Sökning: "ML-estimation"

Visar resultat 1 - 5 av 8 avhandlingar innehållade ordet ML-estimation.

1. 1. Model Selection and Sparse Modeling

   Författare :Yngve Selén; Peter Stoica; Jean-Jacques Fuchs; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; model selection; model order selection; model averaging; nested models; sparse models; Bayesian inference; MMSE estimation; MAP estimation; ML estimation; AIC; BIC; GIC; RAKE receivers; pulse compression; radar; linear models; linear regression models; Signal processing; Signalbehandling;

   Sammanfattning : Parametric signal models are used in a multitude of signal processing applications. This thesis deals with signals for which there are many candidate models, and it is not a priori known which model is the most appropriate one. LÄS MER

3. 2. On-Site Sampling in Economic Valuation Studies

   Författare :Termeh Shafie; Thomas Laitila; Stockholms universitet; []
   Nyckelord :Sample inclusion; Length-bias; Poisson dis- tribution; ML-estimation; Bivariate ordered probit; CVM.; Statistics; statistik;

   Sammanfattning : A commonly used sampling design in economic valuation studies is on-sitesampling. If this sampling design is used, the sampling inclusion probabil-ities may be correlated with respondents’ valuations, invalidating welfaremeasures derived from estimates of the probit model. LÄS MER

4. 3. On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space Models

   Författare :Jimmy Olsson; Matematisk statistik; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; actuarial mathematics; programming; operations research; Statistics; Matematik; Mathematics; state space models; smoothing; sequential Monte Carlo; particle filter; EM algorithm; maximum likelihood; consistency; Asymptotic normality; Statistik; operationsanalys; programmering; aktuariematematik;

   Sammanfattning : This thesis is based on four papers (A-D) treating filtering, smoothing, and maximum likelihood (ML) estimation in general state space models using stochastic particle filters (also referred to as sequential Monte Carlo (SMC) methods). The aim of Paper A is to study the bias of Monte Carlo integration estimates produced by the so-called bootstrap particle filter. LÄS MER

5. 4. Efficient Algorithms for Probabilistic Inference, Combinatorial Optimization and the Discovery of Causal Structure from Data

   Författare :Dag Wedelin; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : In the first article we present a network based algorithm for probabilistic inference in an undirected structure. We show that the algorithm can be used as a general purpose approximation algorithm for combinatorial optimization, and discuss issues of approximation and convergence. LÄS MER

7. 5. Estimation and optimal designs for multi-response Emax models

   Författare :Bergrún Tinna Magnúsdóttir; Hans Nyquist; Ziad Taib; Nancy Flournoy; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; multi-response Emax models; Clinical Utility Index CUI ; optimal designs; system estimation; dose-response studies.; Statistics; statistik;

   Sammanfattning : This thesis concerns optimal designs and estimation approaches for a class of nonlinear dose response models, namely multi-response Emax models. These models describe the relationship between the dose of a drug and two or more efficacy and/or safety variables. LÄS MER