Advances in Statistical Decision Theory and Applications: Statistics for Industry and Technology
Editat de S. Panchapakesan, N. Balakrishnanen Limba Engleză Paperback – 15 sep 2011
Din seria Statistics for Industry and Technology
- 18% Preț: 913.98 lei
- Preț: 385.67 lei
- 18% Preț: 907.08 lei
- Preț: 383.65 lei
- 18% Preț: 1174.64 lei
- 18% Preț: 1168.67 lei
- 15% Preț: 616.97 lei
- 5% Preț: 1063.88 lei
- 18% Preț: 921.90 lei
- 18% Preț: 963.31 lei
- 18% Preț: 914.16 lei
- 15% Preț: 616.84 lei
- 15% Preț: 621.00 lei
- Preț: 399.32 lei
- 18% Preț: 1178.99 lei
- Preț: 379.64 lei
- Preț: 376.53 lei
- 18% Preț: 1050.01 lei
- Preț: 388.40 lei
- 15% Preț: 606.33 lei
- Preț: 379.27 lei
- 15% Preț: 629.99 lei
- 18% Preț: 696.66 lei
- 15% Preț: 626.14 lei
- 18% Preț: 912.05 lei
- 18% Preț: 1072.40 lei
- 18% Preț: 1064.79 lei
- 18% Preț: 915.47 lei
Preț: 385.56 lei
Nou
Puncte Express: 578
Preț estimativ în valută:
73.81€ • 80.40$ • 61.92£
73.81€ • 80.40$ • 61.92£
Carte tipărită la comandă
Livrare economică 18 decembrie 24 - 01 ianuarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781461274957
ISBN-10: 1461274958
Pagini: 504
Ilustrații: 498 p.
Dimensiuni: 178 x 254 x 26 mm
Greutate: 0.87 kg
Ediția:Softcover reprint of the original 1st ed. 1997
Editura: Birkhäuser Boston
Colecția Birkhäuser
Seria Statistics for Industry and Technology
Locul publicării:Boston, MA, United States
ISBN-10: 1461274958
Pagini: 504
Ilustrații: 498 p.
Dimensiuni: 178 x 254 x 26 mm
Greutate: 0.87 kg
Ediția:Softcover reprint of the original 1st ed. 1997
Editura: Birkhäuser Boston
Colecția Birkhäuser
Seria Statistics for Industry and Technology
Locul publicării:Boston, MA, United States
Public țintă
ResearchCuprins
I: Bayesian Inference.- 1 Bayes for Beginners? Some Pedagogical Questions.- 2 Normal Means Revisited.- 3 Bayes m-Truncated Sampling Allocations for Selecting the Best Bernoulli Population.- 4 On Hierarchical Bayesian Estimation and Selection for Multivariate Hypergeometric Distributions.- 5 Convergence Rates of Empirical Bayes Estimation and Selection for Exponential Populations With Location Parameters.- 6 Empirical Bayes Rules for Selecting the Best Uniform Populations.- II: Decision Theory.- 7 Adaptive Multiple Decision Procedures for Exponential Families.- 8 Non-Informative Priors Via Sieves and Packing Numbers.- III: Point And Interval Estimation—Classical Approach.- 9 From Neyman’s Frequentism to the Frequency Validity in the Conditional Inference.- 10 Asymptotic Theory for the Simex Estimator in Measurement Error Models.- 11 A Change Point Problem for Some Conditional Functionals.- 12 On Bias Reduction Methods in Nonparametric Regression Estimation.- 13 Multiple Comparisons With the Mean.- IV: Tests Of Hypotheses.- 14 Properties of Unified Bayesian-Frequentist Tests.- 15 Likelihood Ratio Tests and Intersection-Union Tests.- 16 The Large Deviation Principle for Common Statistical Tests Against a Contaminated Normal.- 17 Multiple Decision Procedures for Testing Homogeneity of Normal Means With Unequal Unknown Variances.- V: Ranking and Selection.- 18 A Sequential Multinomial Selection Procedure With Elimination.- 19 An Integrated Formulation for Selecting the Best From Several Normal Populations in Terms of the Absolute Values of Their Means: Common Known Variance Case.- 20 Applications of Two Majorization Inequalities to Ranking and Selection Problems.- VI: Distributions AND Applications.- 21 Correlation Analysis of Ordered Observations From aBlock-Equicorrelated Multivariate Normal Distribution.- 22 On Distributions With Periodic Failure Rate and Related Inference Problems.- 23 Venn Diagrams, Coupon Collections, Bingo Games and Dirichlet Distributions.- VII: Industrial Applications.- 24 Control Charts for Autocorrelated Process Data.- 25 Reconstructive Estimation in a Parametric Random Censorship Model With Incomplete Data.- 26 A Review of the Gupta-Sobel Subset Selection Rule for Binomial Populations With Industrial Applications.- 27 The Use of Subset Selection in Combined-Array Experiments to Determine Optimal Product or Process Designs.- 28 Large-Sample Approximations to Best Linear Unbiased Estimation and Best Linear Unbiased Prediction Based on Progressively Censored Samples and Some Applications.