One-shot decisions under Linear Partial Information

This paper purports to make a contribution to the analysis of a class of decisions that has received little attention in the literature, although it appears to be of considerable importance. Certain decisions cannot be repeated but must be made under fuzziness in the sense that state probabilities are not exactly known (LPI-fuzziness). The analysis of Linear Partial Information is applied to the principle of neglecting small probabilities found by Allais (1953), enabling the decision maker to break away from the maxmin criterion. By systematic exploitation of the fuzzy information available, strategies are shown to exist that provide payoffs whose lower bound exceeds the maxmin benchmark with sufficiently high probability. The same methodology is shown to be useful for dealing with the case of only ordinal preference orderings that are so typical of those crucial decisions that may be made only once in a lifetime.     

《春秋·公羊传》文本语境下的“孟子论舜”

“孟子论舜”成为学术界对于儒家“亲亲伦理”争鸣的焦点所在,目前双方对此问题的析正难以取得突破.以《春秋·公羊传》作为文本依托,从“亲亲伦理”材料扩展和类比解读的方法展开,尽可能地还原“亲亲伦理”本始意义,并对之进行恰当的价值定性.进而为“孟子论舜”评定提供新的视角,即“孟子论舜”中维护亲情的主张无可厚非,可基于道德义务及法律强制性规定的手段并不可取.     

英、汉语言中的音译借词与谐音词初探

世界上所有活的语言都随着时间的推移和社会的变迁而不断发展变化,任何语言在其发展过程中,都会从世界其他各个主要语种吸收不少借词,汉语也不例外。本文对英语中的汉语借词和汉语中的英语借词进行探索,着重对主要借入方式中的音译借词即谐音词的特征,产生的根源和发展的趋势进行分析研究,从中找出某些规律以提高语言习的效果。     

Inferring Labor Income Risk and Partial Insurance From Economic Choices

This paper uses the information contained in the joint dynamics of individuals' labor earnings and consumption‐choice decisions to quantify both the amount of income risk that individuals face and the extent to which they have access to informal insurance against this risk. We accomplish this task by using indirect inference to estimate a structural consumption–savings model, in which individuals both learn about the nature of their income process and partly insure shocks via informal mechanisms. In this framework, we estimate (i) the degree of partial insurance, (ii) the extent of systematic differences in income growth rates, (iii) the precision with which individuals know their own income growth rates when they begin their working lives, (iv) the persistence of typical labor income shocks, (v) the tightness of borrowing constraints, and (vi) the amount of measurement error in the data. In implementing indirect inference, we find that an auxiliary model that approximates the true structural equations of the model (which are not estimable) works very well, with negligible small sample bias. The main substantive findings are that income shocks are moderately persistent, systematic differences in income growth rates are large, individuals have substantial amounts of information about their income growth rates, and about one‐half of income shocks are smoothed via partial insurance. Putting these findings together, the amount of uninsurable lifetime income risk that individuals perceive is substantially smaller than what is typically assumed in calibrated macroeconomic models with incomplete markets.     

Partial order relations for classification comparisons

The Bayes classification rule offers the optimal classifier, minimizing the classification error rate, whereas the Neyman–Pearson lemma offers the optimal family of classifiers to maximize the detection rate for any given false alarm rate. These motivate studies on comparing classifiers based on similarities between the classifiers and the optimal. In this article, we define partial order relations on classifiers and families of classifiers, based on rankings of rate function values and rankings of test function values, respectively. Each partial order relation provides a sufficient condition, which yields better classification error rates or better performance on the receiver operating characteristic analysis. Various examples and applications of the partial order theorems are discussed to provide comparisons of classifiers and families of classifiers, including the comparison of cross-validation methods, training data that contains outliers, and labelling errors in training data. The Canadian Journal of Statistics 48: 152–166; 2020 © 2019 Statistical Society of Canada     

Specifications of a continual reassessment method design for phase I trials of combined drugs

In studies of combinations of agents in phase I oncology trials, the dose–toxicity relationship may not be monotone for all combinations, in which case the toxicity probabilities follow a partial order. The continual reassessment method for partial orders (PO‐CRM) is a design for phase I trials of combinations that leans upon identifying possible complete orders associated with the partial order. This article addresses some practical design considerations not previously undertaken when describing the PO‐CRM. We describe an approach in choosing a proper subset of possible orderings, formulated according to the known toxicity relationships within a matrix of combination therapies. Other design issues, such as working model selection and stopping rules, are also discussed. We demonstrate the practical ability of PO‐CRM as a phase I design for combinations through its use in a recent trial designed at the University of Virginia Cancer Center. Copyright © 2013 John Wiley & Sons, Ltd.     

On ratio estimators in two phase sampling under size stratification and estimation over two successive occasions

Adopting size stratification when the auxiliary character is approximated by a continuous uniform distribution, a double sampling ratio strategy has been suggested. this sampling strategy has been compared with some of the known sampling strategies. the applicability of this strategy to sampling on two successive occasions has also been investigated     

Bayesiam simultaneous estimation for several multinomial distributions

A Bayesian method is proposed for estimating the cell probabilities of several multinomial distributions. Parameters of different distributions are taken to be a priori exchangeable. The prior specification is based upon mixtures of a hierarchical distribution, referred to as the multivariate “Dirichlet-Dirichlet” distribution. The analysis is facilitated by a multinomial approximation relating to the multinomial-Dirichlet distribution. The posterior estimates depend upon measures of entropy for the various distributions and shrink the individual observed proportions towards values obtained by pooling the data across the distributions. As well as incorporating prior information they are particularly useful when some of the cell frequencies are zero. We use them to investigate a numerical classification of males of various vocations, according to cause of death.     

Empirical likelihood based diagnostics for heteroscedasticity in semiparametric varying-coefficient partially linear errors-in-variables models

In this paper, we propose an empirical likelihood based diagnostic technique for heteroscedasticity in the semiparametric varying-coefficient partially linear errors-in-variables models. Under mild conditions, a nonparametric version of Wilk’s theorem is derived. Simulation results reveal that our test performs well in both size and power.