Testing Monotonicity of Regression Functions – An Empirical Process Approach

We propose several new tests for monotonicity of regression functions based on different empirical processes of residuals and pseudo‐residuals. The residuals are obtained from an unconstrained kernel regression estimator whereas the pseudo‐residuals are obtained from an increasing regression estimator. Here, in particular, we consider a recently developed simple kernel‐based estimator for increasing regression functions based on increasing rearrangements of unconstrained non‐parametric estimators. The test statistics are estimated distance measures between the regression function and its increasing rearrangement. We discuss the asymptotic distributions, consistency and small sample performances of the tests.     

A Mechanical Interpretation of the Coefficient of Rank Correlation and other Analogies

Periodically, the pyramid or “chain letter” scheme is offered to Americans under the guise of a business dealership. Recently, the FTC ordered Glen Turner's “Dare to be Great” firm to repay 44 million dollars to participants. In order to demonstrate that the potential gains are misrepresented by promoters, a probability model of the pyramid scheme is developed. The major implications are that the vast majority of participants have less than a ten percent chance of recouping their initial investment when a small profit is achieved as soon as they recruit three people and that, on the average, half of the participants will recruit no one else and lose all their money.     

Non-Normal Bivariate Distributions with Normal Marginals

The purpose of this note is to indicate that Fieller's Theorem can be expressed in the matrix formulation of the general linear model. The practical consequence is that one general computer program which can estimate the parameters and test the validity of a pertinent model, can also compute confidence limits for the ratios of any linear combinations of the parameters.     

基于核和灰度的双重异构数据序列预测建模方法研究

通过建立灰色异构数据"核"序列的DGM(1,1)模型,实现双重异构数据"核"的预测;以"核"为基础、以双重异构数据序列中较大的区间灰数信息域作为预测结果的信息域,构建基于区间灰数与实数的双重异构数据序列灰色预测模型,有效地将灰色预测模型建模对象从"同质数据"拓展至"双重异构数据"。研究成果对丰富灰色预测模型理论体系具有积极意义。     

Asymptotic normality of Huber–Dutter estimators in a linear model with AR(1) processes

The paper studies a linear regression model with first order autoregressive (AR(1)) processes. The Huber–Dutter (HD) estimators of unknown parameters are given, and the asymptotic normality of the HD estimators is investigated. An example is presented to illustrate the proposed method.     

中国通货膨胀率的非线性动态行为研究——来自MS—ARFIMA模型的新证据

使用允许长记忆参数d服从区制转换的MS—ARFIMA模型对中国月度通货膨胀路径的动态行为进行新的实证研究,结果显示：中国通货膨胀不仅均值水平和不确定性存在着“低通胀”区制和“高通胀”区制,而且更为重要的是,通货膨胀序列的平稳性也表现出显著的区制转换动态。“低通胀”区制下,长记忆参数d1=0．361,说明通货膨胀是协方差平稳序列,“高通胀”区制下,长记忆参数d2=1．145,说明通货膨胀是非平稳序列。这一新的研究结论意味着中国通货膨胀冲击的持久性效应也存在相应的区制转移变化。这要求央行在管控通货膨胀过程中,既要考虑均值和不确定性的区制变化,又要兼顾平稳性和持久性的区制变化。     

基于改进D—S证据理论的TOPSIS模型在科技奖励评价中的应用

针对科技奖励评价的特点,根据改进D—S证据合成规则,将专家的评价指标值转化为指标的综合得分并形成决策矩阵;结合TOPSIS模型求解理想解和负理想解,计算距离和贴近度,对各评价项目进行综合排名。实证结果表明：该模型能够很好地解决科技奖励评价过程中的不确定性问题,为科技奖励综合评价提供了一种新的思路。     

中国粮食价格波动特征研究——基于X-12-ARIMA模型和ARCH类模型

以小麦和大豆为例,研究2002年1月至2012年6月中国粮食价格波动特征。首先利用X-12-ARIMA模型对价格序列进行季节调整,然后运用ARCH类模型对剥离季节因素的价格序列进行波动分析。结果发现:中国粮食价格季节性波动逐年减弱;粮食价格具有明显的波动集簇性,前期价格波动和外部冲击对后期价格的影响具有持续性;粮食市场不存在"高风险、高回报"特征;小麦价格波动的非对称性不显著,而大豆价格波动则呈现明显的非对称特征,且上期价格上涨信息引发的波动要大于下跌信息。     

Pmc-optimal nonparametric quantile estimator

According to Pitman's Measure of Closeness, if T₁and T₂are two estimators of a real parameter $[d], then T₁is better than T₂if Po[d]{T₁-o[d] < T₂-0[d]} > 1/2 for all 0[d]. It may however happen that while T₁is better than T₂and T₂is better than T₃, T₃is better than T₁. Given q ? (0,1) and a sample X₁, X₂, ..., X_nfrom an unknown F ? F, an estimator T* = T*(X₁,X₂...X_n)of the q-th quantile of the distribution F is constructed such that P_F{F(T*)-q <[d] F(T)-q} >[d] 1/2 for all F?F and for all T€T, where F is a nonparametric family of distributions and T is a class of estimators. It is shown that T* =X_j:n'for a suitably chosen jth order statistic.     

A class of weighted normal distributions and its variants useful for inequality constrained analysis

This article develops a class of the weighted normal distributions for which the probability density function has the form of a product of a normal density and a weight function. The class constitutes marginal distributions obtained from various kinds of doubly truncated bivariate normal distributions. This class of distributions strictly includes the normal, skew–normal and two-piece skew–normal and is useful for selection modelling and inequality constrained normal mean analysis. Some distributional properties and Bayesian perspectives of the class are given. Probabilistic representation of the distributions is also given. The representation is shown to be straightforward to specify distribution and to implement computation, with output readily adapted for required analysis. Necessary theories and illustrative examples are provided.