Improper priors for translation families

We consider the problem of inference when sampling from a translation family with an improper prior. Properties of the formal Bayes inference will be studied. We give conditions (on the prior and/or the family) guaranteeing the HS-coherence (see Heath and Sudderth, Ann. Statist. 6 (1978), 333–345) of the formal Bayes posterior. Since HS-coherence is equivalent to being a posterior of a finitely additive prior, all coherence results imply the existence of a finitely additive prior which has the formal Bayes inference as a posterior.     

Noninformative priors for linear combinations of the normal means

In this paper, we develop noninformative priors for linear combinations of the means under the normal populations. It turns out that among the reference priors the one-at-a-time reference prior satisfies a second order probability matching criterion. Moreover, the second order probability matching priors match alternative coverage probabilities up to the second order and are also HPD matching priors. Our simulation study indicates that the one-at-a-time reference prior performs better than the other reference priors in terms of matching the target coverage probabilities in a frequentist sense.     

Bayesian Inference Under Partial Prior Information

Partial prior information on the marginal distribution of an observable random variable is considered. When this information is incorporated into the statistical analysis of an assumed parametric model, the posterior inference is typically non‐robust so that no inferential conclusion is obtained. To overcome this difficulty a method based on the standard default prior associated to the model and an intrinsic procedure is proposed. Posterior robustness of the resulting inferences is analysed and some illustrative examples are provided.     

Bayesian estimates in a one-way ANOVA random effects model

Bayesian estimators of variance components are developed, based on posterior mean and posterior mode, respectively, in a one-way ANOVA random effects model with independent prior distributions. The formulas for the proposed estimators are simple. The estimators give sensible results for 'badly-behaved' datasets, where the standard unbiased estimates are negative. They are markedly robust as compared to the existing estimators such as the maximum likelihood estimators and the maximum posterior density estimators.     

Re-examining informative prior elicitation through the lens of Markov chain Monte Carlo methods

Summary.  In recent years, advances in Markov chain Monte Carlo techniques have had a major influence on the practice of Bayesian statistics. An interesting but hitherto largely underexplored corollary of this fact is that Markov chain Monte Carlo techniques make it practical to consider broader classes of informative priors than have been used previously. Conjugate priors, long the workhorse of classic methods for eliciting informative priors, have their roots in a time when modern computational methods were unavailable. In the current environment more attractive alternatives are practicable. A reappraisal of these classic approaches is undertaken, and principles for generating modern elicitation methods are described. A new prior elicitation methodology in accord with these principles is then presented.     

Belief Disagreements and Collateral Constraints

Belief disagreements have been suggested as a major contributing factor to the recent subprime mortgage crisis. This paper theoretically evaluates this hypothesis. I assume that optimists have limited wealth and take on leverage so as to take positions in line with their beliefs. To have a significant effect on asset prices, they need to borrow from traders with pessimistic beliefs using loans collateralized by the asset itself. Since pessimists do not value the collateral as much as optimists do, they are reluctant to lend, which provides an endogenous constraint on optimists' ability to borrow and to influence asset prices. I demonstrate that the tightness of this constraint depends on the nature of belief disagreements. Optimism concerning the probability of downside states has no or little effect on asset prices because these types of optimism are disciplined by this constraint. Instead, optimism concerning the relative probability of upside states could have significant effects on asset prices. This asymmetric disciplining effect is robust to allowing for short selling because pessimists that borrow the asset face a similar endogenous constraint. These results emphasize that what investors disagree about matters for asset prices, to a greater extent than the level of disagreements. When richer contracts are available, relatively complex contracts that resemble some of the recent financial innovations in the mortgage market endogenously emerge to facilitate betting.     

英文化工专利文献中长句翻译的功能对等

化工专利文献属于信息型文本,其翻译应以实现信息传递准确和译语读者接受为准则。英文化工专利文献以结构复杂和层次繁多的长句为特征。本文通过分析英文化工专利文献中7种长句翻译法,探讨如何实现该文本类型翻译中信息功能与交际功能的对等。     

Joint modeling tumor burden and time to event data in oncology trials

The tumor burden (TB) process is postulated to be the primary mechanism through which most anticancer treatments provide benefit. In phase II oncology trials, the biologic effects of a therapeutic agent are often analyzed using conventional endpoints for best response, such as objective response rate and progression‐free survival, both of which causes loss of information. On the other hand, graphical methods including spider plot and waterfall plot lack any statistical inference when there is more than one treatment arm. Therefore, longitudinal analysis of TB data is well recognized as a better approach for treatment evaluation. However, longitudinal TB process suffers from informative missingness because of progression or death. We propose to analyze the treatment effect on tumor growth kinetics using a joint modeling framework accounting for the informative missing mechanism. Our approach is illustrated by multisetting simulation studies and an application to a nonsmall‐cell lung cancer data set. The proposed analyses can be performed in early‐phase clinical trials to better characterize treatment effect and thereby inform decision‐making. Copyright © 2014 John Wiley & Sons, Ltd.     

The stein effect and bayesian analysis: a reexamination

The Stein, that one could improve frequentist risk by combining “independent” problems, has long been an intriguing paradox to statistics. We briefly review the Bayesian view of the paradox, and indicate that previous justifications of the Stein effect, through concerns of “Bayesian robustness,” were misleading. In the course of doing so, several existing robust Bayesian and Stein-effect estimators are compared for a variety of situations.     

A martingale approach to the problem of dimensionality and performance of bayesian classifiers: asymptotic results

A martingale approach to the problem of performance of Bayesian classifiers with increasing feature dimensionality is applied here . Martingale limit theorems are also used to demonstrate that the expected probability of correct classification tends monotonically to unity for two general classification problems.