we propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of pairwise negative quadrant dependent random variables with a common one-dimensional probability density function. some asymptotic properties of the proposed estimator are investigated. it is found that the estimators have similar properties to their counterparts st...

& Conclusions-Risk analysis is discussed within a Bayes framework. Traditionally, Bayes parameter estimation is based on a quadratic loss-function. This paper introduces an alternative asymmetric precautionary loss-function, derives its main features, and presents a general class of precautionary loss-functions with the quadratic loss-function as a special case. These loss functions approach in...

in this paper, a bayesian approach is proposed for shift point detection in an inverse gaussian distribution. in this study, the mean parameter of inverse gaussian distribution is assumed to be constant and shift points in shape parameter is considered. first the posterior distribution of shape parameter is obtained. then the bayes estimators are derived under a class of priors and using variou...

In this paper the problem of parametric point estimation is addressed from an objective Bayesian viewpoint. Arguing that pure statistical estimation may be appropriately described as a precise decision problem where the loss function is a measure of the divergence between the assumed model and the estimated model, the information-based intrinsic discrepancy is proposed as an appropriate loss fu...

In this article we study function estimation via wavelet shrinkage for data with long-range dependence. We propose a fractional Gaussian noise model to approximate nonparametric regression with long-range dependence and establish asymp-totics for minimax risks. Because of long-range dependence, the minimax risk and the minimax linear risk converge to zero at rates that diier from those for data...

General results on adaptive function estimation are obtained with respect to a collection of estimation strategies for both density estimation and nonparametric regression under square L 2 loss. It is shown that without knowing which strategy in a given countable collection works best for the underlying function, a single strategy can be constructed by mixing the proposed ones so that it is ada...

In this paper, we propose and study exp-kumaraswamy distribution. Some of its properties  are derived, including the density function, hazard rate function, quantile function, moments,  skewness  and kurtosis.   Adata set isused to illustrate an application of the proposed distribution. Also, we obtain a new distribution by transformation onexp-kumaraswamy distribution.   New distribution is an...

An inspection of signal processing approach in order to estimate underwater network cardinalities is conducted in this research. A matter of key prominence for underwater network is its cardinality estimation as the number of active cardinalities varies several times due to numerous natural and artificial reasons due to harsh underwater circumstances. So, a proper estimation technique is mandat...

Vulnerability assessment of structures encounter many uncertainties like seismic excitations intensity and response of structures. The most common approach adopted to deal with these uncertainties is vulnerability assessment through fragility functions. Fragility functions exhibit the probability of exceeding a state namely performance-level as a function of seismic intensity. A common approach...