Tests for assessment of agreement using probability criteria

For the assessment of agreement using probability criteria, we obtain an exact test, and for sample sizes exceeding 30, we give a bootstrap-t$t$ test that is remarkably accurate. We show that for assessing agreement, the total deviation index approach of Lin [2000. Total deviation index for measuring individual agreement with applications in laboratory performance and bioequivalence. Statist. Med. 19, 255–270] is not consistent and may not preserve its asymptotic nominal level, and that the coverage probability approach of Lin et al. [2002. Statistical methods in assessing agreement: models, issues and tools. J. Amer. Statist. Assoc. 97, 257–270] is overly conservative for moderate sample sizes. We also show that the nearly unbiased test of Wang and Hwang [2001. A nearly unbiased test for individual bioequivalence problems using probability criteria. J. Statist. Plann. Inference 99, 41–58] may be liberal for large sample sizes, and suggest a minor modification that gives numerically equivalent approximation to the exact test for sample sizes 30 or less. We present a simple and accurate sample size formula for planning studies on assessing agreement, and illustrate our methodology with a real data set from the literature.     

Difficulties in the use of auxiliary variables in Markov chain Monte Carlo methods

Markov chain Monte Carlo (MCMC) methods are now widely used in a diverse range of application areas to tackle previously intractable problems. Difficult questions remain, however, in designing MCMC samplers for problems exhibiting severe multimodality where standard methods may exhibit prohibitively slow movement around the state space. Auxiliary variable methods, sometimes together with multigrid ideas, have been proposed as one possible way forward. Initial disappointing experiments have led to data-driven modifications of the methods. In this paper, these suggestions are investigated for lattice data such as is found in imaging and some spatial applications. The results suggest that adapting the auxiliary variables to the specific application is beneficial. However the form of adaptation needed and the extent of the resulting benefits are not always clear-cut.     

适用于具有未知或变化时滞的PID自适应控制器

本文通过把Smith型预估和补偿方法与PID控制算法相结合,提出了一种新的、简单而有效的SIOS自适应控制算法,此法便于在实际中应用推广。文末给出了数字仿真与物理仿真结果。     

A New Analysis Strategy for Designs With Complex Aliasing

Abstract

Nonregular designs are popular in planning industrial experiments for their run-size economy. These designs often produce partially aliased effects, where the effects of different factors cannot be completely separated from each other. In this article, we propose applying an adaptive lasso regression as an analytical tool for designs with complex aliasing. Its utility compared to traditional methods is demonstrated by analyzing real-life experimental data and simulation studies.     

Adaptive Modeling Procedure Selection by Data Perturbation

Many procedures have been developed to deal with the high-dimensional problem that is emerging in various business and economics areas. To evaluate and compare these procedures, modeling uncertainty caused by model selection and parameter estimation has to be assessed and integrated into a modeling process. To do this, a data perturbation method estimates the modeling uncertainty inherited in a selection process by perturbing the data. Critical to data perturbation is the size of perturbation, as the perturbed data should resemble the original dataset. To account for the modeling uncertainty, we derive the optimal size of perturbation, which adapts to the data, the model space, and other relevant factors in the context of linear regression. On this basis, we develop an adaptive data-perturbation method that, unlike its nonadaptive counterpart, performs well in different situations. This leads to a data-adaptive model selection method. Both theoretical and numerical analysis suggest that the data-adaptive model selection method adapts to distinct situations in that it yields consistent model selection and optimal prediction, without knowing which situation exists a priori. The proposed method is applied to real data from the commodity market and outperforms its competitors in terms of price forecasting accuracy.     

Performance of Kibria's Method for the Heteroscedastic Ridge Regression Model: Some Monte Carlo Evidence

It is common for a linear regression model that the error terms display some form of heteroscedasticity and at the same time, the regressors are also linearly correlated. Both of these problems have serious impact on the ordinary least squares (OLS) estimates. In the presence of heteroscedasticity, the OLS estimator becomes inefficient and the similar adverse impact can also be found on the ridge regression estimator that is alternatively used to cope with the problem of multicollinearity. In the available literature, the adaptive estimator has been established to be more efficient than the OLS estimator when there is heteroscedasticity of unknown form. The present article proposes the similar adaptation for the ridge regression setting with an attempt to have more efficient estimator. Our numerical results, based on the Monte Carlo simulations, provide very attractive performance of the proposed estimator in terms of efficiency. Three different existing methods have been used for the selection of biasing parameter. Moreover, three different distributions of the error term have been studied to evaluate the proposed estimator and these are normal, Student's t and F distribution.     

Adaptive ridge estimator in a linear regression model with spherically symmetric error under constraint

Abstract

We consider adaptive ridge regression estimators in the general linear model with homogeneous spherically symmetric errors. A restriction on the parameter of regression is considered. We assume that all components are non negative (i.e. on the positive orthant). For this setting, we produce under general quadratic loss such estimators whose risk function dominates that of the least squares provided the number of regressors in the least fore.     

Robust Sparse Regression with High-Breakdown Value

Penalized least squares estimators are sensitive to the influence of outliers like the ordinary least squares estimator. We propose a sparse regression estimator for robust variable selection and estimation based on a robust initial estimator. It is proven that our estimator has at least the same breakdown value as the initial estimator. Numerical examples are presented to illustrate our method.     

Multiproduct cost-volume-profit model with product limits

A multiproduct cost-volume-profit model is extended to incorporate sales or product capacity limits for each product, required rate of return on sales, and the tax rate depending on profit levels as determined by the government. The model also deals with overhead costs that are traceable to a group of products but which cannot be allocated to individual products within the group to any reasonable accuracy. To solve the model, an algorithm is constructed to determine the required volume for each product that will achieve the best possible rate of return on sales revenue. Based upon the dBase Database Management System and the CVP model, a user-friendly menu-driven interactive decision support system is developed. The model algorithm and decision support system is illustrated with an example consisting of five products. Various reports generated by the interactive decision support system are also presented.     

从信息传递角度看旅游资料英译的变译

新疆独特的旅游资源决定着对外宣传上的特殊性,从信息传递的角度对原作所提供的多源信息进行加工,然后再通过翻译策略向国外游客传递当地独特的旅游文化信息更有助于提高旅游资料的翻译质量。文章在变译理论中的减译、增译和改译等变通手段可以实现对冗余性信息采用,对离散性信息采用信息转换法,旨在为提高旅游资料的翻译质量提供一个新的研究视角及评估标准。