Statistical Paradigms

Recent Advances and Reconciliations

Nonfiction, Science & Nature, Mathematics, Probability, Applied, Statistics
Cover of the book Statistical Paradigms by Ashis SenGupta, Tapas Samanta, Ayanendranath Basu, World Scientific Publishing Company
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Author: Ashis SenGupta, Tapas Samanta, Ayanendranath Basu ISBN: 9789814644112
Publisher: World Scientific Publishing Company Publication: October 3, 2014
Imprint: WSPC Language: English
Author: Ashis SenGupta, Tapas Samanta, Ayanendranath Basu
ISBN: 9789814644112
Publisher: World Scientific Publishing Company
Publication: October 3, 2014
Imprint: WSPC
Language: English

This volume consists of a collection of research articles on classical and emerging Statistical Paradigms — parametric, non-parametric and semi-parametric, frequentist and Bayesian — encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability.

Contents:

  • Reviews:

    • Weak Paradoxes and Paradigms (Jayanta K Ghosh)
    • Nonparametrics in Modern Interdisciplinary Research: Some Perspectives and Prospectives (Pranab K Sen)
  • Parametric:

    • Bounds on Distributions Involving Partial, Marginal and Conditional Information: The Consequences of Incomplete Prior Specification (Barry C Arnold)
    • Stepdown Procedures Controlling a Generalized False Discovery Rate (Wenge Guo and Sanat K Sarkar)
    • On Confidence Intervals for Expected Response in 2n Factorial Experiments with Exponentially Distributed Response Variables (H V Kulkarni and S C Patil)
    • Predictive Influence of Variables in a Linear Regression Model when the Moment Matrix is Singular (Md Nurul Haque Mollah and S K Bhattacharjee)
    • New Wrapped Distributions — Goodness of Fit (A V Dattatreya Rao, I Ramabhadra Sarma and S V S Girija)
  • Semi-Parametric:

    • Non-Stationary Samples and Meta-Distribution (Dominique Guégan)
    • MDL Model Selection Criterion for Mixed Models with an Application to Spline Smoothing (Antti Liski and Erkki P Liski)
    • Digital Governance and Hotspot Geoinformatics with Continuous Fractional Response (G P Patil, S W Joshi and R E Koli)
    • Bayesian Curve Registration of Functional Data (Z Zhong, A Majumdar and R L Eubank)
  • Non-Parametric & Probability:

    • Nonparametric Estimation in a One-Way Error Component Model: A Monte Carlo Analysis (Daniel J Henderson and Aman Ullah)
    • GERT Analysis of Consecutive-k Systems: An Overview (Kanwar Sen, Manju Agarwal and Pooja Mohan)
    • Moment Bounds for Strong-Mixing Processes with Applications (Ratan Dasgupta)

Readership: Researchers, professionals and advanced students working on Bayesian and frequentist approaches to statistical modeling and on interfaces for both theory and applications.
Key Features:

  • A scholarly and motivating review of non-parametric methods by P K Sen, winner of the Wilks Medal in 2010
  • Discussion of paradoxes of the frequentist and Bayesian paradigms, related counterexamples, and their implications
  • Stands out in terms of the width and depth
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This volume consists of a collection of research articles on classical and emerging Statistical Paradigms — parametric, non-parametric and semi-parametric, frequentist and Bayesian — encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability.

Contents:

Readership: Researchers, professionals and advanced students working on Bayesian and frequentist approaches to statistical modeling and on interfaces for both theory and applications.
Key Features:

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