Comparisons of parameter and hypothesis definitions in a general linear model

In the context of the general linear model E(Y)=Xβ possibly subject to restrictions Rβ=r two secondary parameters may be well defined by Θ_i=G_iE(Y)-Θ_oi=C_i β-Θ_oi,i=1,2, and corresponding nonconstant hypotheses, H_i:Θ_i=0. The following possible relations are defined: Θ₁: is dependent upon /equivalent to/identical to Θ₂:H₁is a subhypothesis of/is identical to H₂. Necessary and sufficient conditions, involving straightforward matrix computations, are presented for each relation. Comparisons of secondary parameters and hypotheses are illustrated with an incomplete, unbalanced 3 × 4 factorial design from Searle in which, using a constrained version of Searle's model, parameters and hypotheses in the incomplete, unbalanced design are shown to be indentical to parameters one would define if complete balanced data were available. Techniques for computing simplified definitions are illustrated.     

早期佛典翻译中佛教缘起思想的演化

佛教传入中土早期面临的首先是佛典翻译与语言交流问题,此亦为异质文化交流中首要关键问题之一。从佛教在汉代传入的词汇翻译及语言转化的历史轨迹中可以看到佛教传入我国的早期形态。其中,佛教缘起性空思想也在翻译转化中发生着一些微妙的变化。文章重点分析汉代佛教基本词汇,如佛陀、涅榘、无我等词的音译及意译,以此说明早期佛典翻译中缘起思想的演化,以此管窥佛教中国化的早期思想演进特点。     

佛教的"众生平等"思想及其现代意义

在对平等的认识上,佛教主张宇宙万物本质上的平等,即"众生平等",包括人与人之间平等、有情众生之间平等、有情众生与无情众生之间平等."众生平等"的理论依据是佛教的缘起理论,其实现途径可从国家、团体、个人三方面入手."众生平等"思想对维护世界和平、保护生态环境及完善人格具有重要意义.     

Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling

We propose a new class of time dependent random probability measures and show how this can be used for Bayesian nonparametric inference in continuous time. By means of a nonparametric hierarchical model we define a random process with geometric stick-breaking representation and dependence structure induced via a one dimensional diffusion process of Wright-Fisher type. The sequence is shown to be a strongly stationary measure-valued process with continuous sample paths which, despite the simplicity of the weights structure, can be used for inferential purposes on the trajectory of a discretely observed continuous-time phenomenon. A simple estimation procedure is presented and illustrated with simulated and real financial data.     

C338. Misspecified location and scale. 111. the use of S2

The observed high kurtosis of stock market returns and other variables of a speculative nature has aroused interest in stable law distributions. This paper makes the point that most historical findings that returns indeed follow stable laws may have been caused by conditional heteroskedasticity. This presumption is enhanced by Monte Carlo simulations.     

Tradition and Modernity in the Family Farm:

No abstract available for this article.     

Bounds on the expected maximum

Let (X₁,X₂, …,X_n) be jointly distributed random variables. Define X_n:n = max(X₁,X₂, …,X_n).Bounds on E(X_n:n), obtained by putting constraints on the distributions and/or dependence structure of the X_i's, are surveyed.     

A Queueing Theoretic Approach to Set Staffing Levels in Time‐Dependent Dual‐Class Service Systems*

This article addresses the optimal staffing problem for a nonpreemptive priority queue with two customer classes and a time‐dependent arrival rate. The problem is related to several important service settings such as call centers and emergency departments where the customers are grouped into two classes of “high priority” and “low priority,” and the services are typically evaluated according to the proportion of customers who are responded to within targeted response times. To date, only approximation methods have been explored to generate staffing requirements for time‐dependent dual‐class services, but we propose a tractable numerical approach to evaluate system behavior and generate safe minimum staffing levels using mixed discrete‐continuous time Markov chains (MDCTMCs). Our approach is delicate in that it accounts for the behavior of the system under a number of different rules that may be imposed on staff if they are busy when due to leave and involves explicitly calculating delay distributions for two customer classes. Ultimately, we embed our methodology in a proposed extension of the Euler method, coined Euler Pri, that can cope with two customer classes, and use it to recommend staffing levels for the Welsh Ambulance Service Trust (WAST).     

Censored regression with the multistate accelerated sojourn times model

Many disease processes are characterized by two or more successive health states, and it is often of interest and importance to assess state-specific covariate effects. However, with incomplete follow-up data such inference has not been satisfactorily addressed in the literature. We model the logarithm-transformed sojourn time in each state as linearly related to the covariates; however, neither the distributional form of the error term nor the dependence structure of the states needs to be specified. We propose a regression procedure to accommodate incomplete follow-up data. Asymptotic theory is presented, along with some tools for goodness-of-fit diagnostics. Simulation studies show that the proposal is reliable for practical use. We illustrate it by application to a cancer clinical trial.     

Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests

Summary. Models for multiple-test screening data generally require the assumption that the tests are independent conditional on disease state. This assumption may be unreasonable, especially when the biological basis of the tests is the same. We propose a model that allows for correlation between two diagnostic test results. Since models that incorporate test correlation involve more parameters than can be estimated with the available data, posterior inferences will depend more heavily on prior distributions, even with large sample sizes. If we have reasonably accurate information about one of the two screening tests (perhaps the standard currently used test) or the prevalences of the populations tested, accurate inferences about all the parameters, including the test correlation, are possible. We present a model for evaluating dependent diagnostic tests and analyse real and simulated data sets. Our analysis shows that, when the tests are correlated, a model that assumes conditional independence can perform very poorly. We recommend that, if the tests are only moderately accurate and measure the same biological responses, researchers use the dependence model for their analyses.