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938
ROBUST ADAPTIVE FILTERS USING STUDENTT DISTRIBUTION
"... An important application of adaptive filters is in system identification. Robustness of the adaptive filters to impulsive noise has been studied. In this paper, we propose an alternative way to developing robust adaptive filters. Our approach is based on formulating the problem as a maximum penal ..."
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penalized likelihood (MPL) problem. We use studentt distribution to model the noise and a quadratic penalty function to play a regularization role. The minorizationmaximization principle is used to solve the optimization problem. Based on the solution, we propose two LMStype of algorithms called MPL
Original article Bayesian QTL mapping using skewed Studentt distributions
, 2001
"... Abstract – In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Stude ..."
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Studentt distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Studentt distribution is evaluated with simulated data sets under five different scenarios
EFFICIENT ESTIMATION AND SIMULATION OF THE TRUNCATED MULTIVARIATE STUDENTt DISTRIBUTION
"... We propose an exponential tilting method for exact simulation from the truncated multivariate studentt distribution in high dimensions as an alternative to approximate Markov Chain Monte Carlo sampling. The method also allows us to accurately estimate the probability that a random vector with multi ..."
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We propose an exponential tilting method for exact simulation from the truncated multivariate studentt distribution in high dimensions as an alternative to approximate Markov Chain Monte Carlo sampling. The method also allows us to accurately estimate the probability that a random vector
Forecasting expected shortfall with a generalized asymmetric Studentt distribution
, 2009
"... Financial returns typically display heavy tails and some skewness, and conditional variance models with these features often outperform more limited models. The difference in performance may be especially important in estimating quantities that depend on tail features, including risk measures such ..."
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Cited by 4 (0 self)
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such as the expected shortfall. Here, using a recent generalization of the asymmetric Studentt distribution to allow separate parameters to control skewness and the thickness of each tail, we fit daily financial returns and forecast expected shortfall for the S&P 500 index and a number of individual company
Efficient Simulation from the Multivariate Normal and Studentt Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities
, 1991
"... The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Studentt distributions is described. It is shown how the accuracy and convergence of integrals based on the Gibbs sample may be constructed, and how an estimate of the probabil ..."
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Cited by 211 (10 self)
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The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Studentt distributions is described. It is shown how the accuracy and convergence of integrals based on the Gibbs sample may be constructed, and how an estimate
Adaptive Mixture of Studentt Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit
"... This introduction to the R package AdMit is a shorter version of Ardia et al. (2009), published in the Journal of Statistical Software. The package provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kern ..."
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Cited by 4 (0 self)
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kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Studentt distributions to the density of interest. Then, importance sampling or the independence chain MetropolisHastings algorithm is used to obtain quantities of interest
Efficient Sampling Methods for Truncated Multivariate Normal and Studentt Distributions Subject to Linear Inequality Constraints
"... Sampling from a truncated multivariate normal distribution subject to multiple linear inequality constraints is a recurring problem in many areas in statistics and econometrics, such as the order restricted regressions, censored data models, and shaperestricted nonparametric regressions. However, t ..."
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the Gibbs sampler for sampling from a truncated multivariate normal distribution with convex polytope restriction regions. We also generalize the sampling method to truncated multivariate Studentt distributions. Empirical results 1 are presented to illustrate the superior performance of our proposed Gibbs
NEW METHODS FOR SIMULATING THE STUDENT TDISTRIBUTION DIRECT USE OF THE INVERSE CUMULATIVE DISTRIBUTION
"... We explore the Student TDistribution and present some new techniques for simulation. In particular, an explicit and accurate approximation for the inverse, F −1 n of the CDF, Fn, is presented, as well as some simple exact and iterative techniques for defining this function. The methods presented ar ..."
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We explore the Student TDistribution and present some new techniques for simulation. In particular, an explicit and accurate approximation for the inverse, F −1 n of the CDF, Fn, is presented, as well as some simple exact and iterative techniques for defining this function. The methods presented
AdMit: Adaptive Mixture of Studentt Distributions for Efficient Simulation in R, 2008. URL http:// CRAN.Rproject.org/package=AdMit. R package version
"... This note presents the package AdMit (Ardia et al., 2008, 2009), an R implementation of the adaptive mixture of Studentt distributions (AdMit) procedure developed by Hoogerheide (2006); see also ..."
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This note presents the package AdMit (Ardia et al., 2008, 2009), an R implementation of the adaptive mixture of Studentt distributions (AdMit) procedure developed by Hoogerheide (2006); see also
Linearization coefficients of Bessel polynomials and properties of Student tdistributions
 ORNSTEINUHLENBECK PROCESSES IN PHYSICS AND ENGINEERING WULFSOHN, AUBREY University of
, 2006
"... We prove positivity results about linearization and connection coefficients for Bessel polynomials. The proof is based on a recursion formula and explicit formulas for the coefficients in special cases. The result implies that the distribution of a convex combination of independent Studentt random v ..."
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Cited by 6 (2 self)
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variables with arbitrary odd degrees of freedom has a density which is a convex combination of certain Studentt densities with odd degrees of freedom. 2000 Mathematics Subject Classification: primary 33C10; secondary 60E05
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