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Asymptotic Inference of Autocovariances of Stationary Processes
Autocovariance blocks of blocks bootstrapping Box-Pierce test extreme value distribution moderate deviation normal comparison physical dependence measure short range dependence stationary process summability of cumulants
2011/6/17
The paper presents a systematic theory for asymptotic inference of autocovariances of
stationary processes.We consider nonparametric tests for serial correlations based on the maximum (or
L1) and th...
Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Hidden Markov Model Mixture Autoregressive Model Parameter Estimation
2011/6/17
This report introduces a parsimonious structure for mixture of au-
toregressive models, where the weighting coefficients are determined
through latent random variables as functions of all past obser...
Estimating Bernoulli trial probability from a small sample
estimation of population mean sampling without replacement confidence interval coin-tossing prob-lems regularized incomplete beta function Bernoulli process
2011/6/21
The standard textbook method for estimating the probability of a biased coin from finite tosses implicitly
assumes the sample sizes are large and gives incorrect results for small samples. We describ...
Estimation of rare events probabilities in computer experiments
computer experiments rare events Kriging importance sampling Bayesian estimates risk assessment with ghter aircraft
2011/6/16
We are interested in estimating probabilities of rare events in the context of com-
puter experiments. These rare events depend on the output of a physical model with
random input variables. Since t...
Testing for change in mean of heteroskedastic time series
Brownian bridge changes in mean functional central limit theorem heteroskedasticity time series
2011/3/24
In this paper we consider a Lagrange Multiplier-type test (LM) to detect change in the mean of time series with heteroskedasticity of unknown form. We derive the limiting distribution under the null, ...
Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
convergence rate coupling Gibbs sampler iterated random functions local
2011/3/24
We present a framework for obtaining explicit bounds on the rate of convergence to equilibrium of a Markov chain on a general state space, with respect to both total variation and Wasserstein distance...
Breadth First Search (BFS) is a widely used approach for sampling large unknown Internet topologies. Its main advantage over random walks and other exploration techniques is that a BFS sample is a pla...
Predictive Active Set Selection Methods for Gaussian Processes
Gaussian process classifi cation active set selection predictive distribution expectation propagation
2011/3/24
We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists on a two step alter...
Empirical process of residuals for regression models with long memory errors
Empirical process of residuals regression models
2011/3/24
We consider the residual empirical process in random design regression with long memory errors. We establish its limiting behaviour, showing that its rates of convergence are different from the rates ...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backtting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/24
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backfitting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/23
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Active Clustering Robust and Efficient Hierarchical Clustering Adaptively Selected Similarities
2011/3/25
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similaritie...
Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Rejoinder to "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]