This paper describes a new robust multiple linear regression method, which based on the segmentation of the N dimensional space to N+1 sector. An N dimensional regression plane is located so that the half (or other) part of the points are under this plane in each sector. This article also presents a simple algorithm to calculate the parameters of this regression plane. This algorithm is scalable well by the dimension and the count of the points, and capable to calculation with other (not 0.5) quantiles. This paper also contains some studies about the described method, which analyze the result with different datasets and compares to the linear least squares regression. Sector Based Linear Regression (SBLR) is the multidimensional generalizat...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Multiple linear regressions (MLR) model is an important tool for investigating relationships between...
Abstract. A common problem in linear regression is that largely aberrant values can strongly influen...
This paper introduces a new data analysis method for big data using a newly defined regression model...
The aim of this study is to compare different robust regression methods in three main models of mult...
: The problem of n-dimensional orthogonal linear regression is a problem of finding an n-dimensional...
Ridge regression is an alternative to ordinary least-squares (OLS) regression. It is believed to be ...
This dissertation examines the robust regression methods. The primary purpose of this work is to pro...
A procedure relying on linear programming techniques is developed to compute (regression) quantile r...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
2002 Mathematics Subject Classification: 62J05, 62G35.In classical multiple linear regression analys...
Robust regression is a regression method used when the remainder's distribution is not reasonable, o...
Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information...
International audienceThis chapter deals with the multiple linear regression. That is we investigate...
Abstract. In classical multiple linear regression analysis problems will occur if the regressors are...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Multiple linear regressions (MLR) model is an important tool for investigating relationships between...
Abstract. A common problem in linear regression is that largely aberrant values can strongly influen...
This paper introduces a new data analysis method for big data using a newly defined regression model...
The aim of this study is to compare different robust regression methods in three main models of mult...
: The problem of n-dimensional orthogonal linear regression is a problem of finding an n-dimensional...
Ridge regression is an alternative to ordinary least-squares (OLS) regression. It is believed to be ...
This dissertation examines the robust regression methods. The primary purpose of this work is to pro...
A procedure relying on linear programming techniques is developed to compute (regression) quantile r...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
2002 Mathematics Subject Classification: 62J05, 62G35.In classical multiple linear regression analys...
Robust regression is a regression method used when the remainder's distribution is not reasonable, o...
Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information...
International audienceThis chapter deals with the multiple linear regression. That is we investigate...
Abstract. In classical multiple linear regression analysis problems will occur if the regressors are...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Multiple linear regressions (MLR) model is an important tool for investigating relationships between...
Abstract. A common problem in linear regression is that largely aberrant values can strongly influen...