针对面板数据的半参数变系数可加模型的估计和推断

本文研究针对面板数据的半参数变系数可加模型的估计和推断问题,该模型将因变量与自变量之间的关系建模成未知函数的形式,并且假设它们之间的关系是随时间变化的.本文基于B样条方法估计未知的参数和函数.本文在允许(N,T)→∞的情况下建立各个估计量的渐近性质.通过大量的模拟评估所提出的估计方法的表现.最后,本文将所推荐的模型用于调查Fama-French三因子的时变行为.     

乘积λ─可加Fuzzy测度和Fuzzy核

本文首先给出乘积λ─可加Ｆｕｚｚｙ测度的两种等价定义，在此基础上建立了乘积空间上的Ｆｕｂｉｎｉ定理，其次提出了Ｆｕｚｚｙ核的概念，并由Ｆｕｚｚｙ核引出乘积空间上另一类λ─可加Ｆｕｚｚｙ测度．     

Fuzzy值Fuzzy测度的和与乘积的零可加与自连续性

本文讨论了Ｆｕｚｚｙδ－代数上的Ｆｕｚｚｙ值Ｆｕｚｚｙ测度的和与乘积的零可加、自连续性。     

一种新的空间计量模型——部分线性可加自回归模型及其应用

本文提出一种新的空间计量模型,部分线性可加空间自回归模型。首先,引入样条逼近可加函数分量,然后利用截面拟似然的思想得到模型参数的估计;所提出的估计方法简单易行,且对误差分布的要求低。通过模拟研究,得到所提出估计方法的有限样本性质,模拟结果显示了所提出估计的有效性;最后,将估计方法应用到波士顿房屋数据进行统计分析,得到了较他人估计更好的结果。     

二阶可加混料模型稳健D-最优设计

本文研究了在给定两个随机模型先验测度r下的q分量二阶可加混料模型稳健D-最优设计。依据Kiefer次序下完备集的结果且结合稳健D-最优准则,给出了二阶可加模型稳健D-最优的相关理论,并得到了四分量可加模型稳健D-最优ξα_r~*=α_r~*ξ_1~*+(1-α_r~*)ξ_2~*,且利用等价性定理证明了ξα_r~*为稳健D-最优设计。同时基于α_r~*与先验测度r的关系,介绍了先验测度r选择的效率最大最小原则,得到了四分量二阶可加模型的最优先验测度r~*,且比较了四分量二阶可加混料模型稳健D-最优设计与D-最优设计的效率。     

函数型部分线性乘积模型的B-样条估计

本文研究了函数型部分线性乘积模型,该模型可用于响应变量为正数的函数型数据的统计建模问题,经过对数变换后模型转化为函数型部分线性模型.基于B-样条,通过极小化最小一乘相对误差(LARE)和最小乘积相对误差(LPRE),分别给出模型的LARE估计和LPRE估计,其中B-样条基的维数利用Schwarz信息准则选取.对两种估计方法分别给出斜率函数估计的相合性和参数部分估计的渐近正态性,并且证明了斜率函数的收敛率达到了非参数函数估计的最优速率.蒙特卡洛模拟用来比较所提出的方法与最小一乘(LAD)估计和最小二乘(LS)估计在不同误差分布下的有限样本性质,模拟结果表明所提方法是有效和实用的.最后通过一个实际数据分析的例子来说明模型的应用.     

具有周期特征的离散时间序列的非参数可加模型

周期性是在时间序列分析中经常出现的影响因素之一.在离散值响应变量时间序列中,我们利用带惩罚的极大似然估计建立了未知周期的一致估计.基于周期的估计,我们利用B-样条逼近趋势项和可加函数,同时得到了周期项的√n相合估计以及趋势项和可加函数的初始估计.然后基于后移的思想,推导了趋势项和可加函数的改进估计,并证明了估计量的渐近...     

半参数可加模型的广义Liu估计

作为部分线性模型和可加模型的推广,半参数可加模型在统计建模中应用广泛.考虑这类半参数模型在线性部分自变量存在共线性时的估计问题.基于Profile最小二乘方法,提出了参数分量的广义Profile-Liu估计,并给出了该估计量的偏和方差以及均方误差.最后利用数值模拟验证了所提方法的有效性.     

二阶可加混料模型参数估计的Iλ—最优设计

二阶可加混料模型参数估计的Ｉ_λ──最优设计关颖男，张崇岐（东北大学数学系，沈阳１１０００６）Ｉ_λ－ＯＰＴＩＭＡＬＤＥＳＩＧＮＳＯＦＰＡＲＡＭＥＴＥＲＥＳＴＩＭＡＴＩＯＮＦＯＲＴＨＥＡＤＤＩＴＩＶＥＭＩＸＴＵＲＥＭＯＤＥＬＯＦ２－ＤＥＧＲＥＥ￥ＧＵＡ...     

半相依部分线性可加回归模型的统计推断

受实际问题研究的启发, 为减少模型偏差, 提出了一类半相依部分线性可加的半参数回归模型. 这类半相依模型中, 响应变量与 一部分解释变量之间的关系是线性的, 与另一部分解释变量之间的关系未知但具有可加结构, 各方程的误差之间是相关的. 将级 数逼近法、最小二乘法和同期相关的估计结合起来, 提出了用于估计模型参数分量的加权半参数最小二乘估计量(WSLSEs), 和用于估 计模型非参数分量的加权级数逼近估计量(WSEs). 证明了这些加权的估计量比相应的不加权的估计量渐近有效, 并导出了相应的渐近正态性. 另外, 还讨论了利用这些估计量的渐近性质来对模型的参数及非参数分量作统计推断. 用大量的模拟实验考察 了所提出的方法在有限样本情况下的表现, 并对美国的一个关于妇女工资问题的全国纵向调查(NLS)数据集进行了统计分析.     

三阶乘积幻方的构造

通过构造幻方积最小的三阶乘积幻方给出了三阶乘积幻方可构造的一个充分必要条件,并完全确定了所有元素为不同正整数的三阶乘积幻方的结构.     

序列空间上的可乘等距与渐进可乘等距

本文证明了（l^βn）的单位球面间的非满等距在一定条件下可被线性等距延拓至全空间。并刻画了l^p（P〉0）上的非满渐进可乘等距算子．     

Multiplicative B-Product and Its Properties

The author considers a special kind of B-product which is called the multiplicative B-product. The most important property of multiplicative B-product is that it preserves multiplicativity of functions.     

Generalized Smooth Monotonic Regression in Additive Modeling

Common approaches to monotonic regression focus on the case of a unidimensional covariate and continuous response variable. Here a general approach is proposed that allows for additive structures where one or more variables have monotone influence on the response variable. In addition the approach allows for response variables from an exponential family, including binary and Poisson distributed response variables. Flexibility of the smooth estimate is gained by expanding the unknown function in monotonic basis functions. For the estimation of coefficients and the selection of basis functions a likelihood-based boosting algorithm is proposed which is simple to implement. Stopping criteria and inference are based on AIC-type measures. The method is applied to several datasets.     

含有治愈部分的可加危险模型的估计

最近可加危险(AH)模型被广泛地应用于生存分析数据,模型的协变量可以假设为时间独立或时间相关的.基于混合治愈模型,有界累计危险治愈模型和"不正确"的比例危险模型.本文将上述的可乘危险模型延伸到可加的危险模型,这里的模型可以允许含治愈部分的生存数据的存在."不正确"的AH模型的识别和参数估计也将在本文给出讨论.     

Jordan代数上的可乘Jordan导子

设A是Jordan代数,如果映射d:A→A满足任给a,b∈A,都有d(aob)=d(a)o b+aod(b),则称d为可乘Jordan导子.如果A含有一个非平凡幂等p,且A对于p的Peirce分解A=A_1⊕A_(1/2)⊕A_0满足:(1)设ai∈Ai(i=1,0),如果任给t_(1/2)∈A_(1/2),都有a_i○t_(1/2)=0,则a_i=0,则A上的可乘Jordan导子d.如果满足d(p)=0,则d是可加的.由此得到结合代数和三角代数满足一定条件时,其上的任意可乘Jordan导子是可加的.     

Functional Additive Mixed Models

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, for example, spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well, and also scales to larger datasets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.     

Multiplicative maps on ℬ(X)

A structural feature of multiplicative maps on ℬ(X) which sends some rank-1 operator to an operator of rank not greater than 1 is given, and based on it, some characterizations of automorphism of ℬ(X) are obtained and some multiplicative preserver problems are answered. Project supported by the National Natural Science Foundation of China (Grant No.19671055) and Provincial Natural Science Foundation of Shanxi.     

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. However, the choice of which features to treat as linear or nonlinear is typically assumed known. Thus, to make a GPLAM a viable approach in situations in which little is known a priori about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly. We introduce the sparse partially linear additive model (SPLAM), which combines model fitting and both of these model selection challenges into a single convex optimization problem. SPLAM provides a bridge between the lasso and sparse additive models. Through a statistical oracle inequality and thorough simulation, we demonstrate that SPLAM can outperform other methods across a broad spectrum of statistical regimes, including the high-dimensional (p ? N) setting. We develop efficient algorithms that are applied to real datasets with half a million samples and over 45,000 features with excellent predictive performance. Supplementary materials for this article are available online.