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Linear Statistical Inference and Its Applications.

C. A. Robertson, +1 more
- 01 Sep 1975 - 
- Vol. 31, Iss: 3, pp 791
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This article is published in Biometrics.The article was published on 1975-09-01. It has received 4122 citations till now. The article focuses on the topics: Statistical inference.

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Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
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Statistical analysis of cointegration vectors

TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
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Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models

TL;DR: For comments on an earlier draft of this chapter and for detailed advice I am indebted to Robert M. Hauser, Halliman H. Winsborough, Toni Richards, several anonymous reviewers, and the editor of this volume as discussed by the authors.
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Independent component analysis, a new concept?

Pierre Comon
- 01 Apr 1994 - 
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

TL;DR: The bootstrap is extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models.