Pub Date : 2020-04-14DOI: 10.22237/jmasm/1556669460
Lili Yu, V. Sevilimedu, R. Vogel, H. Samawi
Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.
{"title":"Quasi-Likelihood Ratio Tests for Homoscedasticity in Linear Regression","authors":"Lili Yu, V. Sevilimedu, R. Vogel, H. Samawi","doi":"10.22237/jmasm/1556669460","DOIUrl":"https://doi.org/10.22237/jmasm/1556669460","url":null,"abstract":"Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44532014","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 : 2020-04-07DOI: 10.22237/jmasm/1556670060
J. Lloyd, Jacqui Boonstra, B. Forer, Rush Hershler, C. Milbrath, B. Poon, N. Razaz, Pippa Rowcliffe, K. Schonert-Reichl
Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.
{"title":"Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval","authors":"J. Lloyd, Jacqui Boonstra, B. Forer, Rush Hershler, C. Milbrath, B. Poon, N. Razaz, Pippa Rowcliffe, K. Schonert-Reichl","doi":"10.22237/jmasm/1556670060","DOIUrl":"https://doi.org/10.22237/jmasm/1556670060","url":null,"abstract":"Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"113 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141216174","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 : 2020-04-07DOI: 10.22237/jmasm/1556670060
J. Lloyd, Jacqui Boonstra, B. Forer, Rush Hershler, C. Milbrath, B. Poon, N. Razaz, Pippa Rowcliffe, K. Schonert-Reichl
Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.
{"title":"Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval","authors":"J. Lloyd, Jacqui Boonstra, B. Forer, Rush Hershler, C. Milbrath, B. Poon, N. Razaz, Pippa Rowcliffe, K. Schonert-Reichl","doi":"10.22237/jmasm/1556670060","DOIUrl":"https://doi.org/10.22237/jmasm/1556670060","url":null,"abstract":"Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"115 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141216139","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 : 2020-04-06DOI: 10.22237/jmasm/1556670300
Danjie Zou, J. Lloyd, J. Baumbusch
An introduction to using SPSS to analyze complex survey data is given. Key features of complex survey design are described briefly, including stratification, clustering, multiple stages, and weights. Then, annotated SPSS syntax for complex survey data analysis is presented to demonstrate the step-by-step process using real complex samples data.
{"title":"Using SPSS to Analyze Complex Survey Data: A Primer","authors":"Danjie Zou, J. Lloyd, J. Baumbusch","doi":"10.22237/jmasm/1556670300","DOIUrl":"https://doi.org/10.22237/jmasm/1556670300","url":null,"abstract":"An introduction to using SPSS to analyze complex survey data is given. Key features of complex survey design are described briefly, including stratification, clustering, multiple stages, and weights. Then, annotated SPSS syntax for complex survey data analysis is presented to demonstrate the step-by-step process using real complex samples data.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41575615","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 : 2020-04-06DOI: 10.22237/jmasm/1556669160
M. Abu-Shawiesh, Aamid Saghir
In this paper, three robust confidence intervals are proposed as alternatives to the Student t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s t confidence interval. Real-life data was used for illustration and performing a comparison that support the findings obtained from the simulation study.
{"title":"Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval","authors":"M. Abu-Shawiesh, Aamid Saghir","doi":"10.22237/jmasm/1556669160","DOIUrl":"https://doi.org/10.22237/jmasm/1556669160","url":null,"abstract":"In this paper, three robust confidence intervals are proposed as alternatives to the Student t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s t confidence interval. Real-life data was used for illustration and performing a comparison that support the findings obtained from the simulation study.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45892616","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 : 2020-04-01DOI: 10.22237/jmasm/1556670540
Miodrag Lovric
In frequentist statistics, point-null hypothesis testing based on significance tests and confidence intervals are harmonious procedures and lead to the same conclusion. This is not the case in the domain of the Bayesian framework. An inference made about the point-null hypothesis using Bayes factor may lead to an opposite conclusion if it is based on the Bayesian credible interval. Bayesian suggestions to test point-nulls using credible intervals are misleading and should be dismissed. A null hypothesized value may be outside a credible interval but supported by Bayes factor (a Type I conflict), or contrariwise, the null value may be inside a credible interval but not supported by the Bayes factor (Type II conflict). Two computer programs in R have been developed that confirm the existence of a countable infinite number of cases, for which Bayes credible intervals are not compatible with Bayesian hypothesis testing.
