Pub Date : 2020-06-07DOI: 10.22237/JMASM/1556670000
Jang-Dong Seo
Seo, JangDong. Joint models of longitudinal outcomes and informative time. Published Doctor of Philosophy Dissertation, University of Northern Colorado, 2015 In longitudinal data analyses, it is commonly assumed that time intervals for collecting outcomes are predetermined – the same across all subjects – and have no information regarding the measured variables. However, in practice researchers might occasionally have irregular time intervals and informative time, which violate the above assumptions. Hence, if traditional statistical methods are used for this situation, the results would be biased. In this study, as a solution, joint models of longitudinal outcomes and informative time are presented by using joint probability distributions, incorporating the relationships between outcomes and time. The joint models are designed to handle outcome distributions from a member of the exponential family of distributions with informative time following an exponential distribution. For instance, the Poisson probability density function is combined with the exponential distribution for count data, as well as the relations between outcomes and time; the Bernoulli probability density function is combined for binary data; and the Gamma probability density function is combined when the outcome is waiting time or survival time. The maximum likelihood parameter estimates of the joint model are found by using a nonlinear optimization method, and the asymptotic behaviors of the estimators are studied. Moreover, the likelihood ratio test statistic is computed for comparing nested models, and the model selection criteria, such as AIC, AICc, BIC, are found as well.
{"title":"Joint Models of Longitudinal Outcomes and Informative Time","authors":"Jang-Dong Seo","doi":"10.22237/JMASM/1556670000","DOIUrl":"https://doi.org/10.22237/JMASM/1556670000","url":null,"abstract":"Seo, JangDong. Joint models of longitudinal outcomes and informative time. Published Doctor of Philosophy Dissertation, University of Northern Colorado, 2015 In longitudinal data analyses, it is commonly assumed that time intervals for collecting outcomes are predetermined – the same across all subjects – and have no information regarding the measured variables. However, in practice researchers might occasionally have irregular time intervals and informative time, which violate the above assumptions. Hence, if traditional statistical methods are used for this situation, the results would be biased. In this study, as a solution, joint models of longitudinal outcomes and informative time are presented by using joint probability distributions, incorporating the relationships between outcomes and time. The joint models are designed to handle outcome distributions from a member of the exponential family of distributions with informative time following an exponential distribution. For instance, the Poisson probability density function is combined with the exponential distribution for count data, as well as the relations between outcomes and time; the Bernoulli probability density function is combined for binary data; and the Gamma probability density function is combined when the outcome is waiting time or survival time. The maximum likelihood parameter estimates of the joint model are found by using a nonlinear optimization method, and the asymptotic behaviors of the estimators are studied. Moreover, the likelihood ratio test statistic is computed for comparing nested models, and the model selection criteria, such as AIC, AICc, BIC, are found as well.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"32"},"PeriodicalIF":0.0,"publicationDate":"2020-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43670512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-05DOI: 10.22237/jmasm/1556669700
Youn-Jeng Choi, A. Cohen
The effects of three scale identification constraints in mixture IRT models were studied. A simulation study found no constraint effect on the mixture Rasch and mixture 2PL models, but the item anchoring constraint was the only one that worked well on selecting correct model with the mixture 3PL model.
{"title":"Comparison of Scale Identification Methods in Mixture IRT Models","authors":"Youn-Jeng Choi, A. Cohen","doi":"10.22237/jmasm/1556669700","DOIUrl":"https://doi.org/10.22237/jmasm/1556669700","url":null,"abstract":"The effects of three scale identification constraints in mixture IRT models were studied. A simulation study found no constraint effect on the mixture Rasch and mixture 2PL models, but the item anchoring constraint was the only one that worked well on selecting correct model with the mixture 3PL model.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"31"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49351079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.22237/jmasm/1556670240
R. Wilcox
For a binary random variable Y, let p(x) = P(Y = 1 | X = x) for some covariate X. The goal of computing a confidence interval for p(x) is considered. In the logistic regression model, even a slight departure difficult to detect via a goodness-of-fit test can yield inaccurate results. The accuracy of a confidence interval can deteriorate as the sample size increases. The goal is to suggest an alternative approach based on a smoother, which provides a more flexible approximation of p(x).
