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Exact and Approximate Distributions of the Maximum Likelihood Estimator of a Slope Coefficient

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This article is published in Journal of the royal statistical society series b-methodological.The article was published on 1982-09-01. It has received 35 citations till now.

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Estimating Linear Statistical Relationships

TL;DR: In this paper, a general approach to estimating linear statistical relationships is presented, which includes three lectures on linear functional and structural relationships, factor analysis, and simultaneous equations models, focusing on the similarity of maximum likelihood estimators under normality in the different models.
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Error analysis for circle fitting algorithms

TL;DR: A new algebraic (non-iterative) circle fitting algorithm is constructed that outperforms all the existing methods, including the (previously regarded as unbeatable) geometric fit.
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On Estimating Linear Relationships When Both Variables are Subject to Errors

TL;DR: In this article, the problem of estimating a linear relationship between two variables observed with errors is discussed, focusing on confidence intervals for the slope parameter and confidence regions for the intercept and slope parameters in Gaussian models.
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Measurement-methods comparisions and linear statistical relationship

TL;DR: In this paper, the linear statistical relationship model is used to compare two measurement procedures and the confidence interval for slope depends on sample size and information size, and the derived results provide the basis for designing and analyzing a comparison experiment.
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Monitoring linearity of measurement gauges

TL;DR: In this article, the authors developed a Shewhart chart for measuring the linearity between two measurement gauges to ensure consistency of measurements and provide evidence that the gauges are functioning properly.
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The Exact Finite-Sample Distribution of the Limited-Information Maximum Likelihood Estimator in the Case of Two Included Endogenous Variables

TL;DR: In this article, the exact finite-sample distribution of the limited-information maximum likelihood estimator when the structural equation being estimated contains two endogenous variables and is identifiable in a complete system of linear stochastic equations is derived.
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An Asymptotic Expansion of the Distribution of the Limited Information Maximum Likelihood Estimate of a Coefficient in a Simultaneous Equation System

TL;DR: In this article, an asymptotic expansion is made of the distribution of the limited information maximum likelihood estimate of the coefficient of one endogenous variable in an equation with two endogenous variables when the coefficients of the other endogenous variable is prescribed to be unity.
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