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Adaptive Transferred-profile Likelihood Learning

2016 International Joint Conference on Neural Networks (IJCNN), 2016
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved.
Son Ngoc Tran, Artur S. d'Avila Garcez
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On Profile Likelihood

Journal of the American Statistical Association, 2000
Abstract We show that semiparametric profile likelihoods, where the nuisance parameter has been profiled out, behave like ordinary likelihoods in that they have a quadratic expansion. In this expansion the score function and the Fisher information are replaced by the efficient score function and efficient Fisher information.
Murphy, S.A., van der Vaart, A.W.
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Adjustments to profile likelihood

Biometrika, 1989
Summary: Conditional and marginal likelihoods constructed from parameter-dependent functions of the data have an additional parameter dependence that changes with the choice of supporting metric for the corresponding densities. We consider constructing marginal and conditional likelihoods by using densities expressed in terms of an intrinsic choice of ...
Fraser, D. A. S., Reid, N.
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An explanation of generalized profile likelihoods

Statistics and Computing, 2001
Let X, T, Y be random vectors such that the distribution of Y conditional on covariates partitioned into the vectors X e x and T e t is given by f(ys x, p), where p e (t, η(t)). Here t is a parameter vector and η(t) is a smooth, real–valued function of t. The joint distribution of X and T is assumed to be independent of t and η.
Joan G. Staniswalis, Peter F. Thall
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Profile likelihood in systems biology

The FEBS Journal, 2013
Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model.
Kreutz, Clemens, Raue, Andreas, Kaschek, Daniel, Timmer, Jens   +3 more
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A Note on the Relation Between Modified Profile Likelihood and the Cox-Reid Adjusted Profile Likelihood

Biometrika, 1993
Summary: An adjustment to the profile likelihood proposed by \textit{D. R. Cox} and \textit{N. Reid} [J. R. Stat. Soc., Ser. B 49, 1-39 (1987; Zbl 0616.62006)] when the parameters are orthogonal is shown to agree with modified profile likelihood in a number of instances in which the parameters are not orthogonal.
Barndorff-Nielsen, Ole E., McCullagh, Peter   +1 more
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Profile quasi-likelihood

Statistics & Probability Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lin, Lu, Zhang, Runchu
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Model-Averaged Profile Likelihood Intervals

Journal of Agricultural, Biological, and Environmental Statistics, 2011
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Fletcher, David, Turek, Daniel
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A note on the difference between profile and modified profile likelihood

Biometrika, 1992
The difference between profile likelihood and modified profile likelihood depends primarily on the expected value of a certain third order derivative of the log likelihood. It is shown that in exponential family problems this derivative vanishes if the parameter of interest is a mean parameter but in general not when it is a canonical parameter.
D. R. Cox, N. Reid
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