Advances in Shrinkage and Penalized Estimation Strategies

Part I Shrinkage Estimation Strategies.- Chapter 1 Restricted Liu-Type Regression Estimators in Linear Regression Model.- Chapter 2 Shrinkage Strategies for Right-Censored Bell Regression Model with Application.- Chapter 3 On a Class of Shrinkage Estimators of Normal Mean in High-dimensional Data with Unknown Covariance.- Chapter 4 Some Stein-rules Methods in Tensor Regression Model with High-Dimensional Data.- Chapter 5 Some Implications of Preliminary-Test Estimation in the Context of Size-Biased Sampling.- Chapter 6 Study the Performance of New Shrinkage Estimators under the Balanced Loss Function.- Chapter 7 Shrinkage Estimators of the Location Parameter Under Modified Balanced Loss Functions.- Chapter 8 Shrinkage Strategies and Superefficiency.- Chapter 9 Shrinkage Estimation of Restricted Mean Vector Under Balanced Loss with Application inWavelet Denoising.- Chapter 10 On Minimaxity of Shrinkage Estimators Under Concave Loss.- Part II Penalized Estimation and Variable Selection.- Chapter 11 Improved LASSO Estimator in Semiparametric Linear Measurement Error Models.- Chapter 12 Weighted-Average Least Squares Estimation of Panel Data Models.- Chapter 13 Performance of Some Test Statistics for Testing the Regression Coefficients for the One and Two Parameters Multicollinear Gaussian Multiple Linear Regression Models: An Empirical Comparison.- Chapter 14 Ineffectiveness of Model Selection via t-Test in Regression with Collinearity.- Chapter 15 A New Ridge-Based Biased Prediction Technique in Linear Mixed Models.- Chapter 16 L-Estimation of Location: Shrinkage and Selection.- Chapter 17 Variable Selection in Regression Models with Dependent and Asymmetrically Distributed Error Term.- Part III Robust Estimation and Nonparametrics Methods.- Chapter 18 Shrinkage Estimator for Spatial Autoregressive Model with Endogenous Covariates.- Chapter 19 Regularization of Robust Neural Networks: Bayesian Connections and Outlier Detection.- Chapter 20 Estimating Finite Mixture Models Using Component Self-Paced Learning.- Chapter 21 Shrinkage Estimation in Generalized CIR Processes with Change-point.- Chapter 22 Estimating and Pretesting in Additive Censored Models.- Chapter 23 Confidence Interval for a Univariate Normal Mean Based on a Pretest Estimator.- Chapter 24 Prediction of Interruptions in Energy Supply: A Machine Learning Study with Post-Shrinkage Modeling.

• Erscheinungsdatum: 17.02.2026
• Autor/Autorin: Mohammad Arashi
• Reihe: Mathematics and Statistics (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 24.6 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 657 Seiten
• ISBN: 9783031940507
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
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