scholarly journals On computational complexity of construction of c -optimal linear regression models over finite experimental domains

2012 ◽  
Vol 51 (1) ◽  
pp. 11-21
Author(s):  
Jaromír Antoch ◽  
Michal Černý ◽  
Milan Hladík
Keyword(s):  
Linear Programming ◽  
Regression Models ◽  
Parallel Implementation ◽  
Optimal Designs ◽  
Polynomial Algorithms ◽  
Linear Regression Models ◽  
Optimal Linear ◽  
New Algorithms ◽  
Approximate Version ◽  
Strongly Polynomial

ABSTRACT Recent complexity-theoretic results on finding c-optimal designs over finite experimental domain X are discussed and their implications for the analysis of existing algorithms and for the construction of new algorithms are shown. Assuming some complexity-theoretic conjectures, we show that the approximate version of c-optimality does not have an efficient parallel implementation. Further, we study the question whether for finding the c-optimal designs over finite experimental domain X there exist a strongly polynomial algorithms and show relations between considered design problem and linear programming. Finally, we point out some complexity-theoretic properties of the SAC algorithm for c-optimality.

scholarly journals Bayesian Optimal Designs for Linear Regression Models

1991 ◽  
Vol 19 (4) ◽  
pp. 2183-2208 ◽  
Author(s):  
Sadi M. El-Krunz ◽  
W. J. Studden
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Optimal Designs ◽  
Linear Regression Models

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.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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