农民工转户行为的演化博弈分析——以重庆为例

户籍制度是一项包含多种利益分配的身份制度,户籍改革意味着利益结构的大调整,必然面临复杂的博弈.在自愿选择的前提下,中国农民工户籍转变过程逐步趋向于政府与农民工基于成本利益估量的双向博弈.文章以博弈方法分析了当下农民工转户过程中农民工和政府策略影响因素及可能的均衡路径,得出结论为:现阶段政府推动农民工市民化的经济与政治利益大于所需成本,有动力主动推动农民工转户进程;农民工出于对转户后生活不确定性的考虑,需要政府适当保留其部分既有利益且提供切实有效的社会保障后对于转户才有持续积极性.为了推动农民工市民化进程的可持续稳定前进,政府可能在短期需要付出足够多的经济政策扶持;即便如此,对政府和农民工而言,这仍是一个双赢结果.最后以重庆户籍改革过程中的统计数据验证了分析结论并提供政策启示.     

Adaptive Bayes sum test for the equality of two nonparametric functions

The statistical difference among massive data sets or signals is of interest to many diverse fields including neurophysiology, imaging, engineering, and other related fields. However, such data often have nonlinear curves, depending on spatial patterns, and have non-white noise that leads to difficulties in testing the significant differences between them. In this paper, we propose an adaptive Bayes sum test that can test the significance between two nonlinear curves by taking into account spatial dependence and by reducing the effect of non-white noise. Our approach is developed by adapting the Bayes sum test statistic by Hart [13 J.D. Hart, Frequentist-Bayes lack-of-fit tests based on Laplace approximations, J. Stat. Theory Practice 3 (2009), pp. 681–704. doi: 10.1080/15598608.2009.10411954[Taylor &; Francis Online] , [Google Scholar]]. The spatial pattern is treated through Fourier transformation. Resampling techniques are employed to construct the empirical distribution of test statistic to reduce the effect of non-white noise. A simulation study suggests that our approach performs better than the alternative method, the adaptive Neyman test by Fan and Lin [9 J. Fan and S. Lin, Test of significance when data are curves, J. Amer. Math. Soc. 93 (1997), pp. 1007–1021.[Web of Science ®] , [Google Scholar]]. The usefulness of our approach is demonstrated with an application in the identification of electronic chips as well as an application to test the change of pattern of precipitations.     

Solving Noisy ICA Using Multivariate Wavelet Denoising with an Application to Noisy Latent Variables Regression

A novel approach to solve the independent component analysis (ICA) model in the presence of noise is proposed. We use wavelets as natural denoising tools to solve the noisy ICA model. To do this, we use a multivariate wavelet denoising algorithm allowing spatial and temporal dependency. We propose also using a statistical approach, named nested design of experiments, to select the parameters such as wavelet family and thresholding type. This technique helps us to select more convenient combination of the parameters. This approach could be extended to many other problems in which one needs to choose parameters between many choices. The performance of the proposed method is illustrated on the simulated data and promising results are obtained. Also, the suggested method applied in latent variables regression in the presence of noise on real data. The good results confirm the ability of multivariate wavelet denoising to solving noisy ICA.     

On the distribution of products of independent beta variates

This paper concerns an inquiry into the problem of generating closed form expressions for the cumulative distribution and probability density functions of products of independent beta variates. Recursive analytical procedures for constructing the equational forms of these functions-from their Mellin inversion integral representations, via the Cauchy residue theorem-are described. A numerical example illustrating details of the construction of a computable form of the distribution function of the product of three independent beta variates is also included.     

Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices

We propose a new test procedure for testing linear hypothesis on the mean vectors of normal populations with unequal covariance matrices when the dimensionality, p exceeds the sample size N  , i.e. p/N→c<∞$p/N\to c<\infty$. Our procedure is based on the Dempster trace criterion and is shown to be consistent in high dimensions.     

The Distribution of Sums of I.I.D. Pareto Random Variables with Arbitrary Shape Parameter

Laplace transforms are used to derive an exact expression for the cdf of the sum of n i.i.d. Pareto random variables with common pdf f(x) = (α/β)(1 + x/β)^?α?1 for x > 0, where α > 0 and is not an integer, and β > 0. An attractive feature of this expression is that it involves an integral of non oscillating real-valued functions on the positive real line. Examples of values of cdfs are provided and are compared to those determined via simulations.     

Block–Savits Characterization and Star Ordering of Exponential Mixtures

Block and Savits (1980 Block , H. W. , Savits , T. H. ( 1980 ). Laplace transforms for classes of life distributions . Ann. Probab. 8 : 465 – 474 .[Crossref], [Web of Science ®] , [Google Scholar]) established a characterization of life distributions using the Laplace transform. In this article, we remark that one of the necessary conditions to be IFRA distribution is equivalent to the star ordering of exponential mixtures. It leads to the definition of two new classes of life distributions, called LIFR and LIFRA, and their dual classes: LDFR and LDFRA. It occurs that these classes have many useful aging properties and preserve known reliability operations. Properties of the classes are studied and relations with known classes are established.