in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

Ridge regression, being based on the minimization of a quadratic loss function, is sensitive to outliers. Current proposals for robust ridge regression estimators are sensitive to “bad leverage observations”, cannot be employed when the number of predictors p is larger than the number of observations n; and have a low robustness when the ratio p=n is large. In this paper a ridge regression esti...

Recently we have proposed a maximum-likelihood iterative algorithm for estimation of parameters of the Nakagamim distribution. This technique performs better than state of art estimation techniques for this distribution. This could be of particular use in low-data/block based estimation problems. In these scenarios, the estimator should be able to give accurate estimates (in the mean square sen...

Consider a semi-Markov process that, after entering state /, next goes to state j with probability P/ y , and given that the next state isy, the time until the transition from / to j occurs is a random variable with distribution F/j having mean m(i,j). Starting in state 0, suppose we are interested in estimating n = E[T], where T, called the cover time, is the time until all of the states 1,2,....

1 Estimation; the Cramer-Rao inequality Let ρ η (x) be a probability density, depending on a parameter η ∈ R. The Fisher information of ρ η is defined to be [8] G := ρ η (x) ∂ log ρ η (x) ∂η 2 dx. (1) We note that this is the variance of the random variable Y = ∂ log ρ η /∂η, which has mean zero. G is associated with the family M = {ρ η } of distributions, rather than any one of them. This conc...

in this paper, drift change point estimation in the mean of polynomial profiles is considered. for this purpose, the proposed change point estimator is computed using maximum likelihood approach. performance of the proposed estimator is evaluated using monte carlo simulations when t2 control chart issues an out-of-control signal. simulation results show that the performance of the proposed esti...

this paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. a novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. parameters of the fuzzy estimator are adapted to minimize the estimat...

We investigate the estimation of a density f from n-sample on an Euclidean space RD, when data are supported by unknown submanifold M possibly dimension d<D, under reach condition. several nonparametric kernel methods, with data-driven bandwidths that incorporate some learning geometry via local estimator. When has Hölder smoothness ? and regularity ?, our estimator achieves rate n?????(2???+d)...

We study the following model of deconvolution Y = X+ε with i.i.d. observations Y1, . . . , Yn and ε−1, . . . , ε−M . The (Xj)1≤j≤n are i.i.d. with density f , independent of the εj . The aim of the paper is to estimate f without knowing the density fε of the εj . We first define an estimator, for which we provide bounds for the pointwise and the integrated L-risk. We consider ordinary smooth an...