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Marginal analysis of panel counts through estimating functions
We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied by Wellner & Zhang (2000), assuming a Poisson counting pro...
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| Published in: | Biometrika 2009-06, Vol.96 (2), p.445-456 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c604t-e6fdacc3f6a0cb42891e052646ec4a8c2832bb6385afd19e6b9a2f01e641e0893 |
| container_end_page | 456 |
| container_issue | 2 |
| container_start_page | 445 |
| container_title | Biometrika |
| container_volume | 96 |
| creator | HU, X. JOAN LAGAKOS, STEPHEN W. LOCKHART, RICHARD A. |
| description | We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied by Wellner & Zhang (2000), assuming a Poisson counting process. It gives a nondecreasing estimator, which equals the nonparametric maximum likelihood estimator of Wellner & Zhang and is consistent without the Poisson assumption. Motivated by the construction of parametric generalized estimating equations, the second type is a set of data-adaptive quasi-score functions, which are likelihood estimating functions under a mixed-Poisson assumption. We evaluate the procedures using simulation, and illustrate them with the data from a bladder cancer study. |
| doi_str_mv | 10.1093/biomet/asp010 |
| format | article |
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JOAN</creatorcontrib><creatorcontrib>LAGAKOS, STEPHEN W.</creatorcontrib><creatorcontrib>LOCKHART, RICHARD A.</creatorcontrib><title>Marginal analysis of panel counts through estimating functions</title><title>Biometrika</title><addtitle>Biometrika</addtitle><description>We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied by Wellner & Zhang (2000), assuming a Poisson counting process. It gives a nondecreasing estimator, which equals the nonparametric maximum likelihood estimator of Wellner & Zhang and is consistent without the Poisson assumption. Motivated by the construction of parametric generalized estimating equations, the second type is a set of data-adaptive quasi-score functions, which are likelihood estimating functions under a mixed-Poisson assumption. We evaluate the procedures using simulation, and illustrate them with the data from a bladder cancer study.</description><subject>Actuarial science</subject><subject>Algorithms</subject><subject>Analytical estimating</subject><subject>Applications</subject><subject>Biology, psychology, social sciences</subject><subject>Censorship</subject><subject>Consistent estimators</subject><subject>Counting process</subject><subject>Distribution theory</subject><subject>Estimators</subject><subject>Exact sciences and technology</subject><subject>General topics</subject><subject>Interval censoring</subject><subject>Interval estimators</subject><subject>Marginal analysis</subject><subject>Marginal mean function</subject><subject>Mathematical functions</subject><subject>Mathematics</subject><subject>Maximum likelihood estimation</subject><subject>Maximum likelihood estimators</subject><subject>Nonparametric estimation</subject><subject>Nonparametric inference</subject><subject>Placebos</subject><subject>Poisson process</subject><subject>Probability and statistics</subject><subject>Quasi-score function</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><issn>0006-3444</issn><issn>1464-3510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkc-L1DAUx4Mo7uzq0aNSBMFL3aRJ3jSXhWVdd5T1x0FBvIQ0k85kbJuapIvz35vSMrN6kfDyEvLhm_e-D6FnBL8hWNDzyrrWxHMVekzwA7QgDFhOOcEP0QJjDDlljJ2g0xB24xU4PEYnRHBGWQELdPFR-Y3tVJOptO2DDZmrs151psm0G7oYsrj1bthsMxOibVW03Sarh05H67rwBD2qVRPM0zmfoW_vrr9erfLbzzfvry5vcw2YxdxAvVZa0xoU1hUrSkEM5gUwMJqpUhclLaoKaMlVvSbCQCVUUWNigCWwFPQMXUy6_VC1Zq1NF71qZO9TRX4vnbLy75fObuXG3ckCRMmAJoHXs4B3v4bUimxt0KZpUqduCJKUlHMGmIzoy3_QnRt8cifIAhMQaZEE5ROkvQvBm_pQC8FyHIycBiOnwST-w8R70xt9LHzoZ-5OUiUgbfsUBcYiJTseU_QpGOOScZDb2CaxF_fdOH49jzUBr2ZABa2a2qtO23DgCjJy5T1XUh3_beD5hO5CdP4otVyKMnl3NMSGaH4f3pX_KWFJl1yuvv-QHN6yT1_4St7QP0jr2Os</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>HU, X. 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| ispartof | Biometrika, 2009-06, Vol.96 (2), p.445-456 |
| issn | 0006-3444 1464-3510 |
| language | eng |
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| source | Oxford Journals Online; JSTOR Archival Journals |
| subjects | Actuarial science Algorithms Analytical estimating Applications Biology, psychology, social sciences Censorship Consistent estimators Counting process Distribution theory Estimators Exact sciences and technology General topics Interval censoring Interval estimators Marginal analysis Marginal mean function Mathematical functions Mathematics Maximum likelihood estimation Maximum likelihood estimators Nonparametric estimation Nonparametric inference Placebos Poisson process Probability and statistics Quasi-score function Sciences and techniques of general use Statistics |
| title | Marginal analysis of panel counts through estimating functions |
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