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Showing result 1 - 5 of 140 swedish dissertations matching the above criteria.

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. DSGE Model Estimation and Labor Market Dynamics

   Author : Glenn Mickelsson; Nils Gottfries; Karl Walentin; Martin M Andreasen; Uppsala universitet; []
   Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; DSGE Models; Macroeconomics; Estimation; Uninformative Priors; Maximum Likelihood; Labor Hoarding; US Labor Market; Swedish Micro Data; Economics; Nationalekonomi;

   Abstract : Essay 1: Estimation of DSGE Models with Uninformative PriorsDSGE models are typically estimated using Bayesian methods, but because prior information may be lacking, a number of papers have developed methods for estimation with less informative priors (diffuse priors). This paper takes this development one step further and suggests a method that allows full information maximum likelihood (FIML) estimation of a medium-sized DSGE model. READ MORE

5. 3. 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

6. 4. 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

7. 5. Maximum spacing methods and limit theorems for statistics based on spacings

   Author : Magnus Ekström; Umeå universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Estimation; spacings; maximum spacing method; consistency; ^-divergence; goodness of fit; unimodal density; entropy estimation; uniform distribution;

   Abstract : The maximum spacing (MSP) method, introduced by Cheng and Amin (1983) and independently by Ranneby (1984), is a general estimation method for continuous univariate distributions. The MSP method, which is closely related to the maximum likelihood (ML) method, can be derived from an approximation based on simple spacings of the Kullback-Leibler information. READ MORE