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Data cloning estimation of GARCH and COGARCH models

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2015年第9期85卷 1818-1831页

作者： Marin, J. M. Rodriguez-Bernal, M. T. Romero, E. Univ Carlos III Madrid Dept Estadist Madrid Spain Univ Complutense Madrid Dept Estadist & Invest Operat Madrid Spain Univ Europea Madrid Dept Econ & Relac Int Madrid Spain

GARCH models include most of the stylized facts of financial time series and they have been largely used to analyse discrete financial time series. In the last years, continuous-time models based on discrete GARCH models have been also proposed to deal with non-equally spaced observations, as COGARCH model based on Levy processes. In this paper, we propose to use the data cloning methodology in order to obtain estimators of GARCH and COGARCH model parameters. Data cloning methodology uses a Bayesian approach to obtain approximate maximum likelihood estimators avoiding numerically maximization of the pseudo-likelihood function. After a simulation study for both GARCH and COGARCH models using data cloning, we apply this technique to model the behaviour of some NASDAQ time series.

关键词： C11 C15 C22 COGARCH model Bayesian inference continuous-time GARCH process GARCH model Levy process mcmc algorithm data cloning

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Inference on progressive-stress model for the exponentiated exponential distribution under type-II progressive hybrid censoring

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2015年第6期85卷 1165-1186页

作者： Abdel-Hamid, Alaa H. Abushal, Tahani A. Beni Suef Univ Dept Math Fac Sci Bani Suwayf Egypt Umm Al Qura Univ Dept Math Mecca Saudi Arabia

In this paper, progressive-stress accelerated life tests are applied when the lifetime of a product under design stress follows the exponentiated distribution [G(x)](alpha). The baseline distribution, G(x), follows a general class of distributions which includes, among others, Weibull, compound Weibull, power function, Pareto, Gompertz, compound Gompertz, normal and logistic distributions. The scale parameter of G(x) satisfies the inverse power law and the cumulative exposure model holds for the effect of changing stress. A special case for an exponentiated exponential distribution has been discussed. Using type-II progressive hybrid censoring and mcmc algorithm, Bayes estimates of the unknown parameters based on symmetric and asymmetric loss functions are obtained and compared with the maximum likelihood estimates. Normal approximation and bootstrap confidence intervals for the unknown parameters are obtained and compared via a simulation study.

关键词： progressive-stress accelerated life tests maximum likelihood and Bayes estimations exponentiated exponential distribution mcmc algorithm simulation type-II progressive hybrid censoring cumulative exposure model bootstrap confidence intervals

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Implicit Estimation for the Stochastic Volatility Model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2014年第6期43卷 1061-1076页

作者： Graja, Asma Jarraya, Aida Masmoudi, Afif Sfax Univ Lab Probabil & Stat Sfax Tunisia

Efficient, accurate, and fast Markov Chain Monte Carlo estimation methods based on the Implicit approach are proposed. In this article, we introduced the notion of Implicit method for the estimation of parameters in Stochastic Volatility models. Implicit estimation offers a substantial computational advantage for learning from observations without prior knowledge and thus provides a good alternative to classical inference in Bayesian method when priors are missing. Both Implicit and Bayesian approach are illustrated using simulated data and are applied to analyze daily stock returns data on CAC40 index.

关键词： Bayesian analysis Gibbs Implicit distribution mcmc algorithm Metropolis Hastings Stochastic Volatility model Heston model

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Adaptive Mixtures of Regressions: Improving Predictive Inference when Population has Changed

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2014年第10期43卷 2570-2592页

作者： Bouveyron, C. Jacques, J. Univ Paris 01 EA4543 Lab SAMM F-75231 Paris 05 France Univ Lille 1 UMR CNRS 8524 Lab Paul Painleve Lille France INRIA Lille Nord Europe MODAL Team Lille France PolytechLille Lille France

The present work investigates the estimation of regression mixtures when population has changed between the training and the prediction stages. Two approaches are proposed: a parametric approach modeling the relationship between dependent variables of both populations, and a Bayesian approach in which the priors on the prediction population depend on the mixture regression parameters of the training population. The relevance of both approaches is illustrated on simulations and on an environmental dataset.

关键词： Bayesian inference EM algorithm mcmc algorithm Mixture of regressions Switching regression Transfer learning

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Latent variable models with nonparametric interaction effects of latent variables

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STATISTICS IN MEDICINE 2014年第10期33卷 1723-1737页

作者： Song, Xinyuan Lu, Zhaohua Feng, Xiangnan Chinese Univ Hong Kong Dept Stat Shatin Hong Kong Peoples R China

Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P-splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： latent variables Bayesian P-splines nonparametric interaction effects mcmc algorithm semiparametric models

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Forecasting Container Throughputs With Domain Knowledge

Forecasting Container Throughputs With Domain Knowledge

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2nd International Conference on Information Technology and Quantitative Management (ITQM)

作者： Huang, Anqiang Qiao, Han Wang, Shouyang Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

In order to alleviate the limitation of traditional statistical models utilizing only structured data, this paper proposes a new forecasting method, which is able to take full advantage of domain knowledge and avoid many kinds of biases and inconsistencies inherent in subjective judgments. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, we compare its performance with that of the frequently-used ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model. (C) 2014 Published by Elsevier B.V. Open access under CC BY-NC-ND license.

