Statistical Methodologies最新文献

Introductory Chapter: Ramifications of Incomplete Knowledge 导论章:不完全知识的后果

Statistical Methodologies Pub Date : 2020-02-26 DOI: 10.5772/intechopen.86265

J. Hessling

{"title":"Introductory Chapter: Ramifications of Incomplete Knowledge","authors":"J. Hessling","doi":"10.5772/intechopen.86265","DOIUrl":"https://doi.org/10.5772/intechopen.86265","url":null,"abstract":"Mathematical statistics has long been widely practiced in many fields of science [1]. Nevertheless, statistical methods have remained remarkably intact ever since the pioneering work [2] of R.A. Fisher and his contemporary scientists early in the twentieth century. Recently however, it has been claimed that most scientific results are wrong [3], due to malpractice of statistical methods. Errors of that kind are not caused by imperfect methodology but rather, reflect lack of understanding and proper interpretation. In this introductory chapter, a different cause of errors is addressed—the ubiquitous practice of willful ignorance (WI) [4]. Usually it is applied with intent to remedy lack of knowledge and simplify or merely enable application of established statistical methods. Virtually all statistical approaches require complete statistical knowledge at some stage. In practice though, that can hardly ever be established. For instance, Bayes estimation relies upon prior knowledge. Any equal a priori probability assumption (“uninformed prior”) does hardly disguise some facts are not known, which may be grossly deceiving. Uniform distribution is a specific assumption like any other. Willful ignorance of that kind must not be confused with knowledge to which we associate some degree of confidence. It may be better to explore rather than ignore consequences of what is not known at all. That will require novel perspectives on how mathematical statistics is practiced, which is the scope of this book.","PeriodicalId":381809,"journal":{"name":"Statistical Methodologies","volume":"51 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114135240","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}

引用次数: 0

Asymptotic Normality of Hill’s Estimator under Weak Dependence 弱相关下Hill估计量的渐近正态性

Statistical Methodologies Pub Date : 2020-02-26 DOI: 10.5772/intechopen.84555

Y. Berkoun, K. Boualam

引用次数: 0

Methods of Russian Patent Analysis 俄罗斯专利分析方法

Statistical Methodologies Pub Date : 2020-02-26 DOI: 10.5772/intechopen.90136

D. Korobkin, S. Vasiliev, S. Fomenkov, S. Kolesnikov

引用次数: 0

Development of Estimation Procedure of Population Mean in Two-Phase Stratified Sampling 两阶段分层抽样总体均值估计方法的发展

Statistical Methodologies Pub Date : 2019-09-27 DOI: 10.5772/intechopen.82850

P. Parichha, K. Basu, A. Bandyopadhyay

引用次数: 0

A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications Erlang分布的极大似然估计与贝叶斯估计的比较研究及其应用

Statistical Methodologies Pub Date : 2019-09-27 DOI: 10.5772/intechopen.85627

Kaisar Ahmad, Sheikh Parvaiz Ahmad

引用次数: 3

Density Estimation in Inventory Control Systems under a Discounted Optimality Criterion 打折最优准则下库存控制系统的密度估计

Statistical Methodologies Pub Date : 2019-08-07 DOI: 10.5772/INTECHOPEN.88392

J. Minjárez‐Sosa

引用次数: 0

Surveying Sensitive Topics with Indirect Questioning 用间接提问调查敏感话题

Statistical Methodologies Pub Date : 2019-02-25 DOI: 10.5772/INTECHOPEN.84524

E. Oral

引用次数: 3

A Study on the Comparison of the Effectiveness of the Jackknife Method in the Biased Estimators 有偏估计中叠刀方法有效性的比较研究

Statistical Methodologies Pub Date : 2018-12-18 DOI: 10.5772/INTECHOPEN.82366

N. Yıldız

{"title":"A Study on the Comparison of the Effectiveness of the Jackknife Method in the Biased Estimators","authors":"N. Yıldız","doi":"10.5772/INTECHOPEN.82366","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82366","url":null,"abstract":"In this study, we proposed an alternative biased estimator. The linear regression model might lead to ill-conditioned design matrices because of the multicollinearity and thus result in inadequacy of the ordinary least squares estimator (OLS). Scientists have developed alternative estimation techniques that would eradicate the instability in the estimates. Several biased estimators such as Stein estimator, the ordinary ridge regression (ORR) estimator, the principal components regression (PCR) estimator. Liu developed a Liu estimator (LE) by combining the Stein estimator with the ORR estimator. Since both ORR and LE depend on OLS estimator, multicollinearity affects them both. Therefore, the ORR and LE may give misleading information in the presence of multicollinearity. To overcome this problem, Liu introduced a new estimator, which is based on k and d biasing parameters, the authors worked on developing an estimator that would still have the valuable characteristics of the Liu-type estimator (LTE) but have a smaller bias. We are proposing a modified jackknife Liu-type estimator (MJLTE) that was created by combining the ideas underlying both the LTE and JLTE. Under mean square error matrix criteria, the MJLTE is superior to Liu-type estimator (LTE) and jackknifed Liu-type estimator (JLTE). Finally, a real data example and a Monte Carlo simulation are also given to illustrate theoretical results.","PeriodicalId":381809,"journal":{"name":"Statistical Methodologies","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115115464","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}

引用次数: 0