Profile Likelihood for Hierarchical Models Using Data Doubling [PDF]
Entropy, 2023 In scientific problems, an appropriate statistical model often involves a large number of canonical parameters. Often times, the quantities of scientific interest are real-valued functions of these canonical parameters.
Subhash R. Lele
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Profile Likelihood and Incomplete Data. [PDF]
Int Stat Rev, 2010 Summary According to the law of likelihood, statistical evidence is represented by likelihood functions and its strength measured by likelihood ratios. This point of view has led to a likelihood paradigm for interpreting statistical evidence, which carefully distinguishes evidence about a parameter from error probabilities and personal belief.
Zhang Z.
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Statistical Generalized Derivative Applied to the Profile Likelihood Estimation in a Mixture of Semiparametric Models [PDF]
Entropy, 2020 There is a difficulty in finding an estimate of the standard error (SE) of the profile likelihood estimator in the joint model of longitudinal and survival data.
Yuichi Hirose, Ivy Liu
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Maximum likelihood, profile likelihood, and penalized likelihood: a primer. [PDF]
Am J Epidemiol, 2014 The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood
Cole SR, Chu H, Greenland S.
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Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models. [PDF]
PLoS Computational Biology, 2023 Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering.
Matthew J Simpson, Oliver J Maclaren
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Driving the Model to Its Limit: Profile Likelihood Based Model Reduction. [PDF]
PLoS ONE, 2016 In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to
Tim Maiwald +10 more
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An algorithm for computing profile likelihood based pointwise confidence intervals for nonlinear dose-response models. [PDF]
PLoS ONE, 2019 This study was inspired by the need to estimate pointwise confidence intervals (CIs) for a nonlinear dose-response model from a dose-finding clinical trial. Profile likelihood based CI for a nonlinear dose response model is often recommended. However, it
Xiaowei Ren, Jielai Xia
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Cluster Gauss‐Newton method for a quick approximation of profile likelihood: With application to physiologically‐based pharmacokinetic models [PDF]
CPT: Pharmacometrics & Systems PharmacologyPhysiologically‐based pharmacokinetic (PBPK) models can be challenging to work with because they can have too many parameters to identify from observable data.
Yasunori Aoki, Yuichi Sugiyama
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Exercise motivation patterns in emerging adults: dual roles of weight and shape concern in activation and inhibition pathways [PDF]
BMC PsychologyBackground Exercise motivation constitutes a key determinant of physical activity(PA). However, prior research has predominantly adopted a variable-centered approach, thereby overlooking the coexistence of multiple motivational goals within individuals ...
Jiahao Wu +6 more
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On the likelihood of Condorcet's profiles [PDF]
Social Choice and Welfare, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fabrice Valognes +2 more
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