Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 23 for “"Maximum likelihood estimator (MLE)"”.
-
Estimation in random field models for noisy spatial data
… random field. Large sample properties of the Maximum Likelihood Estimator (MLE) of an Onrstein-Uhlenbeck process model with measurement error are studied. The effect caused by adding measurement error, or ""nugget,"" is revealed by the fixed region asymptotics of the MLE. The kriging predictor …
-
Modelling long-term security returns
… and evaluate returns on common stocks using the Maximum Likelihood Estimator (MLE), assuming that daily log returns follow a normal distribution. Additionally, the Merton Jump Diffusion (MJD) model is considered to account for jumps in stock trajectories with an independent Poisson process term …
-
The goodness-of-fit tests for geometric models
… of the standardized difference between the PGF's maximum likelihood estimator (MLE) and its empirical counterpart as the test statistic. We verify the asymptotic properties of the test statistics for the first type of test and explore the asymptotic behaviors of the test statistics for the second …
-
Sparse functional regression models: minimax rates and contamination
… "generalized Hurst exponent". The least squares estimator (LSE) is shown to attain the optimal rate. Also, a lower bound is given on the minimax risk of estimating the parameters in sparse functional GLM, which also depends on the generalized Hurst exponent of the predictor process. The order of …
-
Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters
… predictions describe the simulations of the maximum likelihood estimator (MLE) well, including the mean square error in all SNR regions as well as the bias at high SNR. The threshold SNR and bias predictions are also verified by the SWellEX experimental data processing. These developments …
-
Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters
… predictions describe the simulations of the maximum likelihood estimator (MLE) well, including the mean square error in all SNR regions as well as the bias at high SNR. The threshold SNR and bias predictions are also verified by the SWellEX experimental data processing. These developments …
-
Essays On Spatial Econometrics: Estimation Methods And Applications
… in the innovations. We first prove that the maximum likelihood estimator (MLE) is generally inconsistent when heteroskedasticity is not taken into account in the estimation. We show that the necessary condition for consistency of the MLE depends on the specification of the spatial weight …
-
On The Performance Of The Maximum Likelihood Over Large Models
… the statistical performance of Least Squares Estimator (LSE) --- which also serves as the Maximum Likelihood Estimator (MLE) under Gaussian noise --- over these classes. (1) We demonstrate the minimax sub-optimality of the LSE in the non-Donsker regime, extending traditional findings of over …
-
Position and vibration control of flexible space robots
… difficulties in implementing a Kalman filter, a Maximum Likelihood Estimator (MLE) is proposed. A numerical example illustrates the approach.
-
A Framework for Temperature Imaging using the Change in Backscattered Ultrasonic Signals
… based on the joint distributions. Furthermore, a maximum likelihood estimator: MLE) was developed. Both simulations and experimental results showed that noise effects were reduced by signal averaging. The motion compensation algorithms proved to be able to compensate for motion in images and were …
-
Tail estimation of the spectral density under fixed-domain asymptotics
… Therefore, spatial domain methodologies like Maximum Likelihood Estimator (MLE) or Tapering MLE can be used for the estimation of $c$ and $theta$. Unfortunately, the exact form of $f$ should be unknown in practice. Under this situation, spatial domain methods will not be applied without the …
-
Some applications of minimum norm quadratic estimation and eigenvalue-based test for heteroskedasticity
… estimation (MINQUE) to obtain an alternative estimator of variance-covariance matrix in heteroskedastic models. We derive the analytical expressions for the mean square errors (MSE) of White's (1980), MacKinnon and White's (1985) and MINQU estimators, and perform a numerical comparison. Our …
-
Target localization in passive and active systems : performance bonds
… parameters. To this end, lower bounds on the maximum likelihood estimator (MLE) performance are studied. The Cramer-Rao lower bound (CRLB) for coherent passive localization of a near-field source is derived. It is shown through the Cramer-Rao bound that, the coherent localization systems can …
-
Learning joint latent representations for images and language
… an explicit shared latent embedding space with a maximum-margin ranking loss and novel neighborhood constraints. The second network structure, referred to as a similarity network, fuses the two branches via element-wise product and is trained with regression loss to directly predict a similarity …
-
A modulated renewal Hawkes process and its application to modelling extreme mid-price drops on cryptocurrencies
… explicit expression for the intensity process, likelihood evaluation for the modulated renewal Hawkes process model is not trivial. However, by modifying the likelihood evaluation algorithm for renewal Hawkes process in Chen & Stindl (2018), we are able to propose an algorithm to evaluate the …
-
Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… them, the spatial rank based covariance matrix estimator that utilizes a robust scale estimator (MRCM) is especially appealing due to its high robustness, computational ease and good efficiency. In this dissertation, properties of the estimator on orthogonal equivariance under any distribution …
-
Essays On Robust Estimators For Non-Identically Distributed Observations In Spatial Econometric And Time Series Models
… robust generalized method of moments estimator (RGMME) for the spatial models that allow for spatial dependence in both the dependent variable and the disturbance term (SARAR(1,1)). First, we show that the maximum likelihood estimator (MLE) is generally inconsistent in the presence of …
-
Essays on testing spatial models
… lag, a time lag and a spatial-time lag. The maximum likelihood estimator for the estimation of SDPD models can have asymptotic bias because of individual and time fixed effects. Bias arises since the limiting distributions of the score functions derived from the corresponding concentrated …
-
Essays on Econometrics and Policy Evaluation
… a Synthetic Instrumental Variables (SIV) estimator for panel data that combines the strengths of instrumental variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV …
-
Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
… input model parameter uncertainties based on Maximum Likelihood Estimation (MLE), Bayesian Maximum A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithm for physical models using relevant experimental data. The objective of the present work is to perform the sensitivity analysis of the …
Page 1 of 2