王国维“通人之学”与“专人之学”的尺短寸长

王国维“通人之学”“专人之学”等学术思想是中国学术研究转型的重大创新,通过对王国维学术成长的梳理、学术成果的分析、学术思想的论证,对“静安之学”进行了全面系统的研究。研究认为,王国维凭借厚实的知识贮备,批判地继承西方哲学理论与中国古代哲学思想,在构建中国大文科的学科体系、研究文史哲的学术体系和中国学者的学术话语体系方面做出了全面系统实践,取得重大进展和开创性成果,堪称开一代风气的大学者;王国维以创辟胜解、通方知类的学术思辨,创立二重证据方法,探索出一条由“通人之学”到“专人之学”学问研究蹊径。系统审读、反思静安之学,对我们严肃读书风气、匡正学问研究态度、提倡学术争鸣、甄别解决知识产权争端、清除学术造假现象、规范学术研究流程,能够起到见贤思齐、弃旧图新之作用。     

交易特性、社员身份与农业合作社合约选择

在威廉姆森交易成本理论的基础上,探讨交易特性对农业合作社与农户之间合约选择的影响,发现社员身份特征在其中发挥调节作用。研究结果表明,资产专用性、风险性和规模性对农业合作社与农户之间签订一体化程度不同的合约具有明显的影响,其中,专用性对合约一体化程度的影响是正向的;而风险性和规模性对双方签订一体化程度更高的合约影响是负向的。但和非社员相比,股东身份使资产专用性对双方签订一体化程度更高合约的正向影响作用变大;并使风险性、规模性对双方签订一体化程度更高合约的负向影响作用变小,即股东身份作为调节变量发挥了显著效应。     

Large Deviations for Empirical Estimators of the Stationary Distribution of a Semi-Markov Process with Finite State Space

We prove the large deviation principle for empirical estimators of stationary distributions of semi-Markov processes with finite state space, irreducible embedded Markov chain, and finite mean sojourn time in each state. We consider on/off Gamma sojourn processes as an illustrative example, and, in particular, continuous time Markov chains with two states. In the second case, we compare the rate function in this article with the known rate function concerning another family of empirical estimators of the stationary distribution.     

Geometric Ergodicity and Scanning Strategies for Two-Component Gibbs Samplers

In Markov chain Monte Carlo analysis, rapid convergence of the chain to its target distribution is crucial. A chain that converges geometrically quickly is geometrically ergodic. We explore geometric ergodicity for two-component Gibbs samplers (GS) that, under a chosen scanning strategy, evolve through one-at-a-time component-wise updates. We consider three such strategies: composition, random sequence, and random scans. We show that if any one of these scans produces a geometrically ergodic GS, so too do the others. Further, we provide a simple set of sufficient conditions for the geometric ergodicity of the GS. We illustrate our results using two examples.     

Semiparametric estimator for a secondary analysis with correlated outcomes

ABSTRACT

Researchers are often required to reuse data that have been collected and analyzed for other purposes. Issues may arise if the outcome of this secondary study is related to the outcome of the first study and traditional methods may fail to deliver a consistent estimate. Here we propose a semiparametric approach that takes this correlation into account and produces asymptotically consistent and normally distributed estimates. We discuss its performance through simulations and apply the proposed method to a real dataset.     

The analysis of some biological data

This paper considers the building of stochastic models and the related analysis of discrete data in two biological problems, The first arises from the reproduction of yeast cells, while the secondis concerned with the aggregation of nucleoli. Galton-Watson and aggregation models are constructed for the respective processes and their goodness of fit to the data tested     

Estimation of functions of the parameters of a normal distribution

Several authors have considered the problem of estimating parameters of a distribution after some fixed Gaussian inducing transformation has been applied to the observations. This paper extends this work to the situation where the observations represent a noisy version of a true process, the parameters of the latter requiring estimation     

Nonparametric estimation of the survival distribution in censored data

Two nonparametric estimators o f the survival distributionare discussed. The estimators were proposed by Kaplan and Meier (1958) and Breslow (1972) and are applicable when dealing with censored data. It is known that they are asymptotically unbiased and uniformly strongly consistent, and when properly normalized that they converge weakly to the same Gaussian process. In this paper, the properties of the estimators are carefully inspected in small or moderate samples. The Breslow estimator, a shrinkage version of the Kaplan-Meier, nearly always has the smaller mean square error (MSE) whenever the truesurvival probabilityis at least 0.20, but has considerably larger MSE than the Kaplan-Meier estimator when the survivalprobability is near zero.     

A note on information loss in analyzing a mixture model of count data

In this note we consider estimation of a mixture model of count data which is composed of two discrete random variables. Conditional and unconditional estimation procedures are given for estimating the unknown parameter(s) of interest using the likelihood function. Asymptotic relative efficiencies are given to examine the amount of information loss in using the two estimation procedures. Specifically, we study the change in asymptotic relative efficiency, if any, in different parameter settings.     

Quotas on runs of successes and failures in a multi-state markov chain

For the time-homogeneous multi-state Markov chain {X_n,n≧0} with states labeled as "0" (success) and "f"(failure), f=1,2,… the waiting time problems to be discussed arise by setting quotas on runs of success and failures. Some particular cases are considered.