Maximum correlation for the generalized Sarmanov bivariate distributions

In order to improve the correlation of the traditional Sarmanov distribution, a ‘generalized’ version was introduced earlier by Bairamov et al. (2001). The extent of the improvement in correlation, however, was never investigated in the literature. In this note we compare the two Sarmanov models regarding their maximum correlation. Several examples are given. It is shown that unlike the traditional Sarmanov, the generalized one always has a correlation approaching one regardless of the marginals, as long as the marginals are of the same type. When they are not of the same type, however, the correlation has an upper bound strictly less than one. We find conditions under which the upper bound is attained. Finally, we investigate the rates of convergence to the maximum correlation for the generalized Sarmanov bivariate distributions.     

A new multivariate model involving geometric sums and maxima of exponentials

We study the joint distribution of (X, Y, N), where N has a geometric distribution while X and Y are, respectively, the sum and the maximum of N i.i.d. exponential random variables. We present fundamental properties of this class of mixed trivariate distributions, and discuss their applications. Our results include explicit formulas for the marginal and conditional distributions, joint integral transforms, moments and related parameters, stability properties, and stochastic representations. We also derive maximum likelihood estimators for the parameters of this distribution, along with their asymptotic properties, and briefly discuss certain generalizations of this model. An example from finance, where N represents the duration of the growth period of the daily log-returns of currency exchange rates, illustrates the modeling potential of this model.     

On a class of distributions on the simplex

In the present paper we define and investigate a novel class of distributions on the simplex, termed normalized infinitely divisible distributions, which includes the Dirichlet distribution. Distributional properties and general moment formulae are derived. Particular attention is devoted to special cases of normalized infinitely divisible distributions which lead to explicit expressions. As a by-product new distributions over the unit interval and a generalization of the Bessel function distribution are obtained.     

p-Values,Bayes Factors,and Sufficiency

ABSTRACT

Various approaches can be used to construct a model from a null distribution and a test statistic. I prove that one such approach, originating with D. R. Cox, has the property that the p-value is never greater than the Generalized Likelihood Ratio (GLR). When combined with the general result that the GLR is never greater than any Bayes factor, we conclude that, under Cox’s model, the p-value is never greater than any Bayes factor. I also provide a generalization, illustrations for the canonical Normal model, and an alternative approach based on sufficiency. This result is relevant for the ongoing discussion about the evidential value of small p-values, and the movement among statisticians to “redefine statistical significance.”     

A novel regularization method for estimation and variable selection in multi-index models

Multi-index models have attracted much attention recently as an approach to circumvent the curse of dimensionality when modeling high-dimensional data. This paper proposes a novel regularization method, called MAVE-glasso, for simultaneous parameter estimation and variable selection in multi-index models. The advantages of the proposed method include transformation invariance, automatic variable selection, automatic removal of noninformative observations, and row-wise shrinkage. An efficient row-wise coordinate descent algorithm is proposed to calculate the estimates. Simulation and real examples are used to demonstrate the excellent performance of MAVE-glasso.     

Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory

The GPD is a central distribution in modelling heavy tails in many applications. Applying the GPD to actual datasets however is not trivial. In this paper we propose the Exponentiated GPD (exGPD), created via log-transform of the GPD variable, which has less sample variability. Various distributional quantities of the exGPD are derived analytically. As an application we also propose a new plot based on the exGPD as an alternative to the Hill plot to identify the tail index of heavy tailed datasets, and carry out simulation studies to compare the two.     

商品房限购政策的实体经济发展效应研究

本文依据2014年10月至2017年11月16个“非同期”限购城市和29个非限购城市的面板数据,利用广义合成控制法研究了房地产市场限购政策调控商品房价格、促进实体经济发展的政策效应。研究发现：限购政策能够有效地抑制商品房价格过快上涨,政策效应因商品房建筑面积和城市规模等特征存在差异性;同时发现了房地产限购政策与实体经济发展相关联的典型事实,并证实了限购政策对支持实体经济发展存在较显著的“回馈效应”,特别是由限购政策产生的转移投资对实体经济产出存在约10个月的滞后期。     

广义部分线性单指数模型的惩罚样条估计

 本文讨论了指数族广义部分线性单指数模型(Generalized Partially Linear Single Index Models, GPLSIM) 的惩罚样条迭代估计,提出了基于惩罚似然和一组预先取定的单指数参数向量 的初始估计的迭代估计算法。另外本文还通过一组模拟数据的分析对所提出的迭代算法进行了验证。     

三维平衡多水平轮换设计及其连续性估计方法研究

 针对现行多水平轮换调查存在的一系列问题,本文提出三维平衡多水平轮换模式设计及其广义组合估计方法。在这套设计方法中,将各种偏差和两类相关关系引入到广义组合估计量公式中,提高了估计量的准确性,同时在多目标调查下还应用最优化方法确定一套最优系数,使得连续性的多目标调查的整体估计精度达到最高,同时又兼顾各类目标量的精度要求。这套系统化、理论化的设计方法将为我国未来开展多水平轮换调查提供理论指导。     

The type I distribution of the ratio of independent “Weibullized” generalized beta-prime variables

In this paper we introduce the distribution of , with c >  0, where X _i , i =  1, 2, are independent generalized beta-prime-distributed random variables, and establish a closed form expression of its density. This distribution has as its limiting case the generalized beta type I distribution recently introduced by Nadarajah and Kotz (2004). Due to the presence of several parameters the density can take a wide variety of shapes.