Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate

Metrologia ◽  
2011 ◽  
Vol 48 (5) ◽  
pp. 233-240 ◽  
Author(s):  
Clemens Elster ◽  
Blaza Toman
Keyword(s):  
Uncertainty Analysis ◽  
Regression Model ◽  
Least Squares ◽  
Least Squares Estimate ◽  
Model Versus ◽  
Bayesian Uncertainty Analysis

Rates of convergence for time series regression

1978 ◽  
Vol 10 (04) ◽  
pp. 740-743
Author(s):  
E. J. Hannan
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Least Squares ◽  
Continuous Spectrum ◽  
Rates Of Convergence ◽  
Absolutely Continuous Spectrum ◽  
Time Series Regression ◽  
Absolutely Continuous ◽  
Least Squares Estimate ◽  
Image Position

Consider, initially, a time series regression model of the simplest kind, namely Assume that x(t) is second-order stationary with zero mean and absolutely continuous spectrum with density f(ω) so that The y(t) are taken to be part of a sequence generated entirely independently of x(t) and will be treated as constants. Let β N be the least squares estimate of β and Call the numerator and denominator of b(N), respectively, c(N), d(N). We shall use K for a positive finite constant, not always the same one. We have the following result, which is Menchoff's inequality [3].

Rates of convergence for time series regression

1978 ◽  
Vol 10 (4) ◽  
pp. 740-743 ◽  
Author(s):  
E. J. Hannan
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Least Squares ◽  
Continuous Spectrum ◽  
Rates Of Convergence ◽  
Absolutely Continuous Spectrum ◽  
Time Series Regression ◽  
Absolutely Continuous ◽  
Least Squares Estimate ◽  
Image Position

Consider, initially, a time series regression model of the simplest kind, namely Assume that x(t) is second-order stationary with zero mean and absolutely continuous spectrum with density f(ω) so that The y(t) are taken to be part of a sequence generated entirely independently of x(t) and will be treated as constants. Let βN be the least squares estimate of β and Call the numerator and denominator of b(N), respectively, c(N), d(N). We shall use K for a positive finite constant, not always the same one. We have the following result, which is Menchoff's inequality [3].

A Least Squares Estimate of Satellite Attitude (Grace Wahba)

SIAM Review ◽  
1966 ◽  
Vol 8 (3) ◽  
pp. 384-386 ◽  
Author(s):  
J. L. Farrell ◽  
J. C. Stuelpnagel ◽  
R. H. Wessner ◽  
J. R. Velman ◽  
J. E. Brook
Keyword(s):  
Least Squares ◽  
Least Squares Estimate ◽  
Satellite Attitude

Bayesian uncertainty analysis of inversion models applied to the inference of thermal properties of walls

2021 ◽  
pp. 111188
Author(s):  
Séverine Demeyer ◽  
V. Le Sant ◽  
A. Koenen ◽  
N. Fischer ◽  
Julien Waeytens ◽  
...  
Keyword(s):  
Thermal Properties ◽  
Uncertainty Analysis ◽  
Bayesian Uncertainty Analysis
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