Local linear estimation of a generalized regression function with functional dependent data

This work deals with a local linear non parametric estimation of the generalized regression function in the case of a scalar response variable given a random variable taking values in a semimetric space. The rates of pointwise and uniform almost complete convergence are established for the studied estimator when the sample is an α-mixing sequence. Two real datasets are used to illustrate the performance of a studied estimator with respect to the kernel method.     

Optimum invariant tests on discriminant coefficients or means of multinormal population with additional information

Some optimum invariant tests are derived for certain hypotheses on discriminant coefficients or means when some extra observations are available on some components of a random vector X, which is distributed as N(μ,Σ), where μ and Σ are unknown.     

Optimal covariance estimation of discrete-time locally self-similar processes in time-scale and ambiguity domains

This paper investigates the optimal estimate of the covariance function in the sense of mean-square of errors, for the class of discrete-time locally self-similar processes. The covariance function is estimated in time-scale and ambiguity domains. Since the class of estimators is completely characterized in terms of kernels, the problem is reduced to finding the optimal kernel, which is obtained in time-scale domain. Also, the optimal kernel is computed for two classes of discrete-time locally self-similar and locally self-similar chirp processes. Furthermore, it is shown that the proposed method gives more accurate estimate than the ordinary methods for non stationary processes.     

Selecting more reliable weibull populations than a control

This paper deals with the selection of Weibull populations that are more reliable than a control population at some specified time. In the case when the shape parameters are known, a locally optimal selection rule is derived. From this rule, a modified one is proposed for the case when the shape parameter is unknown but has a known prior distribution. Simulation study shows that this modified rule is quite robust.     

Dynamic models for spatiotemporal data

We propose a model for non-stationary spatiotemporal data. To account for spatial variability, we model the mean function at each time period as a locally weighted mixture of linear regressions. To incorporate temporal variation, we allow the regression coefficients to change through time. The model is cast in a Gaussian state space framework, which allows us to include temporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabilistic inference for the parameters, interpolations and forecasts. To illustrate the model, we apply it to two large environmental data sets: tropical rainfall levels and Atlantic Ocean temperatures.     

Testing homogeneity of control and treatment populations — local optimality and related issues

Durairajan and Raman (1996 a, b) studied the robustness of Locally most powerful invariant (LMPI) tests for compound normal model in control and treatment populations. In the present paper, the Locally most powerful (LMP) tests are constructed for no contamination in normal mixture model through testing the parameter of mixture of distributions and the mixing proportion. The expected performance of LMP tests are compared using Efron's Statistical Curvature on the lines of Sen Gupta and Pal (1991). The Locally most powerful similar (LMPS) tests for the equality of control and treatment populations in the presence of nuisance parameters are also constructed. Further, the null and non-null distributions of the test statistics are derived and some power computations are made. Received: September 1, 1999; revised version: August 31, 2000     

国际广告传播策略及其影响因素分析

在经济全球化背景下,随着企业跨国营销活动的增多,国际广告传播日益兴盛。国际广告传播面临的一个重要问题就是各国在政治、经济、文化背景等方面的差异化,需要差异化对待,而每个品牌又力求在全球市场上保持一个统一的形象。因此,"全球化思考本土化执行"策略成为多数企业进行国际广告传播的一个选择。而其中全球化及本土化策略的选择又受到广告、产品、目标国差异化程度等多个因素的影响。在进行国际广告传播时,应深入把握这些因素的影响,作为执行"全球化思考本土化执行"策略的依据。     

Locally D-Optimal Design for a Logit Model in Discrete Choice Experiment

Conjoint analysis is concerned with understanding how people make choice between products or services (alternatives) or a combination of product and service (choice set), so that businesses can design new products or services that better meet customers needs. In this situation, logit model (Multinomial Logit Model) has been used to calculate the probability related to choosing an alternative in a choice set with the highest utility. Then I considered several choice sets instead of one. In this article, I have used the locally D-optimal design for the combination of the level of attributes (two attributes each with two levels) to create alternatives. The optimal combination of alternatives in choice sets which help us to have a suitable choice.     

Optimal design for discrete choice experiments

In this paper we derive locally optimal designs for discrete choice experiments. As in Kanninen (2002) we consider a multinomial logistic model, which contains various qualitative attributes as well as a quantitative one, which may range over a sufficiently large interval. The derived optimal designs improve upon those given in the literature, but have the feature that every choice set contains alternatives, which coincide in all but the quantitative attributes. The multinomial logistic model will then lead to a response behavior, which is apparently unrealistic.     

On the Functional Approach to Bayesian Efficient Designs for Nonlinear Regression Models

The present article is devoted to an extension of the functional approach elaborated in the book Melas (2006 Melas , V. B. ( 2006 ). Functional Approach to Optimal Experimental Design . Lecture Notes in Statistics , Vol. 184. Heidelberg : Springer . [Google Scholar]) for studying optimal designs in linear and nonlinear regression models. Here we consider Bayesian efficient designs for nonlinear models under the standard assumptions on the observational errors. Sufficient conditions for uniqueness of locally optimal and Bayesian efficient designs for common optimality criteria are given. L-efficient Bayesian designs are constructed and investigated for a special nonlinear regression model of a rational form as an illustration of our main results. This model is interesting in both a practical and a theoretical sense.