the fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. a linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. in contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...

Many biological high-throughput datasets, such as targeted amplicon-based and metagenomic sequencing data, are compositional. A common exploratory data analysis task is to infer robust statistical associations between high-dimensional microbial compositions habitat- or host-related covariates. To address this, a general regression framework RobRegCC (Robust Regression with Compositional Covaria...

Abstract Real-world datasets are often characterised by outliers; data items that do not follow the same structure as rest of data. These outliers might negatively influence modelling In analysis it is, therefore, important to consider methods robust outliers. this paper we develop a regression method finds largest subset can be approximated using sparse linear model given precision. We show yi...

to calculate partial and total productivity of production factors in broiler farms in yazd province, 72 manufacturing units were selected based on simple random sampling method and their information and statistics were collected for one production period in the second half of 2013. to measure productivity, the cobb-douglas production function was estimated using classic and fuzzy regression met...

We study random design linear regression with no assumptions on the distribution of covariates and a heavy-tailed response variable. In this distribution-free setting, we show that boundedness conditional second moment given is necessary sufficient condition for achieving nontrivial guarantees. As starting point, prove an optimal version classical in-expectation bound truncated least squares es...

Where fuzzy regression can be applied and, in which conditions fuzzy regression method more appropriate tool for the investigations are identified in this paper. The contrast between fuzzy regression and ordinary regression analysis and three approach of fuzzy regression are summarized. Key-Words: fuzzy regression, vaguness, fuzziness, randomness, insufficient data, assumptions.