Search for dissertations about: "maximum likelihood"

Showing result 1 - 5 of 268 swedish dissertations containing the words maximum likelihood.

  2. 1. Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables

    Author : Myrsini Katsikatsou; Fan Yang-Wallentin; Irini Moustaki; Karl Gustav Jöreskog; Ruggero Bellio; Uppsala universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; latent variable models; factor analysis; structural equation models; Thurstonian model; item response theory; composite likelihood estimation; pairwise likelihood estimation; maximum likelihood; weighted least squares; ordinal variables; ranking variables; lavaan; Statistics; Statistik;

    Abstract : The estimation of latent variable models with ordinal and continuous, or ranking variables is the research focus of this thesis. The existing estimation methods are discussed and a composite likelihood approach is developed. READ MORE

  4. 2. Likelihood-Based Tests for Common and Idiosyncratic Unit Roots in the Exact Factor Model

    Author : Martin Solberger; Rolf Larsson; Johan Lyhagen; Jean-Pieree Urbain; Uppsala universitet; []
    Keywords : panel unit root; dynamic factors; maximum likelihood; Lagrange multiplier; likelihood ratio; factor analysis; Statistics; Statistik;

    Abstract : Dynamic panel data models are widely used by econometricians to study over time the economics of, for example, people, firms, regions, or countries, by pooling information over the cross-section. Though much of the panel research concerns inference in stationary models, macroeconomic data such as GDP, prices, and interest rates are typically trending over time and require in one way or another a nonstationary analysis. READ MORE

  5. 3. Likelihood-Based Panel Unit Root Tests for Factor Models

    Author : Xingwu Zhou; Johan Lyhagen; Joakim Westerlund; Uppsala universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Panel unit root; Exact factor model; Dynamic factor model; Maximum likelihood; Principal components; Lagrange multiplier;

    Abstract : The thesis consists of four papers that address likelihood-based unit root tests for panel data with cross-sectional dependence arising from common factors.In the first three papers, we derive Lagrange multiplier (LM)-type tests for common and idiosyncratic unit roots in the exact factor models based on the likelihood function of the differenced data. READ MORE

  6. 4. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions

    Author : Mohamed Abdalmoaty; Håkan Hjalmarsson; Adrian Wills; KTH; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Prediction Error Method; Maximum Likelihood; Data-driven; Learning; Stochastic; Nonlinear; Dynamical Models; Non-stationary Linear Predictors; Intractable Likelihood; Latent Variable Models; Estimation; Process Disturbance; Electrical Engineering; Elektro- och systemteknik;

    Abstract : Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. READ MORE

  7. 5. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors

    Author : Mohamed Abdalmoaty; Håkan Hjalmarsson; Jimmy Olsson; KTH; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Stochastic Nonlinear Systems; Nonlinear System Identification; Learning Dynamical Models; Maximum Likelihood; Estimation; Process Disturbance; Prediction Error Method; Non-stationary Linear Predictors; Intractable Likelihood; Latent Variable Models; Electrical Engineering; Elektro- och systemteknik;

    Abstract : The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. READ MORE