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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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 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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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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Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles
Frontiers in Molecular Biosciences, 2022 Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds.
Tim Litwin +8 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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Generalised likelihood profiles for models with intractable likelihoods
Statistics and Computing, 2023 Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models without a tractable likelihood function.
David J. Warne +4 more
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Estimating uncertainty of model parameters obtained using numerical optimisation [PDF]
Modeling, Identification and Control, 2019 Obtaining accurate models that can predict the behaviour of dynamic systems is important for a variety of applications. Often, models contain parameters that are difficult to calculate from system descriptions.
Ole Magnus Brastein +3 more
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