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Some Diffusion Processes Associated With Two Parameter Poisson-Dirichlet Distribution and Dirichlet Process
Some Diffusion Processes Two Parameter Poisson-Dirichlet Distribution Dirichlet Process
2010/3/19
The two parameter Poisson-Dirichlet distribution PD( , ) is the distribution
of an infinite dimensional random discrete probability. It is
a generalization of Kingman’s Poisson-Dirichlet distributi...
Filtering and parameter estimation for a jump stochastic process with discrete observations
Filtering parameter estimation discrete observations
2009/3/19
A compound Poisson process is considered. We estimate the current position of the stochastic process based on past discrete-time observations (non-linear discrete filtering problem) in Bayesian settin...
Estimation of Moment Parameter in Elliptical Distributions
asymptotic expansion consistent estimator elliptical distribution kurtosis parameter moment parameter Monte Carlo simulation perturbation method
2009/3/10
As a typical non-normal case, we consider a family of elliptically symmetric distributions. Then, the moment parameter and its consistent estimator are presented. Also, the asymptotic expectation and ...
The Cusum Test for Parameter Change in Regression Models with ARCH Errors
Brownian bridge regression models with ARCH errors residual cusum test test for parameter change weak convergence
2009/3/10
In this paper we consider the problem of testing for a parameter change in regression models with ARCH errors based on the residual cusum test. It is shown that the limiting distribution of the residu...
Estimating the Smoothing Parameter in the So-called Hodrick-Prescott Filter
adaptive estimation Hodrick-Prescott filter Kalman-Bucy Kalman filtering orthogonal parametrization, random walk, seasonal adjustment, spline state-space models time-series time-varying coefficients trend Whittaker-Henderson graduation
2009/3/9
This note gives a statistical description of the Hodrick-Prescott Filter (1997), originally proposed by Leser (1961). A maximum-likelihood estimator is derived and a related moments estimator is propo...
On the Estimation of a Restricted Location Parameter for Symmetric Distributions
Bayes estimator Cauchy and Student models dominance logconcave densities logconvex densities, maximum likelihood estimator, restricted parameter space scale mixture of Laplace densities squared error loss symmetric location families
2009/3/5
For estimating the median θ of a spherically symmetric univariate distribution under squared error loss, when θ is known to be restricted to an interval [−m,m], m known, we derive sufficient con...
A Consistent Estimator of the Smoothing Parameter in the Hodrick-Prescott Filter
Adaptive estimation Gaussian process Hodrick-Prescott filter orthogonal parametrization
2009/3/5
The so-called Hodrick-Prescott filter was first introduced in actuarial science to estimate trends from claims data and now is widely used in economics and finance to estimate and predict e.g. busines...
Parameter estimation of S-distributions with alternating regression
Alternating regression Parameter estimation S-distribution S-system
2009/2/23
We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the
estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast a...
Parameter estimation for fractional Ornstein-Uhlenbeck processes
Parameter estimation fractional Ornstein-Uhlenbeck processes
2010/3/17
We study a least squares estimator bT for the Ornstein-Uhlenbeck
process, dXt = Xtdt+dBHt , driven by fractional Brownian motion BH
with Hurst parameter H 12 . We prove the strong consistence o...
Exact confidence intervals for the Hurst parameter of a fractional Brownian motion
Concentration Inequalities Exact confidence intervals Fractional Brownian motion Hurst parameter
2010/3/17
In this short note, we show how to use concentration inequalities in order to build exact
confidence intervals for the Hurst parameter associated with a one-dimensional fractional Brownian motion.
Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
Equilibrium climate sensitivity observed and modeled climate space–time modeling statistical surrogate temperature data
2010/3/17
Nonlinear regression is a useful statistical tool, relating observed
data and a nonlinear function of unknown parameters. When the
parameter-dependent nonlinear function is computationally intensive...
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation scheme parameter inference model selection dynamical systems
2010/3/17
Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation...
Adaptive wavelet based estimator of the memory parameter for stationary Gaussian processes
Adaptive wavelet estimator memory parameter stationary Gaussian processes
2010/4/26
This work is intended as a contribution to a wavelet-based adaptive estimator of the memory parameter in the classical semi-parametric framework for Gaussian stationary processes. In particular we int...
Parameter Estimation in Manneville-Pomeau Processes
Manneville-Pomeau Maps Long and Not so Long Dependence Estimation Autocorrelation Decay
2010/4/30
In this work we study a class of stochastic processes {Xt}t2N, where Xt = (◦ T t
s )(X0) is obtained from the iterations of the transformation Ts, invariant for an ergodic probability μs on [0,...
Asymptotically Optimal Estimator of the Parameter of Semi-Linear Autoregression
Martingale estimator optimization convergence
2010/4/30
The difference equations k = af(k−1) + "k, where ("k) is a square
integrable difference martingale, and the differential equation d =
−af()dt + d, where is a square integrable mar...