## Maximum Likelihood Estimation: Logic and Practice, Issue 96In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods. |

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### Contents

A General Modeling Framework Using Maximum | 21 |

An Introduction to Basic Estimation Techniques | 39 |

Further Empirical Examples | 46 |

Additional Likelihoods | 62 |

Conclusions | 68 |

Notes | 83 |