Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Improved communality estimation in factor analysis

The symmetric treatment of an asymmetric approach to factor analysis is shown to provide accurate communality estimates. In comparison with existing estimates including upper and lower bounds, the present approach is shown to be superior. In one example with known communalities, the present approach perfectly captures those communalities. In two empirical examples, it is shown to produce better communality estimates than any existing method.     

Unconditional analogues of Cochran–Mantel–Haenszel tests

The Cochran–Mantel–Haenszel tests are a suite of tests that are usually defined as conditional tests, tests that assume all marginal totals are known before sighting the data. Here unconditional analogues of these tests are defined for the more usual situation when the marginal totals are not known before sighting the data.     

Stationary bootstrapping for realized covariations of high frequency financial data

This paper studies the stationary bootstrap applicability for realized covariations of high frequency asynchronous financial data. The stationary bootstrap method, which is characterized by a block-bootstrap with random block length, is applied to estimate the integrated covariations. The bootstrap realized covariance, bootstrap realized regression coefficient and bootstrap realized correlation coefficient are proposed, and the validity of the stationary bootstrapping for them is established both for large sample and for finite sample. Consistencies of bootstrap distributions are established, which provide us valid stationary bootstrap confidence intervals. The bootstrap confidence intervals do not require a consistent estimator of a nuisance parameter arising from nonsynchronous unequally spaced sampling while those based on a normal asymptotic theory require a consistent estimator. A Monte-Carlo comparison reveals that the proposed stationary bootstrap confidence intervals have better coverage probabilities than those based on normal approximation.     

Decomposition of Variance for Spatial Cox Processes

Abstract. Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.     

On prediction from the location-scale model with equi correlated response

The location-scale model with equi-correlated responses is discussed. The structure of the location-scale model is utilised to genera-te the prediction distribution of a future response and that of a set of future responses. The method avoids the integration procedures usually involved in derivation of prediction distributions and yields results same as those obtained by the Bayes method with the vague prior distribution* Finally the re-suits have been specialised to cover the case of the normal intra-class model.     

Independence test in high-dimension using distance correlation and power enhancement technique

Abstract

This paper is concerned with independence test in high-dimension. A new test statistic is proposed with two terms: one is based on the modified distance correlation statistic, the other is constructed to enhance the power under sparse alternatives. Asymptotic properties of the test statistic are discussed under some regular conditions. The finite-sample simulations exhibit its superiority over some existing procedures. Finally, a real data example illustrates the proposed test.     

我国粮食价格形成机制沿革的历史回顾与探讨

粮食价格形成机制的核心是政府及其市场主体在粮食价格水平决定中的作用机制.改革开放前,政府影响力在我国粮食价格形成中起着决定性作用.改革开放后,我国粮食价格形成机制的变迁以不断调适政府对粮食价格水平决定的影响力为主线,经历了从政府直接控制价格水平到间接影响价格水平形成的转折.总体而言,在粮食供求关系偏紧时,往往通过政府强势的价格影响力来实现粮食增产,而当粮食供求关系偏松时,政府强势影响价格的弊端就会累积性呈现.从改革开放以来粮食价格形成机制的变化及其调整来看,我国一直没有找到粮食供求关系偏松状况下有效的粮食价格形成机制.未来我国粮食价格形成机制改革的关键是政府在粮食价格水平决定中应发挥怎样的作用,而不是放任粮食市场自发形成价格,否则我国粮食市场过度波动难以避免,粮食生产长期稳定发展难免受到影响.     

FDI 对安徽省装备制造业发展的影响分析

外商直接投资（FDI）在拓宽国内资金需求渠道的同时,具有较强的技术溢出效应,迎合了装备制造业资本和技术密集的双重特征。当前,安徽省装备制造业处在资本密集型占主导的发展阶段,对 FDI的偏好性较强。研究表明,安徽省装备制造业与FDI间具有较强的相关性,FDI能够有效促进安徽省装备制造业发展,但安徽省装备制造业对FDI的吸引力较弱,且各具体行业对 FDI依赖程度存在较大差异。据此提出相应的建议,一方面,安徽省应注重装备制造业转型升级,提升行业整体竞争力;另一方面,要优化投资环境,增强安徽省装备制造业对FDI的引致能力。     

Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.