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Statistical Inference in Linear Models.
Biometrics, 1987Statistical problems in modelling causal relationships estimating linear parameters estimating linear parameters using additional information admissibility and improvements of the generalized least squares estimator testing linear hypotheses confidence regions for linear parameters and regression functions Bayesian methods and structural inference ...
Eric Ziegel, H. Bunke, O. Bunke
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Applied Linear Statistical Models
Journal of Quality Technology, 1997(1997). Applied Linear Statistical Models. Journal of Quality Technology: Vol. 29, No. 2, pp. 233-233.
Eric R. Ziegel +4 more
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1994
Linear models form the core of classical statistics and are still the basis of much of statistical practice; many modern modelling and analytical techniques build on the methodology developed for linear models.
W. N. Venables, B. D. Ripley
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Linear models form the core of classical statistics and are still the basis of much of statistical practice; many modern modelling and analytical techniques build on the methodology developed for linear models.
W. N. Venables, B. D. Ripley
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Applied Linear Statistical Models.
Journal of the Royal Statistical Society. Series A (General), 1975This text uses an applied approach, with an emphasis on the understanding of concepts and exposition by means of examples. Sufficient theoretical information is provided to enable applications of regression analysis to be carried out. Case studies are used to illustrate many of the statistical methods.
V. Barnett +2 more
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A Piecewise Linear Approximation Based on a Statistical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984A statistical model is introduced and then, based on it, a piecewise linear approximation algorithm of linear computational complexity is presented. The advantages of the algorithm are proved experimentally in small sample cases and theoretically in the large sample case. The paper is closed with a discussion on some possible extensions.
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1970
We now come to the application of the notions and results, which were given in paragraph 2. We first introduce the concept of a linear statistical model.
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We now come to the application of the notions and results, which were given in paragraph 2. We first introduce the concept of a linear statistical model.
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A linear model of intermediate statistics
Journal of Physics A: Mathematical and General, 1997Approximate Hamiltonians for the one-dimensional Calogero and two-dimensional anyon models in a harmonic well are constructed. In both models the particles interpolate between bosons and fermions. The article focuses on the remarkable properties of the solution of the Calogero model to first order in perturbation theory.
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Statistical Analysis and Linear Models
2020In Chapter 3, we explored the graph and map tasks. In Chapter 4, we will explore statistics and linear models.
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Statistical Inference in Linear Models
Technometrics, 1988Statistical problems in modelling causal relationships estimating linear parameters estimating linear parameters using additional information admissibility and improvements of the generalized least squares estimator testing linear hypotheses confidence regions for linear parameters and regression functions Bayesian methods and structural inference ...
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