Results 231 to 240 of about 301,324 (265)
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Maximum likelihood partially adaptive estimation†
International Journal of Systems Science, 1972This paper extends previous work involving adaptive estimation to the case where only some components of the unknown parameters are adapted to. The method of invariant embedding is used to obtain sequential adaptive algorithms for maximum likelihood estimation.
ANDREW P. SAGE, CHARLES D. WAKEFIELD
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Hypothesis assignment and partial likelihood averaging for cooperative estimation
2019 IEEE 58th Conference on Decision and Control (CDC), 2019We propose a cooperative, decentralized inference algorithm allowing sensor networks to learn a joint parameter best explaining their combined observations. This joint parameter is represented via a probability density over a discrete set of hypotheses.
Parth Paritosh +2 more
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The approximation of partial likelihood with emphasis on case-control studies
Biometrika, 1980For many case-control applications, previously suggested approximations to Cox's (1972, 1975) partial likelihood in prospective applications are shown to be seriously inadequate. Alternative approximations based on unconditional maximum likelihood estimation and Taylor series expansion are developed and shown to yield an improvement over the previous ...
Farewell, V. T., Prentice, R. L.
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Partial and conditional maximum likelihood for variance‐component estimation
Journal of Animal Breeding and Genetics, 1994SummaryPatterson and Thompson's idea of ‘error contrasts’ (or restricted maximum likelihood) (1971) was extended to multiple sets of linear contrasts for variance component estimtion. The error contrasts were established in such a way that only errors are retained in the model. The error variance was then estimated by maximizing the likelihood function
S, Xu, W R, Atchley, W M, Muir
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Projected Partial Likelihood and Its Application to Longitudinal Data
Biometrika, 1995Summary: An estimating equation, which we call the projected partial score, is introduced for longitudinal data analysis. The estimating equation is obtained by projecting the partial likelihood score function onto the vector space spanned by a class of `conditionally linear' estimating equations.
Murphy, Susan, Li, Bing
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A Bayesian justification of Cox's partial likelihood
Biometrika, 2003SUMMARY In this paper, we establish both naive and formal Bayesian justifications of Cox's (1975) partial likelihood and its various modifications. We extend the original work of Kalbfieisch (1978), who showed that the partial likelihood is a limiting marginal posterior under noninformative priors for baseline hazards. We extend the result to scenarios
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Partial likelihood for estimation of multi-class posterior probabilities
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Partial likelihood (PL) provides a unified statistical framework for developing and studying adaptive techniques for nonlinear signal processing. In this paper, we present the general formulation for learning posterior probabilities on the PL cost for multi-class classifier design.
Tülay Adali, Hongmei Ni, Bo Wang 0006
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ASYMPTOTIC LIKELIHOOD APPROXIMATIONS USING A PARTIAL LAPLACE APPROXIMATION
Australian & New Zealand Journal of Statistics, 2006SummaryElimination of a nuisance variable is often non‐trivial and may involve the evaluation of an intractable integral. One approach to evaluate these integrals is to use the Laplace approximation. This paper concentrates on a new approximation, called the partial Laplace approximation, that is useful when the integrand can be partitioned into two ...
Taylor, J., Verbyla, A.
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Mechanism of gating and partial agonist action in the glycine receptor
Cell, 2021Timo Greiner +2 more
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