Wavelet Based Estimation of the Derivatives of a Density for m-Dependent Random Variables

Authors

  • Hassan Doosti
  • Yogendra P. Chaubey
Abstract:

Here, we propose a method of estimation of the derivatives of probability density based wavelets methods for a sequence of m−dependent random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for the such estimators.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Estimation of the Survival Function for Negatively Dependent Random Variables

Let be a stationary sequence of pair wise negative quadrant dependent random variables with survival function {,1}nXn?F(x)=P[X>x]. The empirical survival function ()nFx based on 12,,...,nXXX is proposed as an estimator for ()nFx. Strong consistency and point wise as well as uniform of ()nFx are discussed

full text

Linear Wavelet-Based Estimation for Derivative of a Density under Random Censorship

In this paper we consider estimation of the derivative of a density based on wavelets methods using randomly right censored data. We extend the results regarding the asymptotic convergence rates due to Prakasa Rao (1996) and Chaubey et al. (2008) under random censorship model. Our treatment is facilitated by results of Stute (1995) and Li (2003) that enable us in demonstrating that the same con...

full text

Wavelet Based Estimation of the Derivatives of a Density for a Discrete-Time Stochastic Process: Lp-Losses

We propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for such estimators. We suppose that the process is strongly mixing and we show that the rate of convergence essentially depends on the behavior of a special quad...

full text

estimation of the survival function for negatively dependent random variables

let be a stationary sequence of pair wise negative quadrant dependent random variables with survival function {,1}nxn?f(x)=p[x>x]. the empirical survival function ()nfx based on 12,,...,nxxx is proposed as an estimator for ()nfx. strong consistency and point wise as well as uniform of ()nfx are discussed

full text

ON THE LAWS OF LARGE NUMBERS FOR DEPENDENT RANDOM VARIABLES

In this paper, we extend and generalize some recent results on the strong laws of large numbers (SLLN) for pairwise independent random variables [3]. No assumption is made concerning the existence of independence among the random variables (henceforth r.v.’s). Also Chandra’s result on Cesàro uniformly integrable r.v.’s is extended.

full text

wavelet based estimation of the derivatives of a density for a discrete-time stochastic process: lp-losses

we propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function and obtain an upper bound on lp-losses for such estimators. we suppose that the process is strongly mixing and we show that the rate of convergence essentially depends on the behavior of a special quad...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 4  issue None

pages  97- 105

publication date 2005-11

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023