Results 201 to 210 of about 209,848 (255)
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Misspecified Proportional Hazard Models
Biometrika, 1986Let \((N_ i(t)\), \(t\geq 0\), \(i=1,...,n)\) be a counting process in which \(N_ i(t)\) records the number of failures in [0,t] for i-th item, and let \(Y_ i(t)\lambda_ 0(t) \exp (\beta Z_ i)\) \((i=1,...,n)\) be a random intensity process (for \(N_ i)\).
Struthers, C. A., Kalbfleisch, J. D.
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The Identifiability of the Proportional Hazard Model
The Review of Economic Studies, 1984Summary: This paper presents new identifiability conditions for the Cox proportional hazard model [see \textit{D. R. Cox}, J. R. Stat. Soc., Ser. B 34, 187-220 (1972; Zbl 0243.62041)] for duration data when unobserved person specific variables are present. We compare our conditions with those presented by \textit{C. Elbers} and \textit{G. Ridder} [Rev.
Heckman, J., Singer, B.
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Masking Unmasked in the Proportional Hazards Model
Biometrics, 2000Summary.Influence measures based on the pairwise deletion approach and the differentiation approach are developed for unmasking observations masked by other observations in the proportional hazards model. These influential observations might have substantial impact on statistical inference and might provide important information for model adequacy. One
Wei, Wen Hsiang, Kosorok, Michael R.
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2008
The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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The estimation of duration models has been the subject of significant research in econometrics since the late 1970s. Cox (1972) proposed the use of proportional hazard models in biostatistics and they were soon adopted for use in economics. Since Lancaster (1979), it has been recognized among economists that it is important to account for unobserved ...
Jerry A. Hausman, Tiemen M. Woutersen
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2021
We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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We consider several models that describe survival in the presence of observable covariates, these covariates measuring subject heterogeneity. The most general situation can be described by a model with a parameter of high, possibly unbounded, dimension.
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The Proportional Hazards Model
1988In this chapter and Chapter 7, we will consider models of the length of time until recidivism that contain individual characteristics as explanatory variables. The models of Chapter 7 will be parametric models in the sense that they will assume a particular distribution for the survival times; for example, we will estimate a model based on the ...
Peter Schmidt, Ann Dryden Witte
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Regression Dilution in the Proportional Hazards Model
Biometrics, 1993The problem of regression dilution arising from covariate measurement error is investigated for survival data using the proportional hazards model. The naive approach to parameter estimation is considered whereby observed covariate values are used, inappropriately, in the usual analysis instead of the underlying covariate values. A relationship between
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Mixtures of proportional hazards regression models
Statistics in Medicine, 1999This paper presents a mixture model which combines features of the usual Cox proportional hazards model with those of a class of models, known as mixtures-of-experts. The resulting model is more flexible than the usual Cox model in the sense that the log hazard ratio is allowed to vary non-linearly as a function of the covariates.
O, Rosen, M, Tanner
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2011
This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Jan Beyersmann +2 more
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This chapter discusses the most widely used regression models in competing risks. Following an introduction in Section 5.1, Section 5.2 discusses proportional cause-specific hazards models, and Section 5.3 discusses the proportional subdistribution hazards model. The cause-specific hazards are as defined in Chapter 3.
Jan Beyersmann +2 more
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Multivariate Generalizations of the Proportional Hazards Model
Journal of the Royal Statistical Society. Series A (General), 1985Die Motivation für die vorliegende Arbeit stammt aus Fragestellungen der Medizin, insbes. aus klinischen Versuchen und epidemiologischen Studien in der Erforschung von Krebs- und Kreislauferkrankungen. So werden zum Beispiel herzkranke Vater-Sohn-Paare herausgegriffen und das Alter beim Tode bzw.
Clayton, David, Cuzick, Jack
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