[PDF] Top 20 Asymptotic Efficiency of Maximum Likelihood Estimators Under Misspecified Models
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Asymptotic Efficiency of Maximum Likelihood Estimators Under Misspecified Models
... Maximum likelihood based procedures are quite predominant in classical statistical ...primarily asymptotic, the two key features being consistency and asymptotic efficiency under ... See full document
6
On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding
... Bridge estimators studied by Frank and Friedman (1993), least an- gle regression (LARS) of Efron, Hastie, Johnston, Tibshirani (2004), or the smoothly clipped absolute deviation (SCAD) estimator of Fan and Li ... See full document
34
Maximum likelihood estimators in linear regression models with Ornstein Uhlenbeck process
... the asymptotic normality of the Whittle estimator in linear regression models with non-Gaussian long memory moving average ...linear models with φ -mixing ... See full document
31
Distributions of Maximum Likelihood Estimators and Model Comparisons
... θ in terms of the underlying data, which is useful because the MLE can usually only be found by an iterative esti- mation routine. The formulae however differ from each other in that (2) eschews the use of an expectation ... See full document
7
On the asymptotic efficiency of approximate Bayesian computation estimators
... the efficiency of approximate Baysian computation, where by efficiency we mean that an estimator obtained from running Algorithm 1 has the same rate of convergence as the maximum likelihood ... See full document
15
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
... (i.e., estimators converge to the true parameter values as sample size tends to infinity if the model is true), because it yields the observed case-control ratio in ...pseudo likelihood or conditional like- ... See full document
30
Consistent Pseudo Maximum Likelihood Estimators and Groups of Transformations
... transformations, under appropriate regularity conditions, these PML estimators are asymptotically normal with asymptotic variance-covariance matrices obtained by the so-called sandwich ... See full document
25
Weighted Type of Quantile Regression and its Application
... nonlinear models such as exponential models and ARCH type of ...proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle ... See full document
5
Poisson qmle of count time series models
... certain maximum likelihood estimators (MLEs) can be consistent and asymptotically normal (CAN) for the parameters of the conditional mean and variance, even if the actual conditional distribution is ... See full document
33
Maximum likelihood estimation in possibly misspecified dynamic models with time inhomogeneous Markov Regimes
... in models with time-inhomogeneous Markov regimes involves the use of an incomplete approximation to the likelihood function which ignores the joint dependence of the observation variable and of the ... See full document
60
Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models
... Factor models have been widely used in ...factor models under large-N and large-T setup, where N denotes the number of cross sectional units and T the time ...quasi maximum likelihood ... See full document
93
Global self weighted and local quasi maximum exponential likelihood estimators for ARMA GARCH/IGARCH models
... quasi-maximum likelihood estimator (QMLE) was established by Ling and Li (1997) and by Francq and Zakoïan (2004) when Eε t 4 < ∞ ...the asymptotic normality of the QMLE were obtained by Lee and ... See full document
34
Asymptotic Comparison of Method of Moments Estimators and Maximum Likelihood Estimators of Parameters in Zero Inflated Poisson Model
... Zero-inflated models have found applications in situa- tions where excess number of zero observations are gen- erated. The application of the zero-inflated Poisson model by Lambert [1] in a count regression model ... See full document
7
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
... We conduct theoretic analysis on the maximum likelihood estimators (MLE), which are “asymp- totically optimum.” Both the sample median and geometric mean estimators are about 80% efficient as ... See full document
36
PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION
... In this section we carry out a simulation study to compare the performances of the MLEs and the Bayesian. The simulation is carried out for different choices of n, r and T values. For a particular set of hybrid censored ... See full document
19
IMPROVED ESTIMATION STRATEGIES IN MULTIVARIATE MULTIPLE REGRESSION MODELS
... The asymptotic distributional risk of the restricted least squares estimator is unbounded when the parameter moves far from the subspace of the restriction, while the pretest estimator provides good control on the ... See full document
140
On the comparison of the pre-test and shrinkage estimators for the univariate normal mean
... biased estimators: the restricted estimator (RE) with a coefficient of distrust, the preliminary test estimator (PTE) as a linear combination of the mle and the RE, and the shrinkage estimator (SE) by using the ... See full document
23
On the asymptotic efficiency of approximate Bayesian computation estimators
... The following lemmas are used for the result about the posterior mean of approximate Bayesian computation, proofs of these are given in Section 1·3.. Our first lemma is used to justify i[r] ... See full document
18
On the Maximum Likelihood and Least Squares Estimation for the Inverse Weibull Parameters with Progressively First Failure Censoring
... the maximum likelihood, approximate maximum likelihood and the least squares method estimators for the unknown pa- rameters of the inverse Weibull distribution are ...these ... See full document
16
Multiple Choice Tests: Inferences Based on Estