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RISNormal limits, nonnormal limits, and the bootstrap for quantiles of dependent data
Strong Gaussian approximations of product-limit and quantile processes for truncated data under strong mixing
In this paper, we consider the product-limit quantile estimator of an unknown quantile
function under a truncated dependent model. This is a parallel problem to the estimation of
the unknown distribution ...
The strong mixing and the selfdecomposability properties
ESTIMATION OF THE SURVIVAL FUNCTION FOR ADISCRETE-TIME STOCHASTIC PROCESS
Let {Xn} be a stationary sequence of random variables with survival function
F(x) . The empirical survival function Fn (x) based on X1, X2 ,..., Xn is
proposed as an estimator for Fn (x) . We suppose that the process is strongly...
A general central limit theorem for strong mixing sequences
Subsampling for continuous time almost periodically correlated processes
A Note on the Smooth Estimator of the Quantile Function with Left-Truncated Data
This note focuses on estimating the quantile function based on the kernel smooth estimator under a truncated dependent model. The Bahadurtype representation of the kernel smooth estimator is established, and from the Bahadur ...
Laws of Large Numbers for Random Linear Programs under Dependent Models
The computational solution of large scale linear programming problems
contains various difficulties. One of the most difficult things is to ensure
numerical stability. We have another difficulty of different ...
Wavelet density estimation and statistical evidences role for a GARCH model in the weighted distribution
We consider n observations from the GARCH-type model: Z = UY , where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper bound ...
Asymptotic behaviors of the Lorenz curve for left truncated and dependent data
The purpose of this paper is to provide some asymptotic results for nonparametric estimator of the Lorenz curve and Lorenz process for the case in which data are assumed to be strong mixing subject to random left truncation. First, we show...