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You searched for: Publication Year 2013 Remove constraint Publication Year: 2013 Source 2013 v.108 no.501 Remove constraint Source: 2013 v.108 no.501 Subject equations Remove constraint Subject: equations
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1. A Unified Approach to Semiparametric Transformation Models Under General Biased Sampling Schemes

Author:
Kim, Jane Paik; Lu, Wenbin; Sit, Tony; Ying, Zhiliang
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 217-227
ISSN:
1537-274X
Subject:
data collection; equations; models; variance
Abstract:
... We propose a unified estimation method for semiparametric linear transformation models under general biased sampling schemes. The new estimator is obtained from a set of counting process-based unbiased estimating equations, developed through introducing a general weighting scheme that offsets the sampling bias. The usual asymptotic properties, including consistency and asymptotic normality, are es ...
DOI:
10.1080/01621459.2012.746073
PubMed:
23667280
PubMed Central:
PMC3649773

2. Distribution-Free Prediction Sets

Author:
Lei, Jing; Robins, James; Wasserman, Larry
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 278-287
ISSN:
1537-274X
Subject:
data analysis; equations; prediction
Abstract:
... This article introduces a new approach to prediction by bringing together two different nonparametric ideas: distribution-free inference and nonparametric smoothing. Specifically, we consider the problem of constructing nonparametric tolerance/prediction sets. We start from the general conformal prediction approach, and we use a kernel density estimator as a measure of agreement between a sample p ...
DOI:
10.1080/01621459.2012.751873
PubMed:
25237208
PubMed Central:
PMC4164906

3. Extending the State-Space Model to Accommodate Missing Values in Responses and Covariates

Author:
Naranjo, Arlene; Trindade, A. Alexandre; Casella, George
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 202-216
ISSN:
1537-274X
Subject:
algorithms; data collection; elderly; equations; lung function; models; patients; statistics
Abstract:
... This article proposes an extended state-space model for accommodating multivariate panel data. The novel aspect of this contribution is an adjustment to the classical model for multiple subjects that allows missingness in the covariates in addition to the responses. Missing covariate data are handled by a second state-space model nested inside the first to represent unobserved exogenous informatio ...
DOI:
10.1080/01621459.2012.746066

4. Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent

Author:
Fu, Fei; Zhou, Qing
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 288-300
ISSN:
1537-274X
Subject:
algorithms; data analysis; equations; models
Abstract:
... Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures, and the presence of equivalence classes. In this article, we develop an L ₁-penalized likelihood approach to estimate the structure of causal Gaussian networks. ...
DOI:
10.1080/01621459.2012.754359

5. On a Principal Varying Coefficient Model

Author:
Jiang, Qian; Wang, Hansheng; Xia, Yingcun; Jiang, Guohua
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 228-236
ISSN:
1537-274X
Subject:
data analysis; equations; models
Abstract:
... We propose a novel varying coefficient model (VCM), called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional VCM, PVCM reduces the actual number of nonparametric functions and thus has better estimation efficiency. Compared with the semivarying coefficient model (SVCM), PVC ...
DOI:
10.1080/01621459.2012.736904

6. On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models

Author:
Feng, Zhenghui; Wen, Xuerong Meggie; Yu, Zhou; Zhu, Lixing
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 237-246
ISSN:
1537-274X
Subject:
data analysis; equations; models
Abstract:
... Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial dimension reduction estimation for predictors of primary interest without assuming that the remaining p ...
DOI:
10.1080/01621459.2012.746065

7. On Sampling Strategies in Bayesian Variable Selection Problems With Large Model Spaces

Author:
García-donato, G.; Martínez-beneito, M. A.
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 340-352
ISSN:
1537-274X
Subject:
Bayesian theory; data analysis; equations; models
Abstract:
... One important aspect of Bayesian model selection is how to deal with huge model spaces, since the exhaustive enumeration of all the models entertained is not feasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem with a moderately large number of possible explanatory variables considered in this article. We revie ...
DOI:
10.1080/01621459.2012.742443

8. Selection Adjusted Confidence Intervals With More Power to Determine the Sign

Author:
Weinstein, Asaf; Fithian, William; Benjamini, Yoav
Source:
Journal of the American Statistical Association 2013 v.108 no.501 pp. 165-176
ISSN:
1537-274X
Subject:
brain; confidence interval; equations
Abstract:
... In many current large-scale problems, confidence intervals (CIs) are constructed only for the parameters that are large, as indicated by their estimators, ignoring the smaller parameters. Such selective inference poses a problem to the usual marginal CIs that no longer offer the right level of coverage, not even on the average over the selected parameters. We address this problem by developing thr ...
DOI:
10.1080/01621459.2012.737740