机械化保护性耕作条件下土壤质量的数值化评价

通过9年的长期田间定位试验研究了陕西关中平原中部冬小麦 夏玉米轮作条件下深松耕（ST）、旋耕（RT）、秸秆还（SR）、免耕（NTS）等保护性耕作措施及传统耕作（TT）对土壤理化性状和作物产量的影响,并采用主成分分析方法进行土壤质量的综合评价.结果表明：与传统耕作相比,保护性耕作模式提高了土壤肥力质量,改善了土壤物理环境条件;显著提高了土壤脲酶和碱性磷酸酶的活性;除秸秆覆盖免耕处理的玉米和小麦产量低于传统耕作外,其他保护性耕作措施均不同程度地提高了作物产量,其中小麦增产13%～28%,玉米增产3%～12%.与传统耕作相比,保护性耕作土壤质量指数提高了19.8%～44.0%.综合考虑经济效应和生态效益,隔年深松、秸秆粉碎联合旋耕作业以及秸秆覆盖联合深松作业不仅能增加作物产量还可改善土壤质量,可在研究区进行推广应用.     

基于Fiedler向量的基因表达谱数据分类方法

尝试将一种基于图的Fiedler向量的聚类算法引入到基因表达谱数据的肿瘤分类中来。该方法将分属不同类的所有样本通过高斯权构造Laplace完全图,经SVD分解后获得Fiedler向量,利用各样本所对应的Fiedler向量分量的符号差异来进行基因表达谱数据的分类。通过模拟数据仿真实验和对白血病两个亚型(ALL与AML)及结肠癌真实数据实验,证明了这一方法的有效性。     

A bivariate pseudolikelihood for incomplete longitudinal binary data with nonignorable nonmonotone missingness

For analyzing longitudinal binary data with nonignorable and nonmonotone missing responses, a full likelihood method is complicated algebraically, and often requires intensive computation, especially when there are many follow-up times. As an alternative, a pseudolikelihood approach has been proposed in the literature under minimal parametric assumptions. This formulation only requires specification of the marginal distributions of the responses and missing data mechanism, and uses an independence working assumption. However, this estimator can be inefficient for estimating both time-varying and time-stationary effects under moderate to strong within-subject associations among repeated responses. In this article, we propose an alternative estimator, based on a bivariate pseudolikelihood, and demonstrate in simulations that the proposed method can be much more efficient than the previous pseudolikelihood obtained under the assumption of independence. We illustrate the method using longitudinal data on CD4 counts from two clinical trials of HIV-infected patients.     

Smoothing spline ANOVA frailty model for recurrent event data

Gap time hazard estimation is of particular interest in recurrent event data. This article proposes a fully nonparametric approach for estimating the gap time hazard. Smoothing spline analysis of variance (ANOVA) decompositions are used to model the log gap time hazard as a joint function of gap time and covariates, and general frailty is introduced to account for between-subject heterogeneity and within-subject correlation. We estimate the nonparametric gap time hazard function and parameters in the frailty distribution using a combination of the Newton-Raphson procedure, the stochastic approximation algorithm (SAA), and the Markov chain Monte Carlo (MCMC) method. The convergence of the algorithm is guaranteed by decreasing the step size of parameter update and/or increasing the MCMC sample size along iterations. Model selection procedure is also developed to identify negligible components in a functional ANOVA decomposition of the log gap time hazard. We evaluate the proposed methods with simulation studies and illustrate its use through the analysis of bladder tumor data.     

Linear and nonlinear mixed-effects models for censored HIV viral loads using normal/independent distributions

HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear (and nonlinear) mixed-effects models (with modifications to accommodate censoring) are routinely used to analyze this type of data and are based on normality assumptions for the random terms. However, those analyses might not provide robust inference when the normality assumptions are questionable. In this article, we develop a Bayesian framework for censored linear (and nonlinear) models replacing the Gaussian assumptions for the random terms with normal/independent (NI) distributions. The NI is an attractive class of symmetric heavy-tailed densities that includes the normal, Student's-t, slash, and the contaminated normal distributions as special cases. The marginal likelihood is tractable (using approximations for nonlinear models) and can be used to develop Bayesian case-deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated with two HIV AIDS studies on viral loads that were initially analyzed using normal (censored) mixed-effects models, as well as simulations.     

Detecting disease outbreaks using local spatiotemporal methods

A real-time surveillance method is developed with emphasis on rapid and accurate detection of emerging outbreaks. We develop a model with relatively weak assumptions regarding the latent processes generating the observed data, ensuring a robust prediction of the spatiotemporal incidence surface. Estimation occurs via a local linear fitting combined with day-of-week effects, where spatial smoothing is handled by a novel distance metric that adjusts for population density. Detection of emerging outbreaks is carried out via residual analysis. Both daily residuals and AR model-based detrended residuals are used for detecting abnormalities in the data given that either a large daily residual or an increasing temporal trend in the residuals signals a potential outbreak, with the threshold for statistical significance determined using a resampling approach.     

Nonparametric comparison for panel count data with unequal observation processes

This article considers nonparametric comparison of several treatment groups based on panel count data, which often occur in, among others, medical follow-up studies and reliability experiments concerning recurrent events. For the problem, most of the existing procedures require that observation processes are identical across different treatment groups among other requirements. We propose a new class of nonparametric test procedures that allow different observation processes. The new test statistics are constructed based on the integrated weighted differences between the estimated mean functions of the underlying recurrent event processes. The asymptotic distributions of the proposed test statistics are established and their finite-sample properties are examined through Monte Carlo simulations, which indicate that the proposed approach works well for practical situations. An illustrative example is provided.     

A general probabilistic model for group independent component analysis and its estimation methods

Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a prespecified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multisubject spatiotemporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood (ML) method is used for estimating this general group ICA model. We propose two expectation-maximization (EM) algorithms to obtain the ML estimates. The first method is an exact EM algorithm, which provides an exact E-step and an explicit noniterative M-step. The second method is a variational approximation EM algorithm, which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.