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分数整合广义自回归条件异方差模型AI 解读

The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations. Unlike I(d) processes for the mean, Maximum Likelihood Estimates (MLE) of the FIGARCH parameters are argued to be T^(1/2)-consistent. The small-sample behavior of an approximate MLE procedure is assessed through a…

双侧删失数据的自洽估计与极大似然估计算法AI 解读

The paper investigates the structure of the self-consistent estimators (SCE) and the nonparametric maximum likelihood estimator (NPMLE) for doubly censored data. An explicit sufficient and necessary condition for an SCE to be the NPMLE is given. Based on this, algorithms for computing the SCE and the NPMLE are provided. The relation between our algorithms and the EM algorithm is studied.

序约束下生存曲线非参数估计的渐近理论AI 解读

We consider two problems in nonparametric survival analysis under the restriction of stochastic ordering. The first problem is that of estimating a survival function $\overline{F}(t)$ under the restriction $\overline{F}(t) \geq $\overline{F}_0 (t)$, all t, where $\overline{F}_0 (t)$ is known. The second problem consists of estimating two unknown survival functions $\overline{F}^{(1)}(t)$ and $\overline{F}^{(2)}(t)$ when it is known that $\overline{F}^{(1)}(t) \geq…

利用非参数回归估计变点AI 解读

We consider a regression model in which the mean function may have a discontinuity at an unknown point. We propose an estimate of the location of the discontinuity based on one-side nonparametric regression estimates of the mean function. The change point estimate is shown to converge in probability at rate $O(n^{-1})$ and to have the same asymptotic distribution as maximum likelihood estimates considered by other authors under parametric regression models. Confidence…

零初值或平稳初值下自回归与随机游走模型的精确矩AI 解读

For a first-order autoregressive AR(1) model with zero initial value, x i = ax i−1 ,_, + e i , we provide the bias, mean squared error, skewness, and kurtosis of the maximum likelihood estimator â. Brownian motion approximations by Phillips (1977, Econometrica 45, 463–485; 1978, Biometrika 65, 91–98; 1987, Econometrica 55, 277–301), Phillips and Perron (1988, Biometrika 75, 335–346), and Perron (1991, Econometric Theory 7, 236–252; 1991, Econometrica 59, 211–236), among…

用鞍点积分计算序列相关估计量的分布AI 解读

The efficient method of numerical saddlepoint integration is described and applied to calculating the probability distribution of the maximum likelihood and Yule-Walker estimators of the correlation coefficient a of a first-order autoregressive normal time series with initial value either zero or nonzero when a finite number n of data are at hand. Stationary time series of the same type are also treated. Significance points are computed in a number of examples to show how,…

拟似然与 EM 算法的推广AI 解读

SUMMARY This paper is concerned with situations in which there are missing or otherwise incomplete data and the full likelihood may not be available. Extensions of the EM algorithm are developed to deal with estimation via general estimating functions and in particular the quasi-score. The E-step is replaced by projecting the quasi-score and the M-step requires the solution of an estimating equation. The standard EM algorithm can be obtained as a particular case if the…

股票市场波动率长记忆的建模与定价AI 解读

A new class of fractionally integrated GARCH and EGARCH models for characterizing financial market volatility is discussed. Monte Carlo simulations illustrate the reliability of quasi maximum likelihood estimation methods, standard model selection criteria, and residual-based portmanteau diagnostic tests in this context. New empirical evidence suggests that the apparent long-run dependence in U.S. stock market volatility is best described by a mean-reverting fractionally…

摘要暂缺

解析导数与 GARCH 估计的计算AI 解读

In the context of univariate GARCH models we show how analytic first and second derivatives of the log-likelihood can be successfully employed for estimation purposes. Maximum likelihood GARCH estimation usually relies on the numerical approximation to the log-likelihood derivatives, on the grounds that an exact analytic differentiation is much too burdensome. We argue that this is not the case and that the computational benefit of using the analytic derivatives (first and…

区间删失数据分布函数非参数极大似然估计札记AI 解读

Gentleman & Geyer (1994) discuss the analysis of interval censored data and present results based on standard convex optimisation theory. Here, this problem is viewed from the perspective of a mixing problem of indicator functions. Using an analogy with the problem of mixture distributions a variety of results are easily derived, including a characterisation theorem for the maximum likelihood estimator and various reliably convergent algorithms. Software for the analysis of…

非线性混合效应模型中使用Laplace近似的一则说明AI 解读

The asymptotic properties of estimates obtained using Laplace's approximation for nonlinear mixed-effects models are investigated. Unlike the restricted maximum likelihood approach, e. g. Wolfinger (1993), here the Laplace approximation is appiled only to the random effects of the integrated likelihood. This results in approximate maximum likelihood estimation. The resulting estimates are shown to be consistent with the rate of convergence depending on both the number of…

