Invariance principle for estimates of regression coefficients of a random field

1982 ◽  
Vol 33 (6) ◽  
pp. 580-586
Author(s):  
N. N. Leonenko
Keyword(s):  
Random Field ◽  
Invariance Principle ◽  
Regression Coefficients

An invariance principle for estimating the correlation function of a homogeneous random field

1981 ◽  
Vol 32 (3) ◽  
pp. 213-219
Author(s):  
A. V. Ivanov ◽  
N. N. Leonenko
Keyword(s):  
Correlation Function ◽  
Random Field ◽  
Invariance Principle ◽  
Homogeneous Random Field

Estimates of linear regression coefficients on a homogeneous random field

1979 ◽  
Vol 30 (6) ◽  
pp. 562-568 ◽  
Author(s):  
N. N. Leonenko
Keyword(s):  
Linear Regression ◽  
Random Field ◽  
Regression Coefficients ◽  
Homogeneous Random Field

Invariance principle for a (BL, θ)-dependent random field

2003 ◽  
Vol 58 (3) ◽  
pp. 617-618 ◽  
Author(s):  
A P Shashkin
Keyword(s):  
Random Field ◽  
Invariance Principle

Invariance principle for estimating the correlation function of a homogeneous isotropic random field

1982 ◽  
Vol 33 (3) ◽  
pp. 241-249
Author(s):  
A. V. Ivanov ◽  
N. N. Leonenko
Keyword(s):  
Correlation Function ◽  
Random Field ◽  
Invariance Principle ◽  
Isotropic Random Field ◽  
Isotropic Random

Comparison of random field strengths in (1-x)SrTiO3-xBiFeO3 and (1-x)SrTiO3-xBaTiO3 relaxor ferroelectrics by means of acoustic emission

2020 ◽  
Vol 92 (2) ◽  
pp. 20401
Author(s):  
Evgeniy Dul'kin ◽  
Michael Roth
Keyword(s):  
Acoustic Emission ◽  
Random Field ◽  
Electric Fields ◽  
Bias Field ◽  
Relaxor Ferroelectrics ◽  
External Bias ◽  
Field Strengths

In relaxor (1-x)SrTiO3-xBiFeO3 ferroelectrics ceramics (x = 0.2, 0.3 and 0.4) both intermediate temperatures and Burns temperatures were successfully detected and their behavior were investigated in dependence on an external bias field using an acoustic emission. All these temperatures exhibit a non-trivial behavior, i.e. attain the minima at some threshold fields as a bias field enhances. It is established that the threshold fields decrease as x increases in (1-x)SrTiO3-xBiFeO3, as it previously observed in (1-x)SrTiO3-xBaTiO3 (E. Dul'kin, J. Zhai, M. Roth, Phys. Status Solidi B 252, 2079 (2015)). Based on the data of the threshold fields the mechanisms of arising of random electric fields are discussed and their strengths are compared in both these relaxor ferroelectrics.

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

Regression analysis of indicators of a numerical experiment to determine the cooling time of oil

2020 ◽  
pp. 89-97
Author(s):  
A. U. Yakupov ◽  
D. A. Cherentsov ◽  
K. S. Voronin ◽  
Yu. D. Zemenkov
Keyword(s):  
Regression Analysis ◽  
Computer Experiment ◽  
Calculation Model ◽  
Cooling Time ◽  
Regression Coefficients ◽  
Fisher Criterion ◽  
Empirical Relationships ◽  
Factors Affecting ◽  
Oil Pipeline ◽  
Significant Regression

The article performed the processing of the results of a computer experiment to determine the cooling time of oil in a stopped oil pipeline. We proposed a calculation model in previous works that allows you to simulate the process of cooling oil.There was a need to verify the previously obtained results when conducting a laboratory experiment on a stand with soil. To conduct the experiment, it was necessary to conduct the planning of the experiment. The factors affecting the cooling time of oil in the oil pipeline, which will vary in the proposed experiment, are determined, empirical relationships are established. A regression analysis was carried out, and the dispersion homogeneity was checked using the Cochren criterion. The estimates of reproducibility variances are calculated. The adequacy hypothesis was tested using the Fisher criterion. Significant regression coefficients are established.

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