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A Residual Based Multiple Testing Procedure for Variance Changepoints
Multiple testing Admissibility Multiple change points Variance change
2016/1/19
In this paper, we approach the variance changepoints detection problem from a multiple testing setting. After proving that the standard Step-Up and Step-Down procedures are inadmissible for the standa...
One may consider a discrete-event simulation as a Markov chain evolving on a suitably rich state space. One way that regenerative cycles may be constructed for general state-space Markov chains is to ...
Parametric Stein operators and variance bounds
Chernoff inequality Cramer-Rao inequality parameter of interest Stein charac-terization Stein’s method
2013/6/17
Stein operators are differential operators which arise within the so-called Stein's method for stochastic approximation. We propose a new mechanism for constructing such operators for arbitrary (conti...
Warped Functional Analysis of Variance
Karhunen–Lo`eve decomposition longitudinal data phasevariability quan-titative genetics random-effect models
2013/4/28
This article presents a functional Analysis of Variance model that explicitly incorporates phase variability through a time-warping component, allowing for a unified and parsimonious approach to estim...
Variance estimation for Brier Score decomposition
Variance estimation Brier Score decomposition
2013/4/28
The Brier Score is a widely-used criterion to assess the quality of probabilistic predictions of binary events. The expectation value of the Brier Score can be decomposed into the sum of three compone...
Multifidelity variance reduction for pick-freeze Sobol index estimation
Multifidelity variance reduction pick-freeze Sobol index estimation
2013/4/28
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs
covariance function finite population Hajek approximation Horvitz-Thompso estimator Kullback-Leibler divergence rejective sampling unequal probability sampling without replacement
2012/11/23
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the H\'ajek formula. The interest of this asymptotic varia...
Diagnostic Tests for Non-causal Time Series with Infinite Variance
Non-causal AR Process Infinite Variance Goodness-of-fit Portmanteau Test alpha-stabledistribution
2012/11/22
We study goodness-of-fit testing for non-causal autoregressive time series with non-Gaussian stable noise. To model time series exhibiting sharp spikes or occasional bursts of outlying observations, t...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
Variance Decomposition and Replication In Scrabble: When You Can Blame Your Tiles?
Variance Decomposition eplication In Scrabble
2011/7/19
In the game of Scrabble, letter tiles are drawn uniformly at random from a bag. The variability of possible draws as the game progresses is a source of variation that makes it more likely for an infer...
Corrected portmanteau tests for VAR models with time-varying variance
VAR model Unconditionally heteroscedastic errors Residual autocorrelations Portmanteau tests
2011/6/20
The problem of test of fit for Vector AutoRegressive (VAR) processes
with unconditionally heteroscedastic errors is studied. The volatility structure is
deterministic but time-varying and allows for...
CLTs and asymptotic variance of time-sampled Markov chains
time-sampled Markov chains Barker’s algo-rithm Metropolis algorithm Central Limit Theorem asymptotic variance variance bounding Markov chains MCMC estimation
2011/3/25
For a Markov transition kernel $P$ and a probability distribution $ \mu$ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel $P_{\mu} = \sum_k \mu(k)P^k.$ I...
A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), i...
Reconsidering the asymptotic null distribution of likelihood ratio tests for genetic linkage in multivariate variance components models
asymptotic null distribution likelihood ratio test mixing probabilities multivariate linkage
2010/4/30
Accurate knowledge of the null distribution of hypothesis tests is important for valid application of the tests. In previous papers and software, the asymptotic null distribution of likelihood ratio t...
Multivariate Granger Causality and Generalized Variance
Granger causality causal inference multivariate statistics generalized variance
2010/3/11
Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is
that it on...