[PDF] Top 20 Robust identification for linear in the parameters models
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Robust identification for linear in the parameters models
... Alternatively the PRESS statistic of (9) that op- timises model generalation capability can be used as a robust model selective criterion. Note that (8) does not incorporate parameter regularisation. In order to ... See full document
6
Estimation and identification for vector linear time series models
... regularity conditions, a sequence of iterates that are strongly consistent and asymptotically efficient, and that converge for N sufficiently large, almost surely, to the Gaussian Estimate. Thus we can obtain ... See full document
294
Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?
... parameter identification, whose effect on model predictions is still not fully ...the identification of body landmark positions, maximum muscle tension and musculotendon ...model parameters according ... See full document
10
Comparing linear and neural models for competitive MWE identification
... In the closed track, MLP ranks first for 11 languages, while the SVM model and the best ST.1 systems per language reach the first position respectively for three and five languages. In open track, MLP achieves the ... See full document
11
Essays on Robust Model Selection and Model Averaging for Linear Models
... that there exist certain scenarios where the lasso is inconsistent for variable se- lection. Thus, he suggested the adaptive lasso, where adaptive weights are used for penalizing coefficients differently in the L1 ... See full document
136
Robust Linear Regression Models: Use of a Stable Distribution for the Response Data
... both models versus ...very robust to the presence of the outlier given similar inference results as obtained without the presence of the outlier (see results in Table ...regression parameters with ... See full document
8
An Overview on Speaker Identification Technologies
... time-dependent parameters are observation ...then models were built using the broad phonetic category (BPC) and the HMM-based maximum likelihood linear regression (MLLR) adaptation ... See full document
10
Robust linear static panel data models using epsilon contamination
... the parameters, which is an essential requirement for the implementation of classical Bayes ...The robust Bayesian approach relies upon a class of prior distributions and selects an appropriate prior in a ... See full document
74
On the robust estimation of small failure probabilities for strong non-linear models
... In case dependent random model parameters are considered, the com- putation of the failure probability is usually performed in standard normal space (SNS). Due to the interval-valued uncertainty that is attributed ... See full document
32
Identification and Development of User Requirements to Support Robust Corridor Investment Models
... Stakeholders identified origins and destinations (O/D) and the associated travel distances/travel times as impacting several of the indicators discussed previously. Origins and destinations indicate those locations that ... See full document
82
Robust control of Non Linear Parameters of Paper machine Headbox
... mathematical models used for design and actual system. The following robust control program is developed for controlling stock level, air pressure and overflow parameters of headbox of paper making ... See full document
6
Identification robust inference for endogeneity parameters in linear structural models
... on parameters which represent the dependence between possibly endogenous explanatory variables and distur- bances in a linear structural equation (endogeneity ...endogeneity parameters. Such ... See full document
33
Gaussian Mixture Models for Human Face Recognition under Illumination Variations
... face identification under illu- mination variations is also well-researched ...develop identification me- thods that are robust to these ...shape parameters from a 3D face model, while [13] ... See full document
9
Testing Linearity in Term Structures
... An important problem with this data involves the measurement of the short-end of the TS. The instantaneous rate of interest (or short rate) plays the role of state variables in single-factor TS models (see Section ... See full document
28
Gain Scheduling Dual Mode MPC for a Solar Thermal Power Plant
... Solar energy can be converted by thermal means into electrical energy using concentrated solar power (CSP) technology Goswami et al. (2000). The application of CSP technology is expected to have a major role in long-term ... See full document
7
Mathematical models for prediction of rheological parameters in vinasses derived from sugar cane
... Knowledge of rheological properties of vinasses is very important, because it can be used for different engineering applications such as equipment design, designing of trans- port system, pump capacity design, and power ... See full document
7
Inference for generalised linear mixed models with sparse structure
... denser models, where there is more information available per random effect, the Laplace approximation to the shape of the likelihood appears to be sufficiently good to give accurate inference, even in cases where ... See full document
116
Identification, Estimation and Specification in a Class of Semi Linear Time Series Models
... Various semiparametric regression models have been proposed and discussed extensively in recent years. Primary interest focuses on general nonparametric and semiparametric time series models under ... See full document
21
An improved two-step method in stochastic differential equation's structural parameter estimation
... the parameters of SDE with likelihood approach, we opt to take fully non-likelihood approach by applying the two-step method used in estimating parameters of SDE with non-parametric ...SDE parameters ... See full document
30
Improved estimation of surface biophysical parameters through inversion of linear BRDF models
... model parameters, M, is two or three, the variance-covariance matrix is trivial (and very quick) to ...why linear models have been favoured for many applications, as will be seen in later ...model ... See full document
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