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Visar resultat 1 - 5 av 269 avhandlingar som matchar ovanstående sökkriterier.
1. Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables
Sammanfattning : 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. LÄS MER
2. Likelihood-Based Tests for Common and Idiosyncratic Unit Roots in the Exact Factor Model
Sammanfattning : 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. LÄS MER
3. Likelihood-Based Panel Unit Root Tests for Factor Models
Sammanfattning : 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. LÄS MER
4. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
Sammanfattning : 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. LÄS MER
5. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
Sammanfattning : 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. LÄS MER