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User1865345
User1865345
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Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * PawittanPawitan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawitan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

broken link fixed, cf. https://meta.mathoverflow.net/q/5301/70594
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Glorfindel
Glorfindel
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Some books on Likelihood Estimation

  • ** Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • ** Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • ** Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • ** Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • ** Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • ** Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • ** van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

deleted 3 characters in body
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Nick Cox
Nick Cox
  • 59.4k
  • 8
  • 136
  • 212

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * ButtlerButler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and CassellaCasella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigourous book, here you can find the mystical "regularity conditions".}}$$-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Buttler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Cassella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigourous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

Some books on Likelihood Estimation

  • * Amari, Barndorff-Nielsen, Kass, Lauritzen and Rao, Differential geometry in statistical inference. $-\small{\text{Geometrical approach for proving existence, uniqueness and other properties of MLE.}}$

  • * Butler, Saddlepoint Approximations with Applications.
    $-\small{\text{Saddlepoint approximations to the MLE on complicated models.}}$

  • * Cox, Principles of Statistical Inference.
    $-\small{\text{A basic reference on MLE.}}$

  • * Cox and Barndorff-Nielsen, Inference and Asymptotics. $-\small{\text{Likelihood, pseudo-likelihood, approximation theorems and asymptotics explained by}}$ $ \small{\text{two exponents in this area.}}$

  • * Edwards, Likelihood.
    $-\small{\text{A reference for a general discussion on this concept.}}$

  • * Ferguson, A Course in Large Sample Theory. $-\small{\text{Contains classical results on asymptotic properties of point estimators.}}$

  • * Kalbfleisch, Probability and Statistical Inference II. $\spadesuit$
    $-\small{\text{Introductory book containing interesting basic results such as the continuous }}$ $\small{\text{approximation to the likelihood which is not always explained.}}$

  • * Lehmann and Casella, Theory of Point Estimation.
    $-\small{\text{Classical results on point estimation, an essential reference.}}$

  • * Pace and Salvan, Principles of Statistical Inference: From a Neo-Fisherian Perspective. $-\small{\text{A good reference on a school of thought becoming more and more popular:}}$ $\small{\text{the Neo-Fisherian.}}$

  • * Pawittan, In All Likelihood: Statistical Modelling and Inference Using Likelihood.

  • * Serfling, Approximation Theorems of Mathematical Statistics. $-\small{\text{More rigorous book, here you can find the mystical "regularity conditions".}}$

  • * Severini, Likelihood Methods in Statistics.

  • * Shao, Mathematical Statistics.
    $-\small{\text{Classical results, good as a textbook.}}$

  • * Sprott, Statistical Inference in Science. $\spadesuit$ $-\small{\text{Basic reference on likelihood, profile likelihood and classical statistical modelling.}}$

  • * van der Vaart, Asymptotic Statistics.
    $-\small{\text{A general reference on: modes of convergence, properties of MLE, delta method,}}$ $\small{\text{ moment estimators, efficiency and tests.}}$

  • * Young and Smith, Essentials of Statistical Inference. $-\small{\text{A more recent book on: Likelihood, pseudolikelihood, saddlepoint approximations,}}$ $\small{p^*\text{ formula, modified profile likelihoods and more.}}$

$\spadesuit$ Suggestion for the OP

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