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Numerical algorithms

Christopher G. Small and Jinfang Wang

in Numerical Methods for Nonlinear Estimating Equations

Published in print:
2003
Published Online:
September 2007
ISBN:
9780198506881
eISBN:
9780191709258
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198506881.003.0003
Subject:
Mathematics, Probability / Statistics

This chapter surveys a variety of root-finding and hill-climbing algorithms that are useful for solving estimating equations or maximizing artificial likelihoods, starting with a basic technique ... More


Simulated Maximum Likelihood, Pseudo‐Maximum Likelihood, and Nonlinear Least Squares Methods

Christian Gouriéroux and Alain Monfort

in Simulation-based Econometric Methods

Published in print:
1997
Published Online:
November 2003
ISBN:
9780198774754
eISBN:
9780191596339
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/0198774753.003.0003
Subject:
Economics and Finance, Econometrics

The simulated analogues to Maximum Likelihood, Pseudo‐Maximum Likelihood, and Non‐Linear Least Squares Methods are presented. Their asymptotic properties and bias corrections are given under various ... More


Algorithms for estimating parametric nonlinear models

Timo Teräsvirta, Dag Tjøstheim, and W. J. Granger

in Modelling Nonlinear Economic Time Series

Published in print:
2010
Published Online:
May 2011
ISBN:
9780199587148
eISBN:
9780191595387
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199587148.003.0012
Subject:
Economics and Finance, Econometrics

This chapter contains an introduction to estimating parametric nonlinear models. Estimation has to be carried out using numerical algorithms. Both algorithms not using derivatives of the function to ... More


A primer on probabilistic inference

Thomas L. Griffiths and Alan Yuille

in The Probabilistic Mind:: Prospects for Bayesian cognitive science

Published in print:
2008
Published Online:
March 2012
ISBN:
9780199216093
eISBN:
9780191695971
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199216093.003.0002
Subject:
Psychology, Cognitive Psychology

This chapter provides the technical introduction to Bayesian methods. Probabilistic models of cognition are often referred to as Bayesian models, reflecting the central role that Bayesian inference ... More


Clustering and unsupervised classification

Peter Grindrod CBE

in Mathematical Underpinnings of Analytics: Theory and Applications

Published in print:
2014
Published Online:
March 2015
ISBN:
9780198725091
eISBN:
9780191792526
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198725091.003.0005
Subject:
Mathematics, Analysis, Probability / Statistics

This chapter examines iterative clustering methods including finite mixture modelling based on the EM algorithm. Applications to behavioural segmentation of domestic energy customers, the ... More


A Primer on Empirical Methodology

G. Andrew Karolyi

in Cracking the Emerging Markets Enigma

Published in print:
2015
Published Online:
June 2015
ISBN:
9780199336623
eISBN:
9780190232047
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199336623.003.0003
Subject:
Economics and Finance, International, Development, Growth, and Environmental

This chapter focuses on the empirical methodology—principal components analysis (PCA)—used to build each of the six risk indicators across the 33 emerging and 24 developed markets featured in the ... More


Missing data: mechanisms, methods, and messages

Shinichi Nakagawa

in Ecological Statistics: Contemporary theory and application

Published in print:
2015
Published Online:
April 2015
ISBN:
9780199672547
eISBN:
9780191796487
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199672547.003.0005
Subject:
Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology

Missing data are ubiquitous in ecological and evolutionary data sets as in any other branch of science. The common methods used to deal with missing data are to delete cases containing missing data, ... More


Network statistics and measurement error

Mark Newman

in Networks

Published in print:
2018
Published Online:
October 2018
ISBN:
9780198805090
eISBN:
9780191843235
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198805090.003.0009
Subject:
Physics, Theoretical, Computational, and Statistical Physics

This chapter introduces the mathematics of network statistics, the quantification of errors in network data, and the estimation of network structure in the presence of error. The discussion starts ... More


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