不完全信息动态博弈视角的农村土地征收治理路径优化

农村土地征收对城乡发展具有重要作用,当前治理路径需进一步优化。利用不完全信息动态博弈模型分析征地困境,并据此探讨优化征地活动的路径,为土地征收工作改进提供参考。研究认为：在征地博弈中,地方政府的类型及农民对该类型的先验概率分布对博弈结果会产生重要影响,这也是引起征地实际偏离社会最优征地选择的重要原因。为优化现实中的征地活动,可通过调整征地博弈的收益矩阵、降低农民维权成本等措施来增加对征地活动的约束;同时,对地方政府类型及农民对此的信念进行调整也必不可少。     

A Parametric Bootstrap Solution to Two-way MANOVA without Interaction under Heteroscedasticity: Fixed and Mixed Models

In this article, we propose a parametric bootstrap (PB) test for heteroscedastic two-way multivariate analysis of variance without Interaction. For the problem of testing equal main effects of factors, we obtain a PB approach and compare it with existing modified Brown–Forsythe (MBF) test and approximate Hotelling T² (AHT) test by an extensive simulation study. The PB test is a symmetric function in samples, and does not depend on the chosen weights used to define the parameters uniquely. Simulation results indicate that the PB test performs satisfactorily for various cell sizes and parameter configurations when the homogeneity assumption is seriously violated, and tends to outperform the AHT test for moderate or larger samples in terms of power and controlling size. The MBF test, the AHT test, and the PB test have similar robustness to violations of underlying assumptions. It is also noted that the same PB test can be used to test the significance of random effect vector in a two-way multivariate mixed effects model with unequal cell covariance matrices.     

产教深度融合的MPAcc“财经素质链”人才培养模式创新与实践

深化产教融合,促进教育链、人才链、产业链、创新链有机衔接,是新时代教育服务经济社会发展和民族振兴的战略性路径。传统会计专业硕士人才培养忽略了产教深度融合的作用,妨碍人才培养结构优化与质量提升。重庆工商大学会计学院遵循“人才发展以用为本”理念,强化产教深度融合的实践本位,突出多元协同的培养过程,构建了产教深度融合的MPAcc“财经素质链”人才培养模式,即以产业需求为导向,确立人才培养目标;以财经素质链为核心,设置模块化课程体系;以产教深度融合为抓手,构建内外协同育人平台;以多元协同为手段,构建双向协同育人平台;以“五大建设”为载体,强化产教深度融合落地实践,取得了显著成效。     

A bayesian analysis of the multinomial model for a dichotomous response with nonrespondents

This paper studies the mu1tinomial model 2x2 contingency table data with some cell counts missing .Various hypotheses of interest including row-column independence are tested by using Bayes factors which represent the ratio of the posterior odds to the prior odds for the null hypothesis. The Dirichlet-Beta family of prior distributions is considered for the multinomial parameters cond itional on the complement of the null hypothesis. The Bayes factor for the incomplete data is a mixture of the Bayes factors for different possibilities for the full data.     

Analysis of incomplete data under non-random mechanisms: bayesian inference

Inferences are made concerning population proportions when data are not missing at random.Both one sample and two sample situations are considered with examples in clinical trials.The one samplesituation involves the existence of response related incomplete data in a study conducted to make inferences involving the proportion. The two sample problem involves comparing two treatments in clinical trials when there exists dropouts due to both the treatment and the response to the treatment.Bayes procedures are used in estimating parameters of interest and testing hypotheses of interest in these two situations. An ad-hoc approach to the classical inference is presented for each ofthe two situations and compared with the Bayesian approach discussed. To illustrate the theory developed, data from clinical trials of severe head trauma patients at the Medical College of Virginia Head Injury Center from 1984 to 1987 is considered     

Analysis of multiple sets of incomplete rankings

The L statistic is extended to allow incomplete or partial rankings within groups. As a special case of these extensions, tests for ofered alternatives analogous to the page test are provided for other than complete rankings. A two group illustration is provided by an alternative analysis of the Bradley and Terry taste-testing experiment on pork-roasts.     

Forms for the distribution of ss ilkptxcxtt statistic for bivariate sphericity

This paper examines the nonnuU distribution of the ellipticity statistic used for testing sphericity in a bivariate normal population. The distribution function is expressed as mixtures of F distribution functions, where the mixing probabilities are binomial and mgatiire binomial. A simple approximation to the distribution is suggested and examined     

The stability of several measures of association in small contingency tables

Measures of association are often used to describe the relationship between row and column variables in two—dimensional contingency tables. It is not uncommon in biomedical research to categorize continuous variables to obtain a two—dimensional table. In these situations it is desirable that the measure of association not be too sensitive to changes in the number of categories or to the choice of cut points. To accomplish this objective we attempt to find a measure of association that closely approximates the corresponding measure of association for the underlying distribution.Measures that are close to the underlying measure for various table sizes andcutpoints are called stable measures.     

Comparison of algorithms for replacing missing data in discriminant analysis

We examined the impact of different methods for replacing missing data in discriminant analyses conducted on randomly generated samples from multivariate normal and non-normal distributions. The probabilities of correct classification were obtained for these discriminant analyses before and after randomly deleting data as well as after deleted data were replaced using: (1) variable means, (2) principal component projections, and (3) the EM algorithm. Populations compared were: (1) multivariate normal with covariance matrices ∑₁=∑₂, (2) multivariate normal with ∑₁≠∑₂ and (3) multivariate non-normal with ∑₁=∑₂. Differences in the probabilities of correct classification were most evident for populations with small Mahalanobis distances or high proportions of missing data. The three replacement methods performed similarly but all were better than non - replacement.