Asymptotic properties of a conditional maximum-likelihood estimator

Inference for a scalar interest parameter in the presence of nuisance parameters is considered in terms of the conditional maximum-likelihood estimator developed by Cox and Reid (1987). Parameter orthogonality is assumed throughout. The estimator is analyzed by means of stochastic asymptotic expansions in three cases: a scalar nuisance parameter, m nuisance parameters from m independent samples, and a vector nuisance parameter. In each case, the expansion for the conditional maximum-likelihood estimator is compared with that for the usual maximum-likelihood estimator. The means and variances are also compared. In each of the cases, the bias of the conditional maximum-likelihood estimator is unaffected by the nuisance parameter to first order. This is not so for the maximum-likelihood estimator. The assumption of parameter orthogonality is crucial in attaining this result. Regardless of parametrization, the difference in the two estimators is first-order and is deterministic to this order.     

Modeling Two-state Disease Processes with Random Effects

Many chronic medical conditions are manifested by alternating sojourns in symptom-free and symptomatic states. In many cases, in addition to their relapsing and remitting nature, these conditions lead to worsening disease patterns over time and may exhibit seasonal trends. We develop a mixed-effect two-state model for such disease processes in which covariate effects are modeled multiplicatively on transition intensities. The transition intensities, in turn, are functions of three time scales: the semi-Markov scale involving the backward recurrence time for the cyclical component, the Markov scale for the time trend component, and a seasonal time scale. Multiplicative bivariate log-normal random effects are introduced to accommodate heterogeneity in disease activity between subjects and to admit a possible negative correlation between the transition intensities. Maximum likelihood estimation is carried out using Gauss-Hermite integration and a standard Newton-Raphson procedure. Tests of homogeneity are presented based on score statistics. An application of the methodology to data from a multi-center clinical trial of chronic bronchitis is provided for illustrative purposes.     

最佳线性无偏估计中的一个影响度量

本文讨论数据的剔除对最佳线性无偏估计的影响大小。用方差比做为判定影响大小的度量,并找到了它与相关系数之间的精确关系。     

ASYMPTOTIC PROPERTIES OF RESTRICTED MAXIMUM LIKELIHOOD (REML) ESTIMATES FOR HIERARCHICAL MIXED LINEAR MODELS

This paper explores the asymptotic distribution of the restricted maximum likelihood estimator of the variance components in a general mixed model. Restricting attention to hierarchical models, central limit theorems are obtained using elementary arguments with only mild conditions on the covariates in the fixed part of the model and without having to assume that the data are either normally or spherically symmetrically distributed. Further, the REML and maximum likelihood estimators are shown to be asymptotically equivalent in this general framework, and the asymptotic distribution of the weighted least squares estimator (based on the REML estimator) of the fixed effect parameters is derived.     

粮食规模经营支持政策资金的分配机制——基于社会资本与土地产出率的视角

推进农业规模经营是当前我国农业政策的重要支持方向之一。评估粮食规模经营支持政策资金的分配机制与作用效果对优化政策有重要意义。从社会资本与土地产出率角度审视了支持政策资金的分配与机制。结果显示,社会资本对支持政策资金的分配有重要影响,特别是政治性社会资本对规模经营农户获取政策资金的概率和数量都有显著的正向影响,而商业性社会资本则影响不显著;土地产出率对规模经营农户获得政策资金没有显著影响,表明政策资金分配并不是以提高土地产出率为目的,相反,经营耕地规模则是规模经营农户获得政策资金的重要影响因素。我国当前粮食规模经营支持政策资金的分配方式需要优化和调整,建议采取普惠式的支持方式或者放弃项目支持方式,而将资金集中用于农业公共基础设施和规模经营农户风险防范机制构建上。     

