Statistical inference for nonlinear regression models
A. Ronald Gallant
- 01 Jan 1971
About: The article was published on 01 Jan 1971. and is currently open access. The article focuses on the topics: Frequentist inference & Fiducial inference.
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Citations
Fitting Segmented Polynomial Regression Models Whose Join Points Have to Be Estimated
A. R. Gallant,Wayne A. Fuller +1 more
TL;DR: In this paper, the authors considered the problem of finding the least squares estimates for the unknown parameters of a regression model which consists of grafted polynomial submodels and showed how continuity and differentiability conditions on the model can be used to reparameterize the model so as to allow Modified Gauss-Newton fitting.
260
Testing a Subset of the Parameters of a Nonlinear Regression Model
TL;DR: In this paper, the location of a subset of the parameters entering the response function of a nonlinear regression model is considered and two test statistics, the likelihood ratio test statistic and a test statistic derived from the asymptotic normality of the least squares estimator, are discussed and compared.
The Power of the Likelihood Ratio Test of Location in Nonlinear Regression Models
TL;DR: In this paper, the Likelihood Ratio Test statistic, T, is considered for the hypothesis H: θ = θ0 against A: ξ ≠ ξ 0 in the nonlinear regression model y = f(x, θ) + e with normal errors and unknown variance.
References
•Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
- 01 Jan 1966
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
19K
•Book
Linear statistical inference and its applications
Calyampudi Radhakrishna Rao
- 01 Jan 1965
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
10.1K
•Book
An Introduction to Multivariate Statistical Analysis
T. W. Anderson
- 14 Sep 1984
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
9.7K
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