{"title":"Inferences About the Probability of Success, Given the Value of a Covariate, Using a Nonparametric Smoother","authors":"R. Wilcox","doi":"10.22237/jmasm/1556670240","DOIUrl":"https://doi.org/10.22237/jmasm/1556670240","url":null,"abstract":"For a binary random variable Y, let p(x) = P(Y = 1 | X = x) for some covariate X. The goal of computing a confidence interval for p(x) is considered. In the logistic regression model, even a slight departure difficult to detect via a goodness-of-fit test can yield inaccurate results. The accuracy of a confidence interval can deteriorate as the sample size increases. The goal is to suggest an alternative approach based on a smoother, which provides a more flexible approximation of p(x).","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"29"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42511646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.22237/jmasm/1556670420
R. Wilcox
There is an extensive literature dealing with inferences about the probability of success. A minor goal in this note is to point out when certain recommended methods can be unsatisfactory when the sample size is small. The main goal is to report results on the two-sample case. Extant results suggest using one of four methods. The results indicate when computing a 0.95 confidence interval, two of these methods can be more satisfactory when dealing with small sample sizes.
{"title":"A Note on Inferences About the Probability of Success","authors":"R. Wilcox","doi":"10.22237/jmasm/1556670420","DOIUrl":"https://doi.org/10.22237/jmasm/1556670420","url":null,"abstract":"There is an extensive literature dealing with inferences about the probability of success. A minor goal in this note is to point out when certain recommended methods can be unsatisfactory when the sample size is small. The main goal is to report results on the two-sample case. Extant results suggest using one of four methods. The results indicate when computing a 0.95 confidence interval, two of these methods can be more satisfactory when dealing with small sample sizes.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"30"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42000878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-22DOI: 10.22237/jmasm/1571745720
T. Sajobi, G. Fick, L. Lix
Discriminant function coefficients are useful for describing group differences and identi- fying variables that distinguish between groups. Test procedures were compared based on asymptotically approximations, empirical, and exact distributions for testing hypotheses about discriminant function coefficients. These tests are useful for assessing variable impor- tance in multivariate group designs.
{"title":"On Statistical Significance of Discriminant Function Coefficients","authors":"T. Sajobi, G. Fick, L. Lix","doi":"10.22237/jmasm/1571745720","DOIUrl":"https://doi.org/10.22237/jmasm/1571745720","url":null,"abstract":"Discriminant function coefficients are useful for describing group differences and identi- fying variables that distinguish between groups. Test procedures were compared based on asymptotically approximations, empirical, and exact distributions for testing hypotheses about discriminant function coefficients. These tests are useful for assessing variable impor- tance in multivariate group designs.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":" ","pages":"27"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49608861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-18DOI: 10.22237/jmasm/1568246400
H. P. Singh, A. Yadav
Recommended Citation Singh, Housila P. and Yadav, Anita (2020) "A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters," Journal of Modern Applied Statistical Methods: Vol. 18 : Iss. 1 , Article 26. DOI: 10.22237/jmasm/1568246400 Available at: https://digitalcommons.wayne.edu/jmasm/vol18/iss1/26
{"title":"A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters","authors":"H. P. Singh, A. Yadav","doi":"10.22237/jmasm/1568246400","DOIUrl":"https://doi.org/10.22237/jmasm/1568246400","url":null,"abstract":"Recommended Citation Singh, Housila P. and Yadav, Anita (2020) \"A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters,\" Journal of Modern Applied Statistical Methods: Vol. 18 : Iss. 1 , Article 26. DOI: 10.22237/jmasm/1568246400 Available at: https://digitalcommons.wayne.edu/jmasm/vol18/iss1/26","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"26"},"PeriodicalIF":0.0,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44567682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-15DOI: 10.22237/jmasm/1571545600
S. S. Desai, D. N. Kashid
Support vector machine (SVM) is used for estimation of regression parameters to modify the sum of cross products (Sp). It works well for some nonnormal error distributions. The performance of existing robust methods and the modified Sp is evaluated through simulated and real data. The results show the performance of the modified Sp is good.
