Estimating a linear functional relationship under linear constraints for errors

Juha Lappi and Risto Sievänen

Applied Stochastic Models and Data Analysis, 1993, vol. 9, issue 4, 335-340

Abstract: Maximum likelihood estimates for parameters in a linear functional relationship are derived when the errors are also linearly related for each observation. This is approximately the case, for example, when both variables are smooth functions of time and their values are recorded as an experimental unit reaches a certain state. This kind of model specification was needed to describe how the timing of growth cessation of trees depends on night length and temperature sum. If the slope in the constraint equation for errors varies, then an iterative estimation procedure is needed. The estimation method is extended for a two‐phase linear model.

Date: 1993
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https://doi.org/10.1002/asm.3150090405

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmda:v:9:y:1993:i:4:p:335-340

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