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Limit theorems for kernel density estimators under dependent samples
Kernel density estimator consistency convergence rate mixing rate
2013/6/14
In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated....
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Regularized M-estimators nonconvexity Statistical algorithmic theory local optima
2013/6/14
We establish theoretical results concerning all local optima of various regularized M-estimators, where both loss and penalty functions are allowed to be nonconvex. Our results show that as long as th...
A General Family of Estimators for Estimating Population Mean in Systematic Sampling Using Auxiliary Information in the Presence of Missing Observations
Family of estimators Auxiliary information Mean square error Non-response Systematic sampling
2013/6/14
This paper proposes a general family of estimators for estimating the population mean in systematic sampling in the presence of non-response adapting the family of estimators proposed by Khoshnevisan ...
Calculation of Exact Estimators by Integration Over the Surface of an n-Dimensional Sphere
Calculation Exact Estimators Integration Over the Surface an n-Dimensional Sphere
2013/6/13
This paper reconsiders the problem of calculating the expected set of probabilities , given the observed set of items {m_i}, that are distributed among n bins with an (unknown) set of probabiliti...
Asymptotic normality and efficiency of two Sobol index estimators
sensitivity analysis Sobol indices asymptotic efficiency asymptotic normality confidence intervals metamodelling surface response methodology
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...
Asymptotic Behaviour of Approximate Bayesian Estimators
Parameter Estimation Hidden Markov Model Maximum Likelihood Approximate Bayesian Computation Sequential Monte Carlo
2011/6/20
Although approximate Bayesian computation (ABC) has become
a popular technique for performing parameter estimation when the
likelihood functions are analytically intractable there has not as yet
be...
Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods
random matrix related statistical shrinkage estimators
2011/6/16
Shrinkage estimators of covariance are an important tool in modern applied and theoretical statistics.
They play a key role in regularized estimation problems, such as ridge regression (aka Tykhonov
...