Results 11 to 20 of about 325,195 (256)
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.
europepmc +5 more sources
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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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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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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Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution
Mathematics, 2020 Maximum likelihood estimation (MLE) of the four-parameter kappa distribution (K4D) is known to be occasionally unstable for small sample sizes and to be very sensitive to outliers.
Palakorn Seenoi +2 more
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Profile likelihood biclustering
Electronic Journal of Statistics, 2020 Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees.
Flynn, Cheryl, Perry, Patrick
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Likelihood-based estimation and prediction for a measles outbreak in Samoa
Infectious Disease Modelling, 2023 Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these
David Wu +4 more
doaj +1 more source
IEEE Access, 2020 A stochastic scheme, namely, PLM-Lap, has recently been propounded, which relies on the profile likelihood (PL) constructed with a Laplace distribution for estimating muscle activation onsets (MAOs) in surface electromyographic (sEMG) data.
Easter S. Suviseshamuthu +4 more
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