Bayesian factor models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.     

Discrete Multicolour Random Mosaics with an Application to Network Extraction

We introduce a class of random fields that can be understood as discrete versions of multicolour polygonal fields built on regular linear tessellations. We focus first on a subclass of consistent polygonal fields, for which we show Markovianity and solvability by means of a dynamic representation. This representation is used to design new sampling techniques for Gibbsian modifications of such fields, a class which covers lattice‐based random fields. A flux‐based modification is applied to the extraction of the field tracks network from a Synthetic Aperture Radar image of a rural area.     

Random translation of discrete order statistics

We investigate the existence and uniqueness of a discrete parent distribution supported on the integers whose order statistics are related by a random translation. We also provide some examples using the constructive method that we propose.     

The S-estimator in the change-point random model with long memory

This paper considers two-phase random design linear regression models. Errors and regressors are stationary long-range-dependent Gaussian processes. The regression parameters, the scale parameter and the change-point are estimated using a method introduced by Rousseeuw and Yohai [Robust regression by means of S-estimators, in Robust and Nonlinear Time Series Analysis, J. Franke, W. Hrdle, and R.D. Martin, eds., Lecture Notes in Statistics, Vol. 26, Springer, New York, 1984, pp. 256–272], which is called the S-estimator and has the property be more robust than the classical estimators in the sense that the outliers do not bias the estimation results. Some asymptotic results, including the strong consistency and the convergence rate of the S-estimator are proved. Simulations and an application to the Nile River data are also presented. It is shown via Monte Carlo simulations that the S-estimator is better than two other estimators that are proposed in the literature.     

机械设计制造及其自动化专业人才培养模式的创新实践

在新的历史条件下,深化高等教育改革、提高专业人才培养质量,关键是必须改革和创新专业人才培养模式。围绕专业人才培养目标和基本要求,从制订专业人才培养方案、建立基于课程群的完整的人才培养课程体系、创新人才培养模式等方面,阐述了我校机械设计制造及其自动化专业进行综合改革的具体做法,这对其他高校相关专业的教学改革及人才培养,具有一定的启发。     

Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes

We propose a sparse coefficient estimation and automated model selection procedure for autoregressive processes with heavy-tailed innovations based on penalized conditional maximum likelihood. Under mild moment conditions on the innovation processes, the penalized conditional maximum likelihood estimator satisfies a strong consistency, O_P(N^?1/2) consistency, and the oracle properties, where N is the sample size. We have the freedom in choosing penalty functions based on the weak conditions on them. Two penalty functions, least absolute shrinkage and selection operator and smoothly clipped average deviation, are compared. The proposed method provides a distribution-based penalized inference to AR models, which is especially useful when the other estimation methods fail or under perform for AR processes with heavy-tailed innovations [Feigin, Resnick. Pitfalls of fitting autoregressive models for heavy-tailed time series. Extremes. 1999;1:391–422]. A simulation study confirms our theoretical results. At the end, we apply our method to a historical price data of the US Industrial Production Index for consumer goods, and obtain very promising results.     

还原论及其历史发展

在考察西方不同学者关于还原论定义的基础上合理重建了还原论概念,并据此加以综合分类。接着着重追溯了还原论的历史发展,指出它经历了由萌芽（ 古希腊原子论与中国古代元气说） 到确立（ 近代机械还原论） 再到成熟（ 目标理论多元化的现代还原论） 的三个历史发展阶段     

机械制图课程的CDIO教学初探

按照CDIO教学对人才培养的基本要求,通过精选教学内容、强化实践教学、引入思维导图、改进教学方法和考核方法等有效措施,对机械制图课程开展CDIO教学进行了改革和实践,受到了学生的欢迎,取得了较好的教学效果。     

基于CDIO理念的新机械制图教学模式研究

为了改变传统的机械制图课程教学模式,更好地培养学生的实践能力、创新意识和团队精神,不断提高该课程的教育教学质量,基于CDIO理念,对该课程的教学内容、教学方法、教学体系等方面进行了深入的改革与创新,从而构建了新的机械制图课程的教学模式。     

Efficient Estimation of an Additive Quantile Regression Model

Abstract. In this paper, two non‐parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel‐based approaches. The second estimator involves sequential fitting by univariate local polynomial quantile regressions for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The purpose of the extra local averaging is to reduce the variance of the first estimator. We show that the second estimator achieves oracle efficiency in the sense that each estimated additive component has the same variance as in the case when all other additive components were known. Asymptotic properties are derived for both estimators under dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative empirical application of additive quantile models to ambulance travel times.