{"title":"Support Vector Machine-based Modified Sp Statistic for Subset Selection with Non-Normal Error Terms","authors":"S. S. Desai, D. N. Kashid","doi":"10.22237/jmasm/1571545600","DOIUrl":"https://doi.org/10.22237/jmasm/1571545600","url":null,"abstract":"Support vector machine (SVM) is used for estimation of regression parameters to modify the sum of cross products (Sp). It works well for some nonnormal error distributions. The performance of existing robust methods and the modified Sp is evaluated through simulated and real data. The results show the performance of the modified Sp is good.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"24"},"PeriodicalIF":0.0,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42469239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-15DOI: 10.22237/jmasm/1571545660
Z. Anwar, Neetu Gupta, M. A. Khan, Qazi Azhad Jamal
Recommended Citation Anwar, Zaki; Gupta, Neetu; Khan, Mohd Akram Raza; and Jamal, Qazi Azhad (2020) "Recurrence Relations for Marginal and Joint Moment Generating Functions of Topp-Leone Generated Exponential Distribution based on Record Values and its Characterization," Journal of Modern Applied Statistical Methods: Vol. 18 : Iss. 1 , Article 25. DOI: 10.22237/jmasm/1571545660 Available at: https://digitalcommons.wayne.edu/jmasm/vol18/iss1/25
{"title":"Recurrence Relations for Marginal and Joint Moment Generating Functions of Topp-Leone Generated Exponential Distribution based on Record Values and its Characterization","authors":"Z. Anwar, Neetu Gupta, M. A. Khan, Qazi Azhad Jamal","doi":"10.22237/jmasm/1571545660","DOIUrl":"https://doi.org/10.22237/jmasm/1571545660","url":null,"abstract":"Recommended Citation Anwar, Zaki; Gupta, Neetu; Khan, Mohd Akram Raza; and Jamal, Qazi Azhad (2020) \"Recurrence Relations for Marginal and Joint Moment Generating Functions of Topp-Leone Generated Exponential Distribution based on Record Values and its Characterization,\" Journal of Modern Applied Statistical Methods: Vol. 18 : Iss. 1 , Article 25. DOI: 10.22237/jmasm/1571545660 Available at: https://digitalcommons.wayne.edu/jmasm/vol18/iss1/25","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"25"},"PeriodicalIF":0.0,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41756154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-15DOI: 10.22237/jmasm/1556670480
J. Levin, J. Ferron, Boris S. Gafurov
Detailed is a 20-year arduous journey to develop a statistically viable two-phase (AB) single-case two independent-samples randomization test procedure. The test is designed to compare the effectiveness of two different interventions that are randomly assigned to cases. In contrast to the unsatisfactory simulation results produced by an earlier proposed randomization test, the present test consistently exhibited acceptable Type I error control under various design and effect-type configurations, while at the same time possessing adequate power to detect moderately sized intervention-difference effects. Selected issues, applications, and a multiple-baseline extension of the two-sample test are discussed.
{"title":"An Improved Two Independent-Samples Randomization Test for Single-Case AB-Type Intervention Designs: A 20-Year Journey","authors":"J. Levin, J. Ferron, Boris S. Gafurov","doi":"10.22237/jmasm/1556670480","DOIUrl":"https://doi.org/10.22237/jmasm/1556670480","url":null,"abstract":"Detailed is a 20-year arduous journey to develop a statistically viable two-phase (AB) single-case two independent-samples randomization test procedure. The test is designed to compare the effectiveness of two different interventions that are randomly assigned to cases. In contrast to the unsatisfactory simulation results produced by an earlier proposed randomization test, the present test consistently exhibited acceptable Type I error control under various design and effect-type configurations, while at the same time possessing adequate power to detect moderately sized intervention-difference effects. Selected issues, applications, and a multiple-baseline extension of the two-sample test are discussed.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44676030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-14DOI: 10.22237/jmasm/1556669280
Yan Liu, Chanmin Kim, Amery Wu, P. Gustafson, Edward Kroc, B. Zumbo
To evaluate the performance of propensity score approaches for differential item functioning analysis, this simulation study was conducted to assess bias, mean square error, Type I error, and power under different levels of effect size and a variety of model misspecification conditions, including different types and missing patterns of covariates.
{"title":"Investigating the Performance of Propensity Score Approaches for Differential Item Functioning Analysis","authors":"Yan Liu, Chanmin Kim, Amery Wu, P. Gustafson, Edward Kroc, B. Zumbo","doi":"10.22237/jmasm/1556669280","DOIUrl":"https://doi.org/10.22237/jmasm/1556669280","url":null,"abstract":"To evaluate the performance of propensity score approaches for differential item functioning analysis, this simulation study was conducted to assess bias, mean square error, Type I error, and power under different levels of effect size and a variety of model misspecification conditions, including different types and missing patterns of covariates.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46942225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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