关键词： Container throughput forecast Judgemental adjustment Domain knowledge mcmc algorithm

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Forecasting Container Throughputs with Domain Knowledge

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Procedia Computer Science 2014年 31卷 648-655页

作者： Anqiang Huang Han Qiao Shouyang Wang School of Economy and Management Beihang University No.37 XueYuan Road HaiDian District Beijing 100191 China School of Management University of Chinese Academy of Sciences No.80 Zhongguancun East Road Beijing 100190 China Academy of Mathematics and Systems Science Chinese Academy of Sciences No.55 Zhongguancun East Road Beijing 100190 China

In order to alleviate the limitation of traditional statistical models utilizing only structured data, this paper proposes a new fore- casting method, which is able to take full advantage of domain knowledge and avoid many kinds of biases and inconsistencies inherent in subjective judgments. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, we compare its performance with that of the frequently-used ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model.

关键词： Container throughput forecast Judgemental adjustment Domain knowledge mcmc algorithm

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Bayesian Estimation of Non-Gaussian Stochastic Volatility Models

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Journal of Mathematical Finance 2014年 4卷 95-103页

作者： Asma Graja Elabed Afif Masmoudi Sfax University Sfax Tunisia

In this paper, a general Non - Gaussian Stochastic Volatility model is proposed instead of the usual Gaussian model largely studied. We consider a new specification of SV model where the innovations of the return process have centered non - Gaussian error distribution rather than the standard Gaussian distribution usually employed. The model describes the behaviour of random time fluctuations in stock prices observed in the financial markets. It offers a response to better model the heavy tails and the abrupt changes observed in financial time series. We consider the Laplace density as a special case of non - Gaussian SV models to be applied to our data base. Markov Chain Monte Carlo technique, based on the bayesian analysis, has been employed to estimate the model’s parameters.

关键词： on-Gaussian Distribution Stochastic Volatility Laplace Density Fat Tails Kullback Leiber Divengence Bayesian Analysis mcmc algorithm

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A Latent Source Model to Detect Multiple Spatial Clusters With Application in a Mobile Sensor Network for Surveillance of Nuclear Materials

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2013年第503期108卷 902-913页

作者： Cheng, Jerry Q. Xie, Minge Chen, Rong Roberts, Fred Rutgers State Univ Robert Wood Johnson Med Sch New Brunswick NJ 08901 USA Rutgers State Univ Dept Stat & Biostat Piscataway NJ 08854 USA Cent Univ Econ & Finance Sch Stat Beijing 100081 Peoples R China Rutgers State Univ Command Control & Interoperabil Ctr Adv Data Anal Piscataway NJ 08854 USA Rutgers State Univ Ctr Discrete Math & Theoret Comp Sci DIMACS Piscataway NJ 08854 USA

Potential nuclear attacks are among the most devastating terrorist attacks, with severe loss of human lives as well as damage to infrastructure. To deter such threats, it becomes increasingly vital to have sophisticated nuclear surveillance and detection systems deployed in major cities in the United States, such as New York City. In this article, we design a mobile sensor network and develop statistical algorithms and models to provide consistent and pervasive surveillance of nuclear materials in major cities. The network consists of a large number of vehicles on which nuclear sensors and Global Position System (GPS) tracking devices are installed. Real time sensor readings and GPS information are transmitted to and processed at a central surveillance center. Mathematical and statistical analyses are performed, in which we mimic a signal-generating process and develop a latent source modeling framework to detect multiple spatial clusters. A Monte Carlo expectation-maximization algorithm is developed to estimate model parameters, detect significant clusters, and identify their locations and sizes. We also determine the number of clusters using a modified Akaike Information Criterion/Bayesian Information Criterion. Simulation studies to evaluate the effectiveness and detection power of such a network are described.

关键词： AIC and BIC criteria Cluster detection EM algorithm Likelihood inference mcmc algorithm Nuclear detection and surveillance

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On Numerical Aspects of Bayesian Model Selection in High and Ultrahigh-dimensional Settings

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BAYESIAN ANALYSIS 2013年第4期8卷 741-758页

作者： Johnson, Valen E. Texas A&M Univ Dept Stat College Stn TX 77843 USA

This article examines the convergence properties of a Bayesian model selection procedure based on a non-local prior density in ultrahigh-dimensional settings. The performance of the model selection procedure is also compared to popular penalized likelihood methods. Coupling diagnostics are used to bound the total variation distance between iterates in an Markov chain Monte Carlo (mcmc) algorithm and the posterior distribution on the model space. In several simulation scenarios in which the number of observations exceeds 100, rapid convergence and high accuracy of the Bayesian procedure is demonstrated. Conversely, the coupling diagnostics are successful in diagnosing lack of convergence in several scenarios for which the number of observations is less than 100. The accuracy of the Bayesian model selection procedure in identifying high probability models is shown to be comparable to commonly used penalized likelihood methods, including extensions of smoothly clipped absolute deviations (SCAD) and least absolute shrinkage and selection operator (LASSO) procedures.

关键词： mcmc algorithm convergence diagnostic coupling SCAD sure independence screening penalized likelihood variable selection

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