{"title":"Conflicts in Bayesian Statistics Between Inference Based on Credible Intervals and Bayes Factors","authors":"Miodrag Lovric","doi":"10.22237/jmasm/1556670540","DOIUrl":"https://doi.org/10.22237/jmasm/1556670540","url":null,"abstract":"In frequentist statistics, point-null hypothesis testing based on significance tests and confidence intervals are harmonious procedures and lead to the same conclusion. This is not the case in the domain of the Bayesian framework. An inference made about the point-null hypothesis using Bayes factor may lead to an opposite conclusion if it is based on the Bayesian credible interval. Bayesian suggestions to test point-nulls using credible intervals are misleading and should be dismissed. A null hypothesized value may be outside a credible interval but supported by Bayes factor (a Type I conflict), or contrariwise, the null value may be inside a credible interval but not supported by the Bayes factor (Type II conflict). Two computer programs in R have been developed that confirm the existence of a countable infinite number of cases, for which Bayes credible intervals are not compatible with Bayesian hypothesis testing.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41825812","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 : 2020-04-01DOI: 10.22237/jmasm/1556670180
Miodrag Lovric
The Jeffreys-Lindley paradox is the most quoted divergence between the frequentist and Bayesian approaches to statistical inference. It is embedded in the very foundations of statistics and divides frequentist and Bayesian inference in an irreconcilable way. This paradox is the Gordian Knot of statistical inference and Data Science in the Zettabyte Era. If statistical science is ready for revolution confronted by the challenges of massive data sets analysis, the first step is to finally solve this anomaly. For more than sixty years, the Jeffreys-Lindley paradox has been under active discussion and debate. Many solutions have been proposed, none entirely satisfactory. The Jeffreys-Lindley paradox and its extent have been frequently misunderstood by many statisticians and non-statisticians. This paper aims to reassess this paradox, shed new light on it, and indicates how often it occurs in practice when dealing with Big data.
{"title":"On the Authentic Notion, Relevance, and Solution of the Jeffreys-Lindley Paradox in the Zettabyte Era","authors":"Miodrag Lovric","doi":"10.22237/jmasm/1556670180","DOIUrl":"https://doi.org/10.22237/jmasm/1556670180","url":null,"abstract":"The Jeffreys-Lindley paradox is the most quoted divergence between the frequentist and Bayesian approaches to statistical inference. It is embedded in the very foundations of statistics and divides frequentist and Bayesian inference in an irreconcilable way. This paradox is the Gordian Knot of statistical inference and Data Science in the Zettabyte Era. If statistical science is ready for revolution confronted by the challenges of massive data sets analysis, the first step is to finally solve this anomaly. For more than sixty years, the Jeffreys-Lindley paradox has been under active discussion and debate. Many solutions have been proposed, none entirely satisfactory. The Jeffreys-Lindley paradox and its extent have been frequently misunderstood by many statisticians and non-statisticians. This paper aims to reassess this paradox, shed new light on it, and indicates how often it occurs in practice when dealing with Big data.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44242968","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 : 2020-03-25DOI: 10.22237/JMASM/1556669340
K. Månsson, B. G. Kibria, G. Shukur
A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived. The new estimator may have a lower MSE than the traditional estimators. It was shown using simulation techniques the new shrinkage estimator outperforms the commonly used estimators in the presence of multicollinearity.
{"title":"A New Liu Type of Estimator for the Restricted SUR Estimator","authors":"K. Månsson, B. G. Kibria, G. Shukur","doi":"10.22237/JMASM/1556669340","DOIUrl":"https://doi.org/10.22237/JMASM/1556669340","url":null,"abstract":"A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived. The new estimator may have a lower MSE than the traditional estimators. It was shown using simulation techniques the new shrinkage estimator outperforms the commonly used estimators in the presence of multicollinearity.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42770533","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 : 2020-03-25DOI: 10.22237/JMASM/1571659200
A. Counsell, R. Chalmers, R. Cribbie
Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM.
{"title":"Comparing Means under Heteroscedasticity and Nonnormality: Further Exploring Robust Means Modeling","authors":"A. Counsell, R. Chalmers, R. Cribbie","doi":"10.22237/JMASM/1571659200","DOIUrl":"https://doi.org/10.22237/JMASM/1571659200","url":null,"abstract":"Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45700356","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 : 2020-03-25DOI: 10.22237/jmasm/1556668860
G. Baird, S. Bieber
Differences between the multiple linear regression model with Corrected R2 and Corrected F and the ordered variable regression model with R2 and F when intercorrelation is present are illustrated with simulated and real-world data.
{"title":"Sampling the Porridge: A Comparison of Ordered Variable Regression with F and R2 and Multiple Linear Regression with Corrected F and R2 in the Presence of Multicollinearity","authors":"G. Baird, S. Bieber","doi":"10.22237/jmasm/1556668860","DOIUrl":"https://doi.org/10.22237/jmasm/1556668860","url":null,"abstract":"Differences between the multiple linear regression model with Corrected R2 and Corrected F and the ordered variable regression model with R2 and F when intercorrelation is present are illustrated with simulated and real-world data.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68275789","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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