On implementation of a test for Kronecker product covariance structure for multivariate repeated measures data

Under the assumption of multivariate normality the likelihood ratio test is derived to test a hypothesis for Kronecker product structure on a covariance matrix in the context of multivariate repeated measures data. Although the proposed hypothesis testing can be computationally performed by indirect use of Proc Mixed of SAS, the Proc Mixed algorithm often fails to converge. We provide an alternative algorithm. The algorithm is illustrated with two real data sets. A simulation study is also conducted for the purpose of sample size consideration.     

Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models

Estimating parameters in a stochastic volatility (SV) model is a challenging task. Among other estimation methods and approaches, efficient simulation methods based on importance sampling have been developed for the Monte Carlo maximum likelihood estimation of univariate SV models. This paper shows that importance sampling methods can be used in a general multivariate SV setting. The sampling methods are computationally efficient. To illustrate the versatility of this approach, three different multivariate stochastic volatility models are estimated for a standard data set. The empirical results are compared to those from earlier studies in the literature. Monte Carlo simulation experiments, based on parameter estimates from the standard data set, are used to show the effectiveness of the importance sampling methods.     

具有乘性噪声的线性振荡器的随机共振

研究了具有乘性色噪声的过阻尼线性振荡器的随机共振现象。基于线性系统理论,得到了系统输出幅度增益的精确表达式。讨论了乘性噪声的强度、自相关速率以及振荡器参数对输出幅度增益的影响。研究表明,输出幅度的增益是色噪声的强度和相关速率以及激励信号频率的非单调函数。而且,适当的噪声参数和振荡器参数可以使噪声情况下的输出幅度增益大于无噪声时的输出幅度增益。     

Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK

Summary.  The paper is motivated by a problem in veterinary epidemiology, in which spatially referenced breakdowns of bovine tuberculosis are classified according to their genotype and year of occurrence. We develop a nonparametric method for addressing spatial segregation in the resulting multivariate spatial point process, with associated Monte Carlo tests for the null hypothesis that different genotypes are randomly intermingled and no temporal changes in spatial segregation. Our spatial segregation estimates use a kernel regression method with bandwidth selected by a multivariate cross-validated likelihood criterion.     

Optimal stopping for Brownian motion with applications to sequential analysis and option pricing

Herman Chernoff made fundamental contributions to analytical and computational methods for solving optimal stopping problems for Brownian motion. He also showed how these optimal stopping problems are closely related to some basic problems in sequential analysis and singular stochastic control. This paper gives a survey of these and related developments and describes some recent applications to option valuation in financial economics.     

On the Unification of Families of Skew-normal Distributions

Abstract.  The distribution theory literature connected to the multivariate skew-normal distribution has grown rapidly in recent years, and a number of extensions and alternative formulations have been put forward. Presently there are various coexisting proposals, similar but not identical, and with rather unclear connections. The aim of this paper is to unify these proposals under a new general formulation, clarifying at the same time their relationships. The final part sketches an extension of the argument to the skew-elliptical family.     

多元GARCH模型在国内企业债券波动传递研究中的应用

利用多元GARCH模型研究国内企业债券信用价差的波动传递问题,考察了企业债券信用价差序列的波动持续性和信用价差序列间的波动溢出效应.研究表明,相同或相关产业的企业债券表现出双向的波动溢出效应,而上游产业对下游产业企业债券的波动有显著影响,存在单向的波动溢出效应.     

高频金融数据的长记忆SV模型分析--基于FFF回归

高频金融数据的分析与建模是金融计量学的一个全新的研究领域。与低频数据不同,高频数据通常具有“日历效应”和波动长记忆性。本文在使用弹性傅立叶形式(FFF)回归技术消除“日历效应”的基础上,针对高频数据的波动长记忆性建立了长记忆SV模型,结果发现高频数据的波动持续性大大降低。     

Markov Chain Monte Carlo Methods for Fitting Spatiotemporal Stochastic Models in Plant Epidemiology

Strategies for controlling plant epidemics are investigated by fitting continuous time spatiotemporal stochastic models to data consisting of maps of disease incidence observed at discrete times. Markov chain Monte Carlo methods are used for fitting two such models to data describing the spread of citrus tristeza virus (CTV) in an orchard. The approach overcomes some of the difficulties encountered when fitting stochastic models to infrequent observations of a continuous process. The results of the analysis cast doubt on the effectiveness of a strategy identified from a previous spatial analysis of the CTV data. Extensions of the approaches to more general models and other problems are also considered.     

Generalized mixed estimator for nonlinear models: a maximum likelihood approach

This paper considers the problem of estimating a nonlinear statistical model subject to stochastic linear constraints among unknown parameters. These constraints represent prior information which originates from a previous estimation of the same model using an alternative database. One feature of this specification allows for the disign matrix of stochastic linear restrictions to be estimated. The mixed regression technique and the maximum likelihood approach are used to derive the estimator for both the model coefficients and the unknown elements of this design matrix. The proposed estimator whose asymptotic properties are studied, contains as a special case the conventional mixed regression estimator based on a fixed design matrix. A new test of compatibility between prior and sample information is also introduced. Thesuggested estimator is tested empirically with both simulated and actual marketing data.