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20152020
most citedSpatial analysis and prediction of COVID-19 spread in South Africa after lockdown

28 citations · 39 across the 10 of their papers we have counts for

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7 papers · 1 filter

math.ST2019

Improving efficiency in fuzzy regression modeling by Stein-type shrinkage

M. Kashani, M. Arashi, M. R. Rabiei

The fuzzy linear regression (FLR) modeling was first proposed making use of linear programming and then followed by many improvements in a variety of ways. In almost all approaches…

math.ST20172 cited

Some theoretical results on tensor elliptical distribution

M. Arashi

The multilinear normal distribution is a widely used tool in tensor analysis of magnetic resonance imaging (MRI). Diffusion tensor MRI provides a statistical estimate of a symmetri…

math.ST2017

Preliminary testing derivatives of a linear unified estimator in the logistic regression model

Yasin Asar, Bahadır Yüzbaşı, Mohammad Arashi +1

Recently, the well known Liu estimator (Liu, 1993) is attracted researcher's attention in regression parameter estimation for an ill conditioned linear model. It is also argued tha…

math.ST2017

A sure independence screening procedure for ultra-high dimensional partially linear additive models

M. Kazemi, D. Shahsavani, M. Arashi

We introduce a two-step procedure, in the context of ultra-high dimensional additive models, which aims to reduce the size of covariates vector and distinguish linear and nonlinear…

math.ST2017

On the restricted almost unbiased Liu estimator in the Logistic regression model

Jibo Wu, Yasin Asar, M. Arashi

It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, in the context of biased shr…

math.ST20157 cited

Restricted LASSO and Double Shrinking

M. Norouzirad, M. Arashi, A. K. Md. Ehsanes Saleh

In the context of multiple regression model, suppose that the vector parameter of interest βis subjected to lie in the subspace hypothesis Hβ= h, where this restriction is based on…