优化韩国文学课的实践探索

目前的韩国文学教学中存在较为严重的问题,包括文学课的教材、教法等方面。通过对韩国文学课课程的优化整合,同时辅以教法上的调整,设计出一套立体式教学新模式,提高了韩国文学课课堂的教学质量。     

A note on determining an optimal target by considering the dependence of holding costs and the quality characteristics

Products that do not meet the specification criteria of an intended buyer represent a challenge to the producer in maximizing profits. To understand the value of the optimal process target (OPT) set at a profit-maximizing level, a model was developed by Shao et al. (1999) involving multiple markets and finished products having holding costs independent from their quality. Investigation in cases considered previously has involved holding costs as a fixed amount or as a normal random variable independent of the quality characteristic (QC) of the product. Less specific in nature, this study considers more general cases in which the HC can be a truncated normal random variable, which is dependent on the QC of the product.     

Adaptive designs for selecting drug combinations based on efficacy–toxicity response

We propose a new adaptive procedure for dose-finding in clinical trials with combination of two drugs when both efficacy and toxicity responses are available. We model the distribution of this bivariate binary endpoint using the bivariate probit model. The analytic formulae for the Fisher information matrix are obtained, that form the basis for derivation of the locally optimal, minimax, Bayesian, and adaptive designs in the framework of optimal design theory.     

The Role of Risk and Risk Aversion in an Individual's Migration Decision

Abstract

This paper proposes a simple, partial equilibrium model for studying an individual's migration decisions. It shows that an individual may choose to delay migration when the condition appears to be favorable, giving rise to the “waiting” behavior observed in the data. Using a closed-form solution, it also examines how the duration of the waiting is affected by a number of economic factors such as the risks associated with the wages in regions of origin and destination, the individual's attitude toward risk, etc.     

Robust estimation of extremes

We consider the problem of making inferences about extreme values from a sample. The underlying model distribution is the generalized extreme-value (GEV) distribution, and our interest is in estimating the parameters and quantiles of the distribution robustly. In doing this we find estimates for the GEV parameters based on that part of the data which is well fitted by a GEV distribution. The robust procedure will assign weights between 0 and 1 to each data point. A weight near 0 indicates that the data point is not well modelled by the GEV distribution which fits the points with weights at or near 1. On the basis of these weights we are able to assess the validity of a GEV model for our data. It is important that the observations with low weights be carefully assessed to determine whether diey are valid observations or not. If they are, we must examine whether our data could be generated by a mixture of GEV distributions or whether some other process is involved in generating the data. This process will require careful consideration of die subject matter area which led to the data. The robust estimation techniques are based on optimal B-robust estimates. Their performance is compared to the probability-weighted moment estimates of Hosking et al. (1985) in both simulated and real data.     

On general consistency in deconvolution mode estimation

This paper addresses the problem of estimating the mode of a density function based on contaminated data. Unlike conventional methods, which are based on localizing the maximum of a density estimator, we introduce a procedure which requires computation of the maximum among finitely many quantities only. We show that our estimator is strongly consistent under very weak conditions, where not even continuity of the density at the mode is required; moreover, we show that the estimator achieves optimal convergence rates under common smoothness and sharpness constraints. Some numerical simulations are provided.     

Incorporating Price and Replacement Purchases in New Product Diffusion Models for Consumer Durables*

The article presents theoretical and empirical research findings which incorporate price and replacement purchases in new product diffusion models. On the theoretical side, this paper characterizes, qualitatively, optimum pricing policies for new products. Possible entry of rivals is not considered, but repeat sales, cost learning dynamics and discounting of future profit streams are allowed. Theoretical research findings suggest that the inclusion of repeat purchases in the diffusion model significantly changes the derived optimal pricing policy even if replacements were not price dependent. On the empirical side, alternative first purchase and repeat purchase models have been estimated and compared using nonlinear procedures. The diffusion data analyzed is related to nine consumer durables. Empirical research findings suggest that, for the considered product categories, diffusion is basically an imitative process, price can affect first and replacement purchases, and unit production cost is a decreasing function of cumulative first purchases. Managerial implications of the research findings are also discussed.     

Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action

Many Bayes factors have been proposed for comparing population means in two-sample (independent samples) studies. Recently, Wang and Liu presented an “objective” Bayes factor (BF) as an alternative to a “subjective” one presented by Gönen et al. Their report was evidently intended to show the superiority of their BF based on “undesirable behavior” of the latter. A wonderful aspect of Bayesian models is that they provide an opportunity to “lay all cards on the table.” What distinguishes the various BFs in the two-sample problem is the choice of priors (cards) for the model parameters. This article discusses desiderata of BFs that have been proposed, and proposes a new criterion to compare BFs, no matter whether subjectively or objectively determined. A BF may be preferred if it correctly classifies the data as coming from the correct model most often. The criterion is based on a famous result in classification theory to minimize the total probability of misclassification. This criterion is objective, easily verified by simulation, shows clearly the effects (positive or negative) of assuming particular priors, provides new insights into the appropriateness of BFs in general, and provides a new answer to the question, “Which BF is best?”     

Asymptotic optimality of a two-stage procedure in Bayes sequential estimation

The problem of sequential estimation of the mean with quadratic loss and fixed cost per observation is considered within the Bayesian framework. Instead of fully sequential sampling, a two-stage sampling technique is introduced to solve the problem. The proposed two-stage procedure is robust in the sense that it does not depend on the distribution of outcome variables and the prior. It is shown to be asymptotically not worse than the optimal fixed-sample-size procedures for the arbitrary distributions, and to be asymptotically Bayes for the distributions of one-parameter exponential family.     

Optimal cross-over designs for nonlinear mixed models using a first-order expansion

We consider the construction of optimal cross-over designs for nonlinear mixed effect models based on the first-order expansion. We show that for AB/BA designs a balanced subject allocation is optimal when the parameters depend on treatments only. For multiple period, multiple sequence designs, uniform designs are optimal among dual balanced designs under the same conditions. As a by-product, the same results hold for multivariate linear mixed models with variances depending on treatments.