桂平文化旅游开发研究

桂平文化类型多样,比较有特点的是区域内的民俗文化、宗教文化和历史文化,这些文化资源以其独特的风格成为地区垄断性的旅游产品。针对桂平文化旅游开发中的现状问题,从品牌建设,宣传手段,人才培养,可持续发展等方面提出发展对策。     

广西非物质文化遗产保护问题与对策

广西有12个世居民族,各民族在长期的历史发展进程中,不仅创造了大量有形的物质文化遗产,而且创造了丰富的非物质文化遗产,这些遗产承载着民族精神和情感,维系着国家统一和民族团结,联系着世界的人类智慧,是广西各民族千百年来创造的智慧结晶,是传承各民族文化根脉的宝贵资源财富。广西非物质文化遗产保护工作近年取得了一定的成绩,但也面临不少问题需要解决。     

高校体育教学改革的思考与探讨——以新疆大学体育选项课为例

高校体育课选项教学的实施中,暴露出了一些令人深思的问题,说到底是对选项教学增强学生体质、促进学生身心全面发展的目的理解偏误。应从端正教学目的着力,在引导学生正确选项、重视体能练习、教学手段多样化、考核评价方法灵活化等方面多动脑筋,使选项教学达到理想的目标。     

What are you calling intuitive? Subject heterogeneity as a driver of response times in an impunity game

Studies on the intuitive or deliberate nature of human actions often use time constraints for identification, assuming that constrained individuals fall back to intuitive behavior. This identification strategy disregards individual heterogeneity and self-priming, i.e. the behavioral rule that subjects can form during the instructions phase, and then apply irrespective of the time constraint. We use respondent data from an impunity game as an example of how subject heterogeneity can drive results. 24 respondents face 240 more or less unfair allocation proposals out of a small or large pie and can accept or reject the offer. Upon rejection respondents burn their own money, but not the proposer’s. Respondents decisions are communicated to the proposer. On average, emotional rejections take longer than deliberate acceptances. Including individual heterogeneity, though, we find that subjects who mostly accept (reject) take more time to reject (accept). Faster decisions are the ones conforming with the modal early reaction. We attribute this finding to heterogeneity in self-priming. Since self-priming is orthogonal to time constraints, it has the capacity to invalidate their use in the identification of dual decision modes.     

VARIABLE SELECTION AND INTERPRETATION OF COVARIANCE PRINCIPAL COMPONENTS

In practice, when a principal component analysis is applied on a large number of variables the resultant principal components may not be easy to interpret, as each principal component is a linear combination of all the original variables. Selection of a subset of variables that contains, in some sense, as much information as possible and enhances the interpretations of the first few covariance principal components is one possible approach to tackle this problem. This paper describes several variable selection criteria and investigates which criteria are best for this purpose. Although some criteria are shown to be better than others, the main message of this study is that it is unwise to rely on only one or two criteria. It is also clear that the interdependence between variables and the choice of how to measure closeness between the original components and those using subsets of variables are both important in determining the best criteria to use.     

ESTIMATION PROCEDURES FOR CATEGORICAL SURVEY DATA WITH NONIGNORABLE NONRESPONSE

We consider surveys with one or more callbacks and use a series of logistic regressions to model the probabilities of nonresponse at first contact and subsequent callbacks. These probabilities are allowed to depend on covariates as well as the categorical variable of interest and so the nonresponse mechanism is nonignorable. Explicit formulae for the score functions and information matrices are given for some important special cases to facilitate implementation of the method of scoring for obtaining maximum likelihood estimates of the model parameters. For estimating finite population quantities, we suggest the imputation and prediction approaches as alternatives to weighting adjustment. Simulation results suggest that the proposed methods work well in reducing the bias due to nonresponse. In our study, the imputation and prediction approaches perform better than weighting adjustment and they continue to perform quite well in simulations involving misspecified response models.     

The beta distribution as a latent response model for ordinal data (I): Estimation of location and dispersion parameters

Ordinal data are often modeled using a continuous latent response distribution, which is partially observed through windows of adjacent intervals defined by cutpoints. In this paper we propose the beta distribution as a model for the latent response. The beta distribution has several advantages over the other common distributions used, e.g. , normal and logistic. In particular, it enables separate modeling of location and dispersion effects which is essential in the Taguchi method of robust design. First, we study the problem of estimating the location and dispersion parameters of a single beta distribution (representing a single treatment) from ordinal data assuming known equispaced cutpoints. Two methods of estimation are compared: the maximum likelihood method and the method of moments. Two methods of treating the data are considered: in raw discrete form and in smoothed continuousized form. A large scale simulation study is carried out to compare the different methods. The mean square errors of the estimates are obtained under a variety of parameter configurations. Comparisons are made based on the ratios of the mean square errors (called the relative efficiencies). No method is universally the best, but the maximum likelihood method using continuousized data is found to perform generally well, especially for estimating the dispersion parameter. This method is also computationally much faster than the other methods and does not experience convergence difficulties in case of sparse or empty cells. Next, the problem of estimating unknown cutpoints is addressed. Here the multiple treatments setup is considered since in an actual application, cutpoints are common to all treatments, and must be estimated from all the data. A two-step iterative algorithm is proposed for estimating the location and dispersion parameters of the treatments, and the cutpoints. The proposed beta model and McCullagh's (1980) proportional odds model are compared by fitting them to two real data sets.     

C194. A nearly flat prior,some unsolved problexs,anb soxe comments about improper priors

Two different approaches to obtaining finite-sample corrections to score tests are the analytical and the computational approaches. The former is based either on a Bartletttype correction to the test statistic or on the inversion of an Edgeworth expansion to its null distribution. The latter, on the other hand, is usually based on a bootstrapping resampling scheme. This paper provides a numerical comparison of the size and power properties of these two approaches both under correct model specification and under model misspecification.     

Estimation of an indicator of the representativeness of survey response

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. Schouten et al. (2009) proposed an alternative indicator, which they refer to as an indicator of representativeness or R-indicator. This indicator measures the variability of the probabilities of response for units in the population. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.     

A Parameter-Tuned Genetic Algorithm for Economic-Statistical Design of Variable Sampling Interval X-Bar Control Charts for Non-Normal Correlated Samples

Among innovations and improvements that occurred in the past two decades on the techniques and tools used for statistical process control (SPC), adaptive control charts have shown to substantially improve the statistical and/or economical performances. Variable sampling intervals (VSI) control charts are one of the most applied types of the adaptive control charts and have shown to be faster than traditional Shewhart control charts in identifying small changes of concerned quality characteristics. While in the designing procedure of the VSI control charts the data or measurements are assumed independent normal observations, in real situations the validity of these assumptions is under question in many processes. This article develops an economic-statistical design of a VSI X-bar control chart under non-normality and correlation. Since the proposed design consists of a complex nonlinear cost model that cannot be solved using a classical optimization method, a genetic algorithm (GA) is employed to solve it. Moreover, to improve the performances, response surface methodology (RSM) is employed to calibrate GA parameters. The solution procedure, efficiency, and sensitivity analysis of the proposed design are demonstrated through a numerical illustration at the end.