Semiparametric GEE analysis in partially linear single-index models for longitudinal data

In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link function. Under some mild conditions, we derive the asymptotic properties of the proposed parametric and nonparametric estimators in different scenarios, from which we find that the convergence rates and asymptotic variances of the proposed estimators for sparse longitudinal data would be substantially different from those for dense longitudinal data. We also discuss the estimation of the covariance (or weight) matrices involved in the semiparametric GEE method. Furthermore, we provide some numerical studies including Monte Carlo simulation and an empirical application to illustrate our methodology and theory.Comment: Published at http://dx.doi.org/10.1214/15-AOS1320 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the high-frequency data contaminated by microstructure noise, we introduce a localised pre-averaging estimation method that reduces the effective magnitude of the noise. We then use the estimation tool developed in the noise-free scenario, and derive the uniform convergence rates for the developed spot volatility matrix estimator. We further combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector. In addition, we consider large spot volatility matrix estimation in time-varying factor models with observable risk factors and derive the uniform convergence property. We provide numerical studies including simulation and empirical application to examine the performance of the proposed estimation methods in finite samples

Simultaneous Confidence Bands in Nonlinear Regression Models with Nonstationarity

We consider nonparametric estimation of the regression function g(·) in a nonlinear regression model Yt = g(Xt) + σ(Xt)et, where the regressor (Xt) is a nonstationary unit root process and the error (et) is a sequence of independent and identically distributed (i.i.d.) random variables. With proper centering and scaling, the maximum deviation of the local linear estimator of the regression function g is shown to be asymptotically Gumbel. Based on the latter result, we construct simultaneous confidence bands for g, which can be used to test patterns of the regression function. Our results substantially extend existing ones which typically require independent or stationary weakly dependent regressors. Furthermore, we examine the finite sample behavior of the proposed approach via the simulated and real data examples

Potential of tropical maize populations for improving an elite maize hybrid

Identifying exotic maize (Zea mays L) populations possessing favorable new alleles lacking in local elite hybrids is an important strategy for improving maize hybrids. Selection of an appropriate breeding method will increase the chance of successfully transferring these favorable new alleles into elite inbred lines of local hybrids. The objec¬tives of this study were to: (i) evaluate 14 maize populations from CIMMYT and identify those containing favorable alleles for grain yield, ear length, ear diameter, kernel length, plant height, and ear height that are lacking in a local super hybrid [Jidan261 (W9706 × Ji853)], and to (ii) determine which inbred parent should be improved. These re¬sults showed that the populations Pob43, Pob501, and La Posta had positive and significant numbers of favorable alleles not found in hybrid W9706 × Ji853 that could be used for simultaneous improvement of its grain yield, ear length, and kernel length, and that population QPM-Y was also a good donor for improvement of ear diameter and kernel length in the hybrid. Based on allele frequencies in the two inbred lines and the donor population, when the populations Pob43, La Posta, Pob501, and QPM-Y were used as donors, inbred line W9706 would be improved by selfing the F1 of the cross W9706 × donor population. These results suggested that CIMMYT germplasm has potential to improve temperate elite hybrids. The relationship between GCA and SCA from a previous study and the parameters obtained from the Dudley method are discussed. The results showed that the values of Lplμ’ esti¬mates obtained by applying the Dudley method had the same trend as GCA effects for grain yield but a less clear trend for ear length, while the trends in the relationship value were reversed for SCA between these populations and Lancaster-derived lines

Uniform Consistency of Nonstationary Kernel-Weighted Sample Covariances for Nonparametric Regression

We obtain uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation. In the fixed design case these nonparametric sample covariances have different uniform asymptotic rates depending on direction, a result that differs fundamentally from the random design and stationary cases. The uniform asymptotic rates derived exceed the corresponding rates in the stationary case and confirm the existence of uniform super-consistency. The modelling framework and convergence rates allow for endogeneity and thus broaden the practical econometric import of these results. As a specific application, we establish uniform consistency of nonparametric kernel estimators of the coefficient functions in nonlinear cointegration models with time varying coefficients or functional coefficients, and provide sharp convergence rates. For the fixed design models, in particular, there are two uniform convergence rates that apply in two different directions, both rates exceeding the usual rate in the stationary case

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics

Dissecting the Genetic Basis Underlying Combining Ability of Plant Height Related Traits in Maize

Maize plant height related traits including plant height, ear height, and internode number are tightly linked with biomass, planting density, and grain yield in the field. Previous studies have focused on understanding the genetic basis of plant architecture traits per se, but the genetic basis of combining ability remains poorly understood. In this study, 328 recombinant inbred lines were inter-group crossed with two testers to produce 656 hybrids using the North Carolina II mating design. Both of the parental lines and hybrids were evaluated in two summer maize-growing regions of China in 2015 and 2016. QTL mapping highlighted that 7 out of 16 QTL detected for RILs per se could be simultaneously detected for general combining ability (GCA) effects, suggesting that GCA effects and the traits were genetically controlled by different sets of loci. Among the 35 QTL identified for hybrid performance, 57.1% and 28.5% QTL overlapped with additive/GCA and non-additive/SCA effects, suggesting that the small percentage of hybrid variance due to SCA effects in our design. Two QTL hotspots, located on chromosomes 5 and 10 and including the qPH5-1 and qPH10 loci, were validated for plant height related traits by Ye478 derivatives. Notably, the qPH5-1 locus could simultaneously affect the RILs per se and GCA effects while the qPH10, a major QTL (PVE > 10%) with pleiotropic effects, only affected the GCA effects. These results provide evidence that more attention should be focused on loci that influence combining ability directly in maize hybrid breeding

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive