论文检索

统计逆问题的双重平滑 EM 算法AI 解读

Recent results on discretization effects in unfolding intensity function of an indirectly observed nonhomogeneous Poisson process show that a critical feasibility condition for the strong L2 consistency of the histogram sieve maximum likelihood estimates requires that the approximation error of the function of interest with step functions tend to 0 faster than the squared singular values of the discretized folding operator. This condition may not be fulfilled in some…

矩形阵列渐近框架下投影得分方法的效率AI 解读

Summary The paper considers a rectangular array asymptotic embedding for multistratum data sets, in which both the number of strata and the number of within-stratum replications increase, and at the same rate. It is shown that under this embedding the maximum likelihood estimator is consistent but not efficient owing to a non-zero mean in its asymptotic normal distribution. By using a projection operator on the score function, an adjusted maximum likelihood estimator can be…

协方差逆矩阵难以估计时的自适应估计方程AI 解读

Summary To construct an optimal estimating function by weighting a set of score functions, we must either know or estimate consistently the covariance matrix for the individual scores. In problems with high dimensional correlated data the estimated covariance matrix could be unreliable. The smallest eigenvalues of the covariance matrix will be the most important for weighting the estimating equations, but in high dimensions these will be poorly determined. Generalized…

大簇规模下广义估计方程的渐近理论AI 解读

Generalized estimating equations are used in regression analysis of longitudinal data, where observations on each subject are correlated. Statistical analysis using such methods is based on the asymptotic properties of regression parameter estimators. This paper presents asymptotic results when either the number of independent subjects or the cluster sizes (the number of observations on each subject) or both go to infinity. A set of (information matrix based) general…

通过混合表示进行凸约束模型的非参数估计AI 解读

We present a general approach to estimating probability measures constrained to lie in a convex set. We represent constrained measures as mixtures of simple, known extreme measures, and so the problem of estimating a constrained measure becomes one of estimating an unconstrained mixing measure. Convex constraints arise in many modeling situations, such as estimation of the mean and estimation under stochastic ordering constraints. We describe mixture representation…

03.1.1 利用完全数据似然推导有序 Probit 与 Logit 模型的观测信息矩阵AI 解读

Louis (1982) presents a method for computing the observed information matrix and standard errors of maximum likelihood estimates obtained via the EM algorithm based on the complete-data log likelihood function. The problem illustrates the well-known method of Louis (1982) for a widely used qualitative response model in econometrics. The observed-data log likelihood function for the following model can, of course, be easily differentiated to obtain the observed information…

重尾误差下 ARCH 与 GARCH 模型的推断AI 解读

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In…

高维因子模型的推断理论AI 解读

This paper develops an inferential theory for factor models of large dimensions. The principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator (if normality is assumed). We derive the rate of convergence and the limiting distributions of the estimated factors, factor loadings, and common components. The theory is developed within the framework of large cross sections ("N") and a large time…

协整误差修正模型的有效贝叶斯估计AI 解读

linear restrictions and the nonlinear method of Johansen's maximum likelihood procedure. That the linear method can produce invalid estimates while the Johansen approach always produces valid estimates has been recognized in several recent articles. Because all Bayesian studies to date have used linear restrictions, this article presents a Bayesian method for obtaining estimates of cointegrating vectors that will always be valid. In addition, it also presents an approach for…

基于 M 估计的经验最优预测统一刀切理论AI 解读

The paper presents a unified jackknife theory for a fairly general class of mixed models which includes some of the widely used mixed linear models and generalized linear mixed models as special cases. The paper develops jackknife theory for the important, but so far neglected, prediction problem for the general mixed model. For estimation of fixed parameters, a jackknife method is considered for a general class of M-estimators which includes the maximum likelihood, residual…

非平衡双向误差分量回归模型的替代估计量比较AI 解读

This paper considers the unbalanced two-way error component model studied by Wansbeek and Kapteyn (1989). Alternative analysis of variance (ANOVA), minimum norm quadratic unbiased and restricted maximum likelihood (REML) estimation procedures are proposed. The mean squared error performance of these estimators are compared using Monte Carlo experiments. Results show that for the estimates of the variance components, the computationally more demanding maximum likelihood (ML)…

允许序约束与边际建模的扩展 RC 关联模型AI 解读

In the context of two-way contingency tables, the RC model may be seen as a way of modeling the set of log-odds ratios for adjacent 2 × 2 subtables through row and column scores. This article presents an extended class of RC models that allows simultaneous modeling of the marginal distributions and the association between two categorical variables; these components are parameterized with logits and log-odds ratios that may be of different types (e.g., global or continuation)…

