11. |
Comment |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 100-102
MarkS. Handcock,
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ISSN:0162-1459
DOI:10.1080/01621459.1999.10473824
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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12. |
Comment |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 102-105
Michael Sherman,
Edward Carlstein,
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ISSN:0162-1459
DOI:10.1080/01621459.1999.10473825
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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13. |
Comment |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 106-107
MichaelL. Stein,
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ISSN:0162-1459
DOI:10.1080/01621459.1999.10473826
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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14. |
Rejoinder |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 107-110
SoumendraN. Lahiri,
MarkS. Kaiser,
Noel Cressie,
Nan-Jung Hsu,
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ISSN:0162-1459
DOI:10.1080/01621459.1999.10473827
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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15. |
Semiparametric Regression Models for Repeated Events with Random Effects and Measurement Error |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 111-124
Wenxin Jiang,
BruceW. Turnbull,
LarryC. Clark,
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摘要:
Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our approach to parameter estimation and significance testing is to start with a naive model of semiparametric Poisson process regression, and then to adjust for random effects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical trial for the prevention of recurrent skin tumors.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473828
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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16. |
Semiparametric Inference in the Proportional Odds Regression Model |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 125-136
Song Yang,
RossL. Prentice,
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摘要:
For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have desirable properties such as easy computation, asymptotic normality via a martingale analysis, and reliable asymptotic covariance estimation in closed form. Extensive numerical studies show that the minimumL2distance estimators have very good finite-sample behaviors compared to existing methods. Results of some simulation studies and applications to a real dataset are given. The weighted odds function–based approach also provides inference on the baseline odds function and some measures for lack-of-fit analysis.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473829
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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17. |
Censored Median Regression Using Weighted Empirical Survival and Hazard Functions |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 137-145
Song Yang,
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摘要:
For median regression models that regress the median of the survival time or a transform thereof on the covariates, some semi-parametric estimators that include the intercept component are introduced when the survival time may be censored. These new median regression estimators do not require estimating the censoring distributions. They can be viewed as an extension of the sample median to the censored regression model. These estimators are based on some weighted empirical survival and hazard functions and are shown to be consistent and asymptotically normal. They performed very well in various numerical studies. The proposed procedures are illustrated in some real data examples.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473830
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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18. |
Nonparametric Estimation of a Recurrent Survival Function |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 146-153
Mei-Cheng Wang,
Shu-Hui Chang,
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摘要:
Recurrent event data are frequently encountered in studies with longitudinal designs. Let the recurrence time be the time between two successive recurrent events. Recurrence times can be treated as a type of correlated survival data in statistical analysis. In general, because of the ordinal nature of recurrence times, statistical methods that are appropriate for standard correlated survival data in marginal models may not be applicable to recurrence time data. Specifically, for estimating the marginal survival function, the Kaplan–Meier estimator derived from the pooled recurrence times serves as a consistent estimator for standard correlated survival data but not for recurrence time data. In this article we consider the problem of how to estimate the marginal survival function in nonparametric models. A class of nonparametric estimators is introduced. The appropriateness of the estimators is confirmed by statistical theory and simulations. Simulation and analysis from schizophrenia data are presented to illustrate the estimators' performance.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473831
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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19. |
Methods for Estimating a Conditional Distribution Function |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 154-163
Peter Hall,
RodneyC. L. Wolff,
Qiwei Yao,
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摘要:
Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya–Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473832
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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20. |
Improved Estimators in Nonparametric Regression Problems |
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Journal of the American Statistical Association,
Volume 94,
Issue 445,
1999,
Page 164-173
LindaH. Zhao,
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摘要:
Linear estimators of multivariate means are considered. Generalizations of some well-known theorems about admissibility of linear estimators are given. The results then are applied to show that commonly used kernel-type estimators in nonparametric regression problems can be constructively improved in a simple way. An asymptotic result is described that gives a quantitative measure of the maximum improvement to be gained in certain situations. A theoretical bound shows that gains are achievable in the relative risk of up to 58.6% (rectangular kernel) or 29.2% (Epanechnikov kernel). Some examples of smaller sample size are also investigated, and these show relative risk gains ranging up to 18% in realistic settings.
ISSN:0162-1459
DOI:10.1080/01621459.1999.10473833
出版商:Taylor & Francis Group
年代:1999
数据来源: Taylor
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