Search for dissertations about: "Linear estimator"

Showing result 1 - 5 of 106 swedish dissertations containing the words Linear estimator.

  2. 1. On Estimation and Prediction in Linear Mixed Models : A new approach to studying equal BLUEs and BLUPs

    Author : Azadeh Chizarifard; Tatjana von Rosen; Katarzyna Filipiak; Stockholms universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Best linear unbiased estimator; Best linear unbiased predictor; Linear zero function; Matrix equations; statistik; Statistics;

    Abstract : Linear mixed models (LMMs) are widely used to analyze repeated, longitudinal, or clustered data in many disciplines, such as biology, medicine, psychology, sociology, economics, etc. One of the essential components of a linear mixed model is its covariance structure, i.e. READ MORE

  4. 2. Calibration Adjustment for Nonresponse in Sample Surveys

    Author : Bernardo João Rota; Thomas Laitila; Sune Karlsson; Risto Lehtonen; Örebro universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Auxiliary variables; Calibration; Nonresponse; principal com-ponents; regression estimator; response probability; survey sampling; two-step estimator; variance estimator; weighting; Statistics; Statistik;

    Abstract : In this thesis, we discuss calibration estimation in the presence of nonresponse with a focus on the linear calibration estimator and the propensity calibration estimator, along with the use of different levels of auxiliary information, that is, sample and population levels. This is a fourpapers- based thesis, two of which discuss estimation in two steps. READ MORE

  5. 3. Fundamental Estimation and Detection Limits in Linear Non-Gaussian Systems

    Author : Gustaf Hendeby; Fredrik Gustafsson; Niclas Bergman; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Estimation; Detection; Linear systems; Non-Gaussian; Limits; GLR; CRLB; Automatic control; Reglerteknik;

    Abstract : Many methods used for estimation and detection consider only the mean and variance of the involved noise instead of the full noise descriptions. One reason for this is that the mathematics is often considerably simplified this way. READ MORE

  6. 4. Massive Multi-Antenna Communications with Low-Resolution Data Converters

    Author : Sven Jacobsson; Chalmers tekniska högskola; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; orthogonal frequency-division multiplexing; channel estimation; quantization; linear precoding; convex optimization; nonlinear precoding; linear combing; hardware impairments; analog-to-digital converter; beamforming; Massive multi-user multiple-input multiple-output; digital-to-analog converter;

    Abstract : Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in future cellular communication systems. In massive MU-MIMO systems, the number of antennas at the base station (BS) is scaled up by several orders of magnitude compared to traditional multi-antenna systems with the goals of enabling large gains in capacity and energy efficiency. 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