of Misapplying Statistical Methods, Vance W. Berger
Training Statisticians To Be Alert To The Dangers Of Misapplying Statistical Methods, Vance W. Berger
Journal of Modern Applied Statistical Methods
Statisticians are faced with a variety of challenges. Their ability to cope successfully with these challenges depends, in large part, on the quality of their training. It is not the purpose of this article to present a comprehensive training plan that will overhaul the standard curriculum a statistician might follow under current training regimens (i.e., in a degree program). Rather, the objective is to point out important areas that appear to be under-represented in standard curricula and correspondingly overlooked too often in practice. The hope is that these areas might be better integrated into the training of the next generation …
Inferences About The Components Of A Generalized Additive Model, Rand R. Wilcox
Inferences About The Components Of A Generalized Additive Model, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
A method for making inferences about the components of a generalized additive model is described. It is found that a variation of the method, based on means, performs well in simulations. Unlike many other inferential methods, switching from a mean to a 20% trimmed mean was found to offer little or no advantage in terms of both power and controlling the probability of a Type I error.