模型误设下连续再评估方法的一致性AI 解读

This paper studies large sample behaviour of the continual reassessment method used to determine the target dose levels in phase I clinical trials. A sequential maximum likelihood procedure is investigated for local estimation of a given targeted toxicity level. Consistency and asymptotic normality for the maximum likelihood estimates are established under model misspecification and it is shown that, in general, the recommended dose level will converge to the target level.…

系统发育树的自助法：大偏差与离散程度效应AI 解读

A large deviation result is established for the bootstrap empirical distribution in a finite sample space, thereby validating both nonparametric and parametric bootstrapping in certain phylogenetic inference problems. The bias previously observed in the bootstrap distribution of the estimated tree topology is shown to stem from dispersion effects in the joint distribution of sample and bootstrap empirical distributions. Both results are examined for maximum likelihood…

长度偏倚数据的风险率回归分析AI 解读

Length-biased sampling is frequently a convenient technique for the collection of positive-valued or lifetime data. Estimation porocedures based on pseudo-likelihood are developed under the proportional hazards model for length-biased data. Bias-adjusted risk set sampling is used for the construction of the pseudo-likelihood and the risk set sampling is rephcated to improve estimation performance. The average of the resulting likelihood estimators is taken as the estimator…

正常被积函数序列的上图收敛与最大似然估计的强一致性AI 解读

Using the variational properties of epi-convergence together with suitable results on the measurability of multifunctions and integrands, we prove a strong law of large numbers for sequences of integrands from which we deduce a general theorem of almost sure convergence (strong consistency) for the maximum likelihood estimator.

估计方程一致性与效率的极小极大方法AI 解读

The consistency of estimating equations has been studied, in the main, along the lines of Cramér's classical argument, which only asserts the existence of consistent solutions. The statement similar to that of Doob and Wald, which identifies the consistent solutions, has not yet been established. The obstacle is that the solutions of estimating equations cannot in general be defined as the maximum of likelihood functions. In this paper we demonstrate that the consistent…

误设时间序列模型中的渐近最优估计AI 解读

A concept of asymptotically efficient estimation is presented when a misspecified parametric time series model is fitted to a stationary process. Efficiency of several minimum distance estimates is proved and the behavior of the Gaussian maximum likelihood estimate is studied. Furthermore, the behavior of estimates that minimize the h-step prediction error is discussed briefly. The paper answers to some extent the question what happens when a misspecified model is fitted to…

通过序贯插补实现非参数分层贝叶斯估计AI 解读

We consider the empirical Bayes estimation of a distribution using binary data via the Dirichlet process. Let $\mathscr{D}(\alpha)$ denote a Dirichlet process with $\alpha$ being a finite measure on Instead of having direct samples from an unknown random distribution F from $\mathscr{D}(\alpha)$, we assume that only indirect binomial data are observable. This paper presents a new interpretation of Lo's formula, and thereby relates the predictive density of the observations…

从多项响应数据恢复信息的最大熵方法AI 解读

The classical maximum entropy (ME) approach to estimating the unknown parameters of a multinomial discrete choice problem, which is equivalent to the maximum likelihood multinomial logit (ML) estimator, is generalized. The generalized maximum entropy (GME) model includes noise terms in the multinomial information constraints. Each noise term is modeled as the mean of a finite set of a priori known points in the interval [−1,1] with unknown probabilities where no parametric…

连续时间中的纵向有序响应数据分析AI 解读

A simple Markov model is developed for assessing the predictive effect of time-dependent covariates on an intermittently observed ordinal response in continuous time. This is accomplished by reparameterizing an ergodic intensity matrix in terms of its equilibrium distribution and a parametrically independent component that assesses the rate of movement between ordinal categories. The effect of covariates on the equilibrium distribution can then be modeled using any link…

现时状态数据分析的半参数比例优势回归模型AI 解读

An approach to covariate analysis of current status data is described. The method is applicable when the logit of the conditional probability of survival given the covariates is some increasing function of time plus a linear combination of the covariates. The approach is based on approximating the infinite-dimensional nuisance parameter, the baseline log-odds of failure, with a step function, and carrying out a maximum likelihood procedure. The resulting finite dimensional…

一般线性模型中响应变量的测量误差AI 解读

Problems where there is measurement error in the response variable in a general linear model are considered. With Y denoting the true value and U the observed/surrogate value, a rich class of measurement error models is considered in which (a) the mean of U given Y = y can be either linear or nonlinear in y, (b) the variance of U given Y = y can depend on y, and (c) the parameters in the model for U given Y can change across observations in the study. This could occur, for…

连续时间纵向有序响应数据分析AI 解读

A simple Markov model is developed for assessing the predictive effect of time-dependent covariates on an intermittently observed ordinal response in continuous time. This is accomplished by reparameterizing an ergodic intensity matrix in terms of its equilibrium distribution and a parametrically independent component that assesses the rate of movement between ordinal categories. The effect of covariates on the equilibrium distribution can then be modeled using any link…