Nonlinear mixed-effects models are very useful in analyzing repeated-measures data and have received a lot of attention in the field. It is of common interest to test for the correlation within clusters and the heterogeneity across different clusters. In this paper, we address these problems by proposing a class of score tests for the null hypothesis that all components of within- and between-subject variance are zeros in a kind of nonlinear mixed-effects model, and the asymptotic properties of the proposed tests are studied. The finite sample performance of this test is examined through simulation studies, and an illustrative example is presented.
We propose composite quantile regression for dependent data, in which the errors are from short‐range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root‐nconsistency and asymptotic normality. We investigate the asymptotic relative efficiency of composite quantile estimator to both single‐level quantile regression and least‐squares regression. When the errors have finite variance, the relative efficiency of composite quantile estimator with respect to the least‐squares estimator has a universal lower bound. Under some regularity conditions, the adaptive least absolute shrinkage and selection operator penalty leads to consistent variable selection, and the asymptotic distribution of the non‐zero coefficient is the same as that of the counterparts obtained when the true model is known. We conduct a simulation study and a real data analysis to evaluate the performance of the proposed approach.
Risk factor selection is very important in the insurance industry, which helps precise rate making and studying the features of high‐quality insureds. Zero‐inflated data are common in insurance, such as the claim frequency data, and zero‐inflation makes the selection of risk factors quite difficult. In this article, we propose a new risk factor selection approach, EM adaptive LASSO, for a zero‐inflated Poisson regression model, which combines the EM algorithm and adaptive LASSO penalty. Under some regularity conditions, we show that, with probability approaching 1, important factors are selected and the redundant factors are excluded. We investigate the finite sample performance of the proposed method through a simulation study and the analysis of car insurance data from SAS Enterprise Miner database.
AbstractInfertility affects approximately 15% of couples at child-bearing ages and assisted reproductive technologies (ART), especially in vitro fertilization and embryo transfer (IVF-ET), provided infertile patients with an effective solution. The current paradox is that multiple embryo transfer that may leads to severe obstetric and perinatal complications seems to be the most valid measure to secure high success rate in the majority of clinic centers. Therefore, to avoid multiple transfer of embryos, it is urgent to explore biomarkers for IVF prognosis to select high-quality oocytes and embryos. Follicular fluid (FF), a typical biofluid constituted of the plasma effusion and granulosa-cell secretion, provides essential intracellular substances for oocytes maturation and its variation in composition reflects oocyte developmental competence and embryo viability. With the advances in metabolomics methodology, metabolomics, as an accurate and sensitive analyzing method, has been utilized to explore predictors in FF for ART success. Although FF metabolomics has provided a great possibility for screening markers with diagnostic and predictive value, its effectiveness is still doubted by some researchers. This may be resulted from the ignorance of the impact of sterility causes on the FF metabolomic profiles and thus its predictive ability might not be rightly illustrated. Therefore, in this review, we categorically demonstrate the study of FF metabolomics according to specific infertility causes, expecting to reveal the predicting value of metabolomics for IVF outcomes.