社会网络分析的潜在空间方法AI 解读

Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make…

均值含随机波动率模型：国际股票市场的经验证据AI 解读

Abstract In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is developed elsewhere for Autoregressive Conditional Heteroscedastic (ARCH) models, known as the ARCH in Mean (ARCH‐M) model. The estimation of ARCH models is relatively easy…

局部重组率估计的近似似然方法AI 解读

Summary There is currently great interest in understanding the way in which recombination rates vary, over short scales, across the human genome. Aside from inherent interest, an understanding of this local variation is essential for the sensible design and analysis of many studies aimed at elucidating the genetic basis of common diseases or of human population histories. Standard pedigree-based approaches do not have the fine scale resolution that is needed to address this…

半空间后验概率的小相对误差渐近正态近似AI 解读

Let $\Theta$ be a parameter space included in a finite-dimensional Euclidean space and let $A$ be a half-space. Suppose that the maximum likelihood estimate $\theta_n$ of $\theta$ is not in $A$ (otherwise, replace $A$ by its complement) and let $\Delta$ be the maximum log likelihood (at $\theta_n$) minus the maximum log likelihood over the boundary $\partial A$. It is shown that under some conditions, uniformly over all half-spaces $A$, either the posterior probability of $A$…

基于模拟的矩方法与效率AI 解读

The method of moments is based on a relation E[superscript theta[subscript 0]](h(X[subscript t, theta)) = 0, from which an estimator of theta is deduced. In many econometric models, the moment restrictions can not be evaluated numerically due to, for instance, the presence of a latent variable. Monte Carlo simulations method make possible the evaluation of the generalized method of moments (GMM) criterion. This is the basis for the simulated method of moments. Another…

广义矩估计与经验似然AI 解读

Generalized method of moments (GMM) estimation has become an important unifying framework for inference in econometrics in the last 20 years. It can be thought of as encompassing almost all of the common estimation methods, such as maximum likelihood, ordinary least squares, instrumental variables, and two-stage least squares, and nowadays is an important part of all advanced econometrics textbooks. The GMM approach links nicely to economic theory where orthogonality…

k 步统计量与极值统计量的高阶渐近效率等价性AI 解读

It is well known that a one-step scoring estimator that starts from any N 1/2 -consistent estimator has the same first-order asymptotic efficiency as the maximum likelihood estimator. This paper extends this result to k -step estimators and test statistics for k ≥ 1, higher order asymptotic efficiency, and general extremum estimators and test statistics. The paper shows that a k -step estimator has the same higher order asymptotic efficiency, to any given order, as the…

不变性、可识别性与形态测量AI 解读

The form of an object is that characteristic that remains invariant under a group of transformations comprising translation, rotation, and possibly reflection. Group invariance thus naturally plays an important role in the statistical analysis of forms. We examine the existing methods for the statistical analysis of form from the invariance perspective. We begin with a review of the important basic ideas behind invariance and derive a maximal invariant under the group of…

马尔可夫链边际自助法AI 解读

Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent…

平滑器与 Cp、广义最大似然及扩展指数准则AI 解读

Nonparametric regression, often called smoothing, is a widely used data analysis method. The use of a smoother requires the choice of a smoothing parameter that by balancing fidelity and roughness controls how much smoothing is done. Two popular selection criteria for choosing the smoothing parameter are Cp and generalized maximum likelihood (GML). Each of these has its own problems. ForCp, the problem is its high variability, whereas for GML, the problem is its potentially…

We propose a novel statistic for conducting joint tests on all the structural parameters in instrumental variables regression. The statistic is straightforward to compute and equals a quadratic form of the score of the concentrated log–likelihood. It therefore attains its minimal value equal to zero at the maximum likelihood estimator. The statistic has a χ2 limiting distribution with a degrees of freedom parameter equal to the number of structural parameters. The limiting…

STAR 与 STAR-GARCH 模型的极大似然估计：理论与蒙特卡洛证据AI 解读

Abstract Theoretical and practical interest in non‐linear time series models, particularly regime switching models, have increased substantially in recent years. Given the abundant research activity in analysing time‐varying volatility through Generalized Autoregressive Conditional Heteroscedasticity (GARCH) processes (see Engle, 1982 ; Bollerslev, 1986 ), it is important to analyse regime switching models with GARCH errors. A popular specification in this class is the…