Locally-Biased Regression

Tom Fearn; Anthony M.C. Davies

doi:10.1255/jnirs.397

PREDICTION PERFORMANCE OF THE NEW LINEAR CONVEX COMBINATION OF THE BIASED REGRESSION ESTIMATORS

10.21506/j.ponte.2016.5.23 ◽

2016 ◽

Vol 72 (5) ◽

Author(s):

Issam Dawoud ◽

Selahattin Kaciranlar

Keyword(s):

Convex Combination ◽

Prediction Performance ◽

Regression Estimators ◽

Linear Convex Combination ◽

Biased Regression

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A comparison of neural networks, non-linear biased regression and a genetic algorithm for dynamic model identification

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/0169-7439(95)00041-0 ◽

1995 ◽

Vol 30 (1) ◽

pp. 81-89 ◽

Cited By ~ 24

Author(s):

Barry M. Wise ◽

Bradley R. Holt ◽

Neal B. Gallagher ◽

Samuel Lee

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Dynamic Model ◽

Model Identification ◽

Non Linear ◽

Biased Regression

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Modelling dynamic systems with biased regression and spectral methods

Kybernetes ◽

10.1108/03684929510094271 ◽

1995 ◽

Vol 24 (6) ◽

pp. 38-43

Author(s):

Ralf Östermark

Keyword(s):

Dynamic Systems ◽

Spectral Methods ◽

Biased Regression

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Robust weights and designs for biased regression models: Least squares and generalized M-estimation

Journal of Statistical Planning and Inference ◽

10.1016/s0378-3758(99)00102-0 ◽

2000 ◽

Vol 83 (2) ◽

pp. 395-412 ◽

Cited By ~ 29

Author(s):

Douglas P. Wiens

Keyword(s):

Least Squares ◽

Regression Models ◽

M Estimation ◽

Biased Regression

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Composite forecasting of annual earnings: an application of biased regression techniques

Journal of Statistical Computation and Simulation ◽

10.1080/00949658608810886 ◽

1986 ◽

Vol 24 (1) ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

John B. Guerard ◽

Carl R. Beidleman

Keyword(s):

Regression Techniques ◽

Annual Earnings ◽

Biased Regression

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New approach in biased regression

Journal of Applied Statistics ◽

10.1080/02664769723396 ◽

1997 ◽

Vol 24 (6) ◽

pp. 647-658 ◽

Cited By ~ 2

Author(s):

E. M. Qannari ◽

E. Vigneau ◽

M. Semenou

Keyword(s):

New Approach ◽

Biased Regression

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Development and evaluation of an automated dye-binding assay for protein in cerebrospinal fluid.

Clinical Chemistry ◽

10.1093/clinchem/34.5.980 ◽

1988 ◽

Vol 34 (5) ◽

pp. 980-983 ◽

Cited By ~ 4

Author(s):

C M Huang

Keyword(s):

Cerebrospinal Fluid ◽

Absorption Spectrum ◽

Reaction Time ◽

Absorption Maximum ◽

Binding Assay ◽

Sodium Dodecyl ◽

Coomassie Brilliant Blue ◽

Hands On ◽

Coomassie Brilliant ◽

Biased Regression

Abstract Addition of sodium dodecyl sulfate (SDS) to Coomassie Brilliant Blue reagent equalizes the binding variability of the dye to various proteins and markedly improves the accuracy of quantification of protein in cerebrospinal fluid. In the presence of SDS, the absorption spectrum and the absorption maximum are affected by reaction time and temperature, age of the dye preparation, and protein constituent. I automated this procedure, to optimize precision, enable use of a smaller sample, decrease hands-on time, maintain consistency in the time of reading, and avoid carryover. The results (y) compared well with those of the aca (x), with a Deming de-biased regression equation of y = 0.991x + 14.1 mg/L, Sy.x = 33.4 mg/L. The within-run and between-run precision (CV) was less than 2.5% and less than 4.5%, respectively. Commonly used antibiotics, flucytosine, or amphotericin B do not interfere. This automated procedure is fast and accurate and requires only 10 microL of sample.

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