Maximum Likelihood Dynamic Factor Modeling for ArbitraryNandTUsing SEM
نویسندگان
چکیده
منابع مشابه
Maximum Likelihood Dynamic Factor Modeling for Arbitrary N and T Using SEM
This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N D 1) and conventional...
متن کاملConstrained Maximum Likelihood Modeling with Gaussian Distributions
Maximum Likelihood (ML) modeling of multiclass data using gaussian distributions for classification often suffers from the following problems: a) data insufficiency implying overtrained or unreliable models b) large storage requirement c) large computational requirement and/or d) ML is not discriminating between classes. Sharing parameters across classes (or constraining the parameters) clearly...
متن کاملMaximum Likelihood Parallel Factor Analysis (mlparafac)
% " " "$ " & $" "$ " "$ " " ' " " " " ( ) "$ * $ " " + " " " " " " ( , & . $ / 0 " " ' "$ $ ' " ' " "$ * ' "$ " " #" '" 1 "$ * ' " " ( ) " " " 2 ' " ( 3 " " "$ ' "$ * " ' " " ' " " " '" " ( ) "$ 4 " / 0 " $ " "$ " " " " '" 5 6 " 6 " $ " & ' "$ " " ' "$ & $ " ' " " $"" ( 3 7 " 8 " " " ' ! " " " ( 9" "$ " " " ' & "$ " ( 3 "$ " " " " ' ' ' "$ ' ( : "$ ' & ' ' " # " ( , & ! " ; < ! "$ " " $ " !( , ...
متن کاملMaximum Likelihood and the Bootstrap for Nonlinear Dynamic Models
The bootstrap is an increasingly popular method for performing statistical inference. This paper provides the theoretical foundation for using the bootstrap as a valid tool of inference for quasimaximum likelihood estimators (QMLE). We provide a unified framework for analyzing bootstrapped extremum estimators of nonlinear dynamic models for heterogeneous dependent stochastic processes. We apply...
متن کاملMaximum likelihood modeling with Gaussian distributions for classification
Maximum Likelihood (ML) modeling of multiclass data for classi cation often su ers from the following problems: a) data insu ciency implying overtrained or unreliable models b) large storage requirement c) large computational requirement and/or d) ML is not discriminating between classes. Sharing parameters across classes (or constraining the parameters) clearly tends to alleviate the rst three...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Structural Equation Modeling: A Multidisciplinary Journal
سال: 2012
ISSN: 1070-5511,1532-8007
DOI: 10.1080/10705511.2012.687656