Topics in Survey Sampling: Lecture Notes in Statistics, cartea 153
Autor Parimal Mukhopadhyayen Limba Engleză Paperback – 14 sep 2000
Din seria Lecture Notes in Statistics
- 17%
Preț: 333.93 lei - 15%
Preț: 607.49 lei -
Preț: 371.20 lei - 15%
Preț: 495.91 lei - 15%
Preț: 618.19 lei - 18%
Preț: 909.21 lei -
Preț: 367.49 lei - 15%
Preț: 609.08 lei -
Preț: 371.97 lei - 15%
Preț: 616.64 lei - 20%
Preț: 607.16 lei - 15%
Preț: 614.90 lei - 15%
Preț: 608.79 lei - 15%
Preț: 633.43 lei - 18%
Preț: 1183.54 lei - 18%
Preț: 907.64 lei -
Preț: 368.79 lei - 18%
Preț: 905.13 lei - 15%
Preț: 615.97 lei - 18%
Preț: 906.03 lei -
Preț: 368.59 lei - 15%
Preț: 608.90 lei - 15%
Preț: 611.12 lei -
Preț: 378.78 lei - 15%
Preț: 675.70 lei - 15%
Preț: 619.75 lei - 15%
Preț: 620.23 lei -
Preț: 367.85 lei - 15%
Preț: 611.74 lei - 15%
Preț: 622.91 lei -
Preț: 366.19 lei - 15%
Preț: 609.85 lei - 15%
Preț: 623.70 lei -
Preț: 364.56 lei - 15%
Preț: 623.52 lei - 15%
Preț: 622.59 lei - 18%
Preț: 750.16 lei - 15%
Preț: 616.45 lei - 18%
Preț: 1059.82 lei - 15%
Preț: 618.34 lei -
Preț: 370.10 lei - 15%
Preț: 615.66 lei - 15%
Preț: 625.26 lei - 15%
Preț: 616.95 lei - 15%
Preț: 613.49 lei - 15%
Preț: 619.45 lei
Preț: 374.14 lei
Puncte Express: 561
Preț estimativ în valută:
66.21€ • 77.38$ • 57.49£
66.21€ • 77.38$ • 57.49£
Carte tipărită la comandă
Livrare economică 20 februarie-06 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780387951089
ISBN-10: 0387951083
Pagini: 292
Ilustrații: XI, 292 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.44 kg
Ediția:Softcover reprint of the original 1st ed. 2001
Editura: Springer
Colecția Springer
Seria Lecture Notes in Statistics
Locul publicării:New York, NY, United States
ISBN-10: 0387951083
Pagini: 292
Ilustrații: XI, 292 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.44 kg
Ediția:Softcover reprint of the original 1st ed. 2001
Editura: Springer
Colecția Springer
Seria Lecture Notes in Statistics
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
1 The Basic Concepts.- 1.1 Introduction.- 1.2 The Fixed Population model.- 1.3 Different Types of Sampling Designs.- 1.4 The Estimators.- 1.5 Some Inferential Problems under Fixed Population Set-Up.- 1.6 Plan of the Book.- 2 Inference under Frequentist Theory Approach.- 2.1 Introduction.- 2.2 Principles of Inference Based on Theory of Prediction.- 2.3 Robustness of Model-Dependent Optimal Strategies.- 2.4 A Class of Predictors under Model ?(X, v).- 2.5 Asymptotic Unbiased Estimation of Design-Variance of $${{\hat{T}}_{{GR}}}$$.- 3 Bayes and Empirical Bayes Prediction of a Finite Population Total.- 3.1 Introduction.- 3.2 Bayes and Minimax Prediction of Finite Population Parameters.- 3.3 Bayes Prediction of a Finite Population Total under Normal Regression Model.- 3.4 Bayes Prediction under an Asymmetric Loss Function.- 3.5 James-Stein Estimator and Associated Estimators.- 3.6 Empirical Bayes Prediction of Population Total under Simple Location Model.- 3.7 EB-Prediction under Normal Model using Covariates.- 3.8 Applications in Small Area Estimation.- 3.9 Bayes Prediction under Random Error Variance Model.- 3.10 Exercises.- 4 Modifications of Bayes Procedure.- 4.1 Introduction.- 4.2 Linear Bayes Prediction.- 4.3 Restricted Linear Bayes Prediction.- 4.4 Constrained Bayes Prediction.- 4.5 Bayesian Robustness under a Class of Alternative Models.- 4.6 Robust Bayes Estimation under Contaminated Priors.- 4.7 Exercises.- 5 Estimation of Finite Population Variance, Regression Coefficient.- 5.1 Introduction.- 5.2 Design-Based Estimation of a Finite Population Variance.- 5.3 Model-Based Prediction of V.- 5.4 Bayes Prediction of V(y).- 5.5 Asymptotic Properties of Sample Regression Coefficient.- 5.6 PM-Unbiased Estimation of Slope Parameters in the Linear Regression Model.- 5.7Optimal Prediction of Finite Population Regression Coefficient under Multiple Regression Model.- 5.8 Exercises.- 6 Estimation of a Finite Population Distribution Function.- 6.1 Introduction.- 6.2 Design-Based Estimators.- 6.3 Model-Based Predictors.- 6.4 Conditional Approach.- 6.5 Asymptotic Properties of the Estimators.- 6.6 Non-Parametric Kernel Estimators.- 6.7 Desirable Properties of an Estimator.- 6.8 Empirical Studies.- 6.9 Best Unbiased Prediction (BUP) under Gaussian Superpopulation Model.- 6.10 Estimation of Median.- 7 Prediction in Finite Population under Measurement Error Models.- 7.1 Introduction.- 7.2 Additive Measurement Error Models.- 7.3 Prediction under Multiplicative Error-in-Variables Model.- 7.4 Exercises.- 8 Miscellaneous Topics.- 8.1 Introduction.- 8.2 Calibration Estimators.- 8.3 Post-Stratification.- 8.4 Design-Based Conditional Unbiasedness.- 8.5 Exercises.- References.- Author Index.