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Statistical Outliers and Related Topics

Statistical Outliers and Related Topics (Hardcover, 1)

CRC Press
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Statistical Outliers and Related Topics
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· 제목 : Statistical Outliers and Related Topics (Hardcover, 1) 
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781032460567
· 쪽수 : 514쪽
· 출판일 : 2025-01-27

목차

A Brief History of Statistical Outliers. Some Recent Developments in the Identification of Outliers in Spatial Data and Spatial Regression. Identification of Multiple Unusual Observations in Generalized Linear Models. The Conditional Breakdown Properties of LAD-LASSO Regression. Statistical Methods for Identifying Outliers in Univariate and Multivariate Data. Statistical Modeling of Maximum Rainfall in Guinea. Statistical Outliers and a New Generalized Probability Distribution with Applications. Outlier detection based on Range Statistic: Empirical Evaluation and Comparisons. Exponentiated Reduced Modified Weibull Distribution. Robust Weighted Random Forest Regression with Alternative Bootstrap. Robust Remedies for Multiple Regression Model in the Presence of High Leverage Collinearity-Influential Observations. Mixture Modeling of Car Insurance Claim in Zimbabwe. Examining Research in Statistics and Computer Science Literatures: The Need to Cross the Borders and to Adapt and Modernize the Statistics Discipline. On a Unit-EEG Quantile Regression Analysis. The cos-modified Lindley distribution: Properties, Estimation and Applications. The two-sided generalized Lomax distribution: estimation and application. Monitoring the Blood Levels of Animals using Control Charts. On Classical and Bayesian Inference for Bivariate Binomial Conditionals Distributions: Properties and Applications. Applications of the cos-modified Lindley distribution on Climate Data. Classification of Rainfall Regions in the U.K. Using Spatial Distances. A Novel Method of Identification of High Leverage Points in High Dimensional Sparse and Non-Sparse Data. Design of Control Chart using Gini-Process Capability Index and Resampling. Robust Measures of Goodness of Fit and Outlier Detection in Linear Regression Models.

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