… the associated regression coefficients in the sparse Cox model are mainly based on partial
… The covariate vector x is allowed to be time dependent, denoted by x ( t ) . Throughout this …

… Cox model or relative risk model to reflect this broader applicability. The latter term is descriptive
in that (or a time-dependent … think of the regression parameter β as time dependent, but …

… by adding a continuous normal noise to the regression coefficients. It leads to continuously
… An empirical likelihood (EL) approach can overcome these present challenges nevertheless…

… proportional hazards model. We demonstrate that our … of high-dimensional hazard regression
coefficients, we introduce a … locally on disjoint subsets of the dataset and send their local …

… with time-varying coefficients. We propose to use the local partial log-likelihood approach
for nonparametric time-varying coefficient estimation. Simulation studies demonstrate that our …

… Jayasinghe and Zeephongsekul [Citation8] used a local … also been done using the jackknife
empirical likelihood (JEL)… probabilities, even for the skewness and kurtosis coefficients […

… Using this key tool, we establish the asymptotic joint normality of the proposed estimators and
the weak convergence of the estimated slope function, and then construct local … coefficients …

… a new empirical likelihood (NEL) inference procedure for the … limiting distribution of the
empirical log-likelihood ratio for the … Moreover, we develop the adjusted new empirical likelihood (…

… This paper aims at using these recent results for the local composite likelihood for spatial …
addressed via spatial covariates, a latent error process, and their estimated local coefficients. …

… empirical likelihood ratio test statistic has an asymptotic central chisquare distribution. The
conditions of local … are on the zero coefficients, then the likelihood ratio test equals zero with …