[PDF] Top 20 A Maximum Likelihood Approach to Least Absolute Deviation Regression
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A Maximum Likelihood Approach to Least Absolute Deviation Regression
... Least absolute deviation (LAD) regression is an important tool used in numerous applications throughout science and engineer- ing, mainly due to the intrinsic robust characteristics of ...LAD ... See full document
8
Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression
... quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum ...the maximum likeli- hood problem is equivalent to the solution of a maximum entropy ... See full document
41
Exact properties of the maximum likelihood estimator in exponential regression models: a differential geometry approach
... the maximum likelihood estimator ...exponential regression model is well-known to be of this type, and in this paper we apply the Hillier and Armstrong result to the MLE for this ... See full document
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Linear Maximum Likelihood Regression Analysis for Untransformed Log Normally Distributed Data
... linear regression models, based on moderate sample ...the absolute effect), but still take into account the log-normal distribution with a non-constant variance, we propose a maximum ... See full document
12
Robustness of maximum likelihood estimates for mixed Poisson regression models
... Gustafson (1996) used an influence function approach (Hampel et al., 1986) (Huber, 1981) to examine the robustness of maximum likelihood estimates for certain conjugate mixture models un[r] ... See full document
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On the equivalence between Total Least Squares and Maximum Likelihood PCA
... The maximum likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known ... See full document
14
Dealing Heteroscedasticity Problem in Regression Modeling Using ML-Fisher Scoring Algorithm: Simulation Study
... the regression model may not be accurate, and the hypothesis testing might be ...the maximum likelihood estimator using the fisher scoring approach, which is usually used to handle the ... See full document
7
Maximum likelihood estimators in linear regression models with Ornstein Uhlenbeck process
... linear regression models with non-Gaussian long memory moving average ...the regression parameters in a linear re- gression model with autoregressive ... See full document
31
Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation
... The MLLR approach requires an initial independent contin- uous density HMM system. MLLR takes some adaptation data from a speaker to adapt the acoustic models. MLLR updates the model mean parameters to maximize ... See full document
5
The Dual of the Maximum Likelihood
... The Maximum Likelihood method estimates the parameter values of a statistical model that maximizes the corresponding likelihood function, given the sample ...primal approach that, in this ... See full document
8
Least absolute deviation estimation of linear econometric models: A literature review
... linear regression model with random coefficient autocorrelated (RCA) ...by Least Absolute Deviation (LAD) ...the absolute value and not the squared value of the ... See full document
27
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression
... In reality, there are many data sets recorded by year, month, week, day, hour, minute, etc. For example, human growth is usually recorded by year, and temperature is recorded by hour, day or month. In this section, we ... See full document
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Evaluation of three methods for estimating the Weibull distribution parameters of Chinese pine (Pinus tabulaeformis )
... the regression method uses simple linear regression to estimate the parameters c and b of the Weibull func- tion, it may be an appropriate method for predicting a future ... See full document
6
A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models
... This procedure, long known to the and calculate a new value of p, pAO, profession as the Cochran-Orcutt iterative method, represents a convenient way of calculating the maximum likelihoo[r] ... See full document
12
The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... large deviation results for the least squares estimator of the nonlinear regression model based on the WOD error will be ...large deviation results based on widely orthant dependent ... See full document
11
Maximum likelihood estimation for stochastic processes - a martingale approach
... the likelihood function is a ...the likelihood and the maximum likelihood estimate (MLE), we can often use these limit theorems to prove asymptotic properties of the ... See full document
228
Ridge penalized logistical and ordinal partial least squares regression for predicting stroke deficit from infarct topography
... The maximum number of components from RPL-PLS is equal to the number of samples in the dataset. Since these components are sorted in a descending order ac- cording to the proportion of variance they explained, ... See full document
8
Estimation of the Reliability Measures of a Three component System with Human Errors and Common Cause Failures
... estimation approach which could give formal estimation procedure of the reliability measures with specific reference to CCS failures as well as human ...estimation approach is proposed to estimate the ... See full document
7
The Equivalence of the Maximum Likelihood and a Modified Least Squares for a Case of Generalized Linear Model
... Abstract: During the analysis of statistical data, one of the most important steps is the estimation of the considered parameters model. The most common estimation methods are the maximum likelihood and the ... See full document
5
A Maximum Likelihood Approach to Single-channel Source Separation
... thus least redundant. In constrast, correlation-based transformations such as principal component analysis (PCA) are based on dimensionality reduction. They search for the axis that has minimum correlations, which ... See full document
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