Robust online algorithm for adaptive linear regression parameter estimation and prediction

2016 ◽  
Vol 30 (6) ◽  
pp. 308-323 ◽  
Author(s):  
Shekhar Sharma ◽  
Swanand Khare ◽  
Biao Huang
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Online Algorithm ◽  
Regression Parameter ◽  
Estimation And Prediction

Optimal Designs in Random Coefficient Linear Regression Models

1996 ◽  
Vol 46 (3-4) ◽  
pp. 211-230 ◽  
Author(s):  
Erkki P. Liski ◽  
Arto Luoma ◽  
Bikas K. Sinha
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Upper Bound ◽  
Regression Models ◽  
Random Coefficients ◽  
Repeated Measurements ◽  
Optimal Designs ◽  
Slope Parameter ◽  
Linear Regression Models ◽  
Estimation And Prediction

In this paper we consider a random coefficients regression model in the context of repeated measurements. The measurements are taken at consecutive points for several experimental units, and the total number of measurements have a fixed upper bound. Observations on the same unit at different points will be correlated while observations on two differents units are uncorrelated. We present optimal designs for slope parameter estimation and prediction.

The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data

2019 ◽  
Vol 29 (5) ◽  
pp. 1434-1446
Author(s):  
Francisco Louzada ◽  
Taciana KO Shimizu ◽  
Adriano K Suzuki
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Large Scale ◽  
Compositional Data ◽  
Child Malnutrition ◽  
Lasso Regression ◽  
Brazilian Child ◽  
Ilr Coordinates ◽  
Log Ratio ◽  
Brazilian Children

There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data.

scholarly journals On guaranteed parameter estimation of a multiparameter linear regression process

Automatica ◽  
2010 ◽  
Vol 46 (4) ◽  
pp. 637-646 ◽  
Author(s):  
Uwe Küchler ◽  
Vyacheslav A. Vasiliev
Keyword(s):  
Parameter Estimation ◽  
Linear Regression

scholarly journals Parameter estimation and prediction for groundwater contamination based on measure theory

2015 ◽  
Vol 51 (9) ◽  
pp. 7608-7629 ◽  
Author(s):  
S. A. Mattis ◽  
T. D. Butler ◽  
C. N. Dawson ◽  
D. Estep ◽  
V. V. Vesselinov
Keyword(s):  
Parameter Estimation ◽  
Groundwater Contamination ◽  
Measure Theory ◽  
Estimation And Prediction
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