Abstract
Let \( \{ ({x'_i},{y_i})\} _{i = 1}^N \) be an iid sample drawn from a known distribution F(x i,y i, ß), where ß is a k × 1 vector of unknown parameters. Let f y|x (y, β) denote the likelihood function of y | x, which is the density function of y | x if y |x is continuous or the probability of y | x if y | x is discrete. Define f x (x) analogously, which is not a function of ß.
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© 1996 Springer Science+Business Media New York
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Lee, Mj. (1996). Maximum Likelihood Estimation. In: Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2550-6_4
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DOI: https://doi.org/10.1007/978-1-4757-2550-6_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-2552-0
Online ISBN: 978-1-4757-2550-6
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