Computation of determinantal subset influence in regression

One of the important goals of regression diagnostics is the detection of cases or groups of cases which have an inordinate impact on the regression results. Such observations are generally described as influential. A number of influence measures have been proposed, each focusing on a different aspect of the regression. For single cases, these measures are relatively simple and inexpensive to calculate. However, the detection of multiple-case or joint influence is more difficult on two counts. First, calculation of influence for a single subset is more involved than for an individual case, and second, the sheer number of subsets of cases makes the computation overwhelming for all but the smallest data sets.Barrett and Gray (1992) described methods for efficiently examining subset influence for those measures that can be expressed as the trace of a product of positive semidefinite (psd) matrices. There are, however, other popular measures that do not take this form, but rather are expressible as the ratio of determinants of psd matrices. This article focuses on reducing the computation for the determinantal ratio measures by making use of upper and lower bounds on the influence to limit the number of subsets for which the actual influence must be explicitly determined.     

我国生猪市场价格调控中的"猪粮比价":理论内涵与实践反思

无论从稳定生猪生产和农民增收,还是从保障居民的猪肉消费,生猪市场的重要性都是不言而喻的.2007年以来,我国政府逐步形成了一整套依据"猪粮比价"高低进行价格干预的制度安排.然而,学术界却并没有深究"猪粮比价"这一指标背后的经济逻辑.文章尝试推导出了"猪粮比价"的理论公式,发现"猪粮比价"等于生猪养殖中技术口径的投入产出效率与经济口径的投入产出效率之比值.与此同时,还发现在会计核算和经济核算两种口径下有着不一样的计算结果.通过简要回顾我国政府根据"猪粮比价"对生猪市场进行价格调控的历程,发现价格干预的效果并不尽如人意.对比理论公式和价格干预实践,文章找到了导致以上结果的原因.事实上,美国农业部虽然也公布"猪粮比价",但却并没有将之用于指导猪肉市场的价格调控.     

旋流式气化炉冷态流场数值模拟研究

为探究一种新型旋流式气化炉冷态流场分布特性及内部结构对其流场的影响,应用计算流体力学( CFD)软件 FLUENT对该气化炉进行三维数值模拟分析。采用RNG K-8湍流模型,通过多点测压实验,对模拟结果进行验证,并且 采用不同的开孔率及旋流片角度组合分析气化炉内部结构对其流场的影响。结果表明：模拟和测压实验结果较为一致; 旋流式气化炉内气流主要做螺旋上升运动;栅板开孔率大小主要影响气化炉内压降、轴向速度以及湍流强度;而旋流片 角度则主要影响切向速度及湍流强度分布。新型旋流式气化炉内部栅板旋流片结构可增加气化反应速率及反应停留 时间,有助于气化反应的进行。     

On a class of bivariate mixed Sarmanov distributions

Multivariate distributions are more and more used to model the dependence encountered in many fields. However, classical multivariate distributions can be restrictive by their nature, while Sarmanov's multivariate distribution, by joining different marginals in a flexible and tractable dependence structure, often provides a valuable alternative. In this paper, we introduce some bivariate mixed Sarmanov distributions with the purpose to extend the class of bivariate Sarmanov distributions and to obtain new dependency structures. Special attention is paid to the bivariate mixed Sarmanov distribution with Poisson marginals and, in particular, to the resulting bivariate Sarmanov distributions with negative binomial and with Poisson‐inverse Gaussian marginals; these particular types of mixed distributions have possible applications in, for example modelling bivariate count data. The extension to higher dimensions is also discussed. Moreover, concerning the dependency structure, we also present some correlation formulas.     

Outlier detection in contingency tables using decomposable graphical models

For high-dimensional data, it is a tedious task to determine anomalies such as outliers. We present a novel outlier detection method for high-dimensional contingency tables. We use the class of decomposable graphical models to model the relationship among the variables of interest, which can be depicted by an undirected graph called the interaction graph. Given an interaction graph, we derive a closed-form expression of the likelihood ratio test (LRT) statistic and an exact distribution for efficient simulation of the test statistic. An observation is declared an outlier if it deviates significantly from the approximated distribution of the test statistic under the null hypothesis. We demonstrate the use of the LRT outlier detection framework on genetic data modeled by Chow–Liu trees.