Results 1 to 10 of about 299,935 (165)
On partial likelihood and the construction of factorisable transformations. [PDF]
Inf Geom, 2022 Abstract Models whose associated likelihood functions fruitfully factorise are an important minority allowing elimination of nuisance parameters via partial likelihood, an operation that is valuable in both Bayesian and frequentist inferences, particularly when the number of nuisance parameters is not small.
Battey HS, Cox DR, Lee SH.
europepmc +4 more sources
Partial Likelihood Thompson Sampling
CoRR, 2022 We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task.
Wu H, Wager S.
europepmc +4 more sources
Global Partial Likelihood for Nonparametric Proportional Hazards Models. [PDF]
J Am Stat Assoc, 2010 As an alternative to the local partial likelihood method of Tibshirani and Hastie and Fan, Gijbels, and King, a global partial likelihood method is proposed to estimate the covariate effect in a nonparametric proportional hazards model, λ(t|x) = exp{ψ(x)}λ(0)(t).
Chen K, Guo S, Sun L, Wang JL.
europepmc +6 more sources
How to use χ2 test correctly——the likelihood ratio test and the implementation of SAS software
Sichuan jingshen weisheng, 2021 The purpose of this article was to introduce the likelihood ratio test and the SAS implementation. Specifically， three definitions of the likelihood ratio test statistics and six more commonly used likelihood ratio test statistics were introduced.
Hu Chunyan, Hu Liangping
doaj +1 more source
Partial order optimum likelihood (POOL): maximum likelihood prediction of protein active site residues using 3D Structure and sequence properties. [PDF]
PLoS Computational Biology, 2009 A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics.
Wenxu Tong +4 more
doaj +1 more source
Quasi-likelihood Estimation in Fractional Levy SPDEs from Poisson Sampling
European Journal of Mathematical Analysis, 2022 We study the quasi-likelihood estimator of the drift parameter in the stochastic partial differential equations driven by a cylindrical fractional Levy process when the process is observed at the arrival times of a Poisson process.
Jaya P. N. Bishwal
doaj +1 more source
Frontiers in Psychology, 2021 In this paper, a new item-weighted scheme is proposed to assess examinees’ growth in longitudinal analysis. A multidimensional Rasch model for measuring learning and change (MRMLC) and its polytomous extension is used to fit the longitudinal item ...
Xuemei Xue, Jing Lu, Jiwei Zhang
doaj +1 more source
Arthroscopy, Sports Medicine, and Rehabilitation, 2022 Purpose: To determine the likelihood of and risk factors for tear progression among patients with a symptomatic partial or full-thickness rotator cuff tears (RCTs) who return with continued shoulder pain and obtain subsequent magnetic resonance imaging ...
Jeffrey J. Frandsen, M.D. +5 more
doaj +1 more source
Phylogenetic estimation with partial likelihood tensors [PDF]
Journal of Theoretical Biology, 2010 We present an alternative method for calculating likelihoods in molecular phylogenetics. Our method is based on partial likelihood tensors, which are generalizations of partial likelihood vectors, as used in Felsenstein's approach. Exploiting a lexicographic sorting and partial likelihood tensors, it is possible to obtain significant computational ...
Jeremy G. Sumner, Michael A. Charleston
openaire +4 more sources
A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling [PDF]
International Journal of Data and Network Science, 2022 Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator.
Raudhah Zulkifli +3 more
doaj +1 more source