Literature DB >> 20640048

What's So Special About Semiparametric Methods?

Michael R Kosorok1.   

Abstract

The number of scientific publications on semiparametric methods per year has been steadily increasing since the early 1980s. This increased interest has happened in spite of the fact that the novelty of semiparametrics for its own sake has run its course, and semiparametric methods are by now considered classical. The underlying reasons for this continued interest include the genuine scientific utility of semiparametric models combined with the breadth and depth of the many theoretical questions that remain to be answered. Empirical process techniques are an essential research tool for many of these questions. Moreover, both semiparametric methods and empirical processes are playing an increasingly valuable role in high dimensional data analysis and in other emerging areas in statistics. The topics are very fruitful and intriguing for new researchers to engage in. Graduate programs in statistics, biostatistics and econometrics can and should include more empirical processes and semiparametrics in their teaching in order to ensure a sufficient supply of suitably qualified researchers.

Entities:  

Year:  2009        PMID: 20640048      PMCID: PMC2903063     

Source DB:  PubMed          Journal:  Sankhya Ser B        ISSN: 0581-5738


  9 in total

1.  Robust semiparametric microarray normalization and significance analysis.

Authors:  Shuangge Ma; Michael R Kosorok; Jian Huang; Hehuang Xie; Liliana Manzella; Marcelo Bento Soares
Journal:  Biometrics       Date:  2006-06       Impact factor: 2.571

2.  Nonparametric bayesian estimation of positive false discovery rates.

Authors:  Yongqiang Tang; Subhashis Ghosal; Anindya Roy
Journal:  Biometrics       Date:  2007-05-14       Impact factor: 2.571

3.  Variable selection for multivariate failure time data.

Authors:  Jianwen Cai; Jianqing Fan; Runze Li; Haibo Zhou
Journal:  Biometrika       Date:  2005       Impact factor: 2.445

4.  Current Methods for Recurrent Events Data with Dependent Termination: A Bayesian Perspective.

Authors:  Debajyoti Sinha; Tapabrata Maiti; Joseph G Ibrahim; Bichun Ouyang
Journal:  J Am Stat Assoc       Date:  2008-06-01       Impact factor: 5.033

5.  The Penalized Profile Sampler.

Authors:  Guang Cheng; Michael R Kosorok
Journal:  J Multivar Anal       Date:  2009-03-01       Impact factor: 1.473

6.  Sparse and Efficient Estimation for Partial Spline Models with Increasing Dimension.

Authors:  Guang Cheng; Hao Helen Zhang; Zuofeng Shang
Journal:  Ann Inst Stat Math       Date:  2015-02-01       Impact factor: 1.267

7.  Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data.

Authors:  Yi Li; Ross L Prentice; Xihong Lin
Journal:  Biometrika       Date:  2008-12       Impact factor: 2.445

8.  Univariate shrinkage in the cox model for high dimensional data.

Authors:  Robert J Tibshirani
Journal:  Stat Appl Genet Mol Biol       Date:  2009-04-14

9.  CASE-CONTROL SURVIVAL ANALYSIS WITH A GENERAL SEMIPARAMETRIC SHARED FRAILTY MODEL - A PSEUDO FULL LIKELIHOOD APPROACH.

Authors:  Malka Gorfine; David M Zucker; Li Hsu
Journal:  Ann Stat       Date:  2009       Impact factor: 4.028

  9 in total
  1 in total

Review 1.  Quantification in magnetic resonance spectroscopy based on semi-parametric approaches.

Authors:  Danielle Graveron-Demilly
Journal:  MAGMA       Date:  2013-07-28       Impact factor: 2.310

  1 in total

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