Vor Ort
Merkzettel Warenkorb
Produktbild: Advances in Statistical Decision Theory and Applications

Advances in Statistical Decision Theory and Applications

Aus der Reihe Statistics for Industry and Technology

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.09.2011

Abbildungen

498 p.

Herausgeber

S. Panchapakesan + weitere

Verlag

Birkhäuser Boston

Seitenzahl

498

Maße (L/B/H)

25,4/17,8/2,8 cm

Gewicht

938 g

Auflage

Softcover reprint of the original 1st edition 1997

Sprache

Englisch

ISBN

978-1-4612-7495-7

Beschreibung

Portrait

N. Balakrishnan is an Associate Director and Professor at Department of Aerospace Engineering and Supercomputer Edu- cation and Research Centre, Indian Institute of Science. His research interests include numerical electromagnetic, multi-parameter radars, and signal processing. His publications include 19 books and many peer-reviewed journal papers.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.09.2011

Abbildungen

498 p.

Herausgeber

Verlag

Birkhäuser Boston

Seitenzahl

498

Maße (L/B/H)

25,4/17,8/2,8 cm

Gewicht

938 g

Auflage

Softcover reprint of the original 1st edition 1997

Sprache

Englisch

ISBN

978-1-4612-7495-7

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Advances in Statistical Decision Theory and Applications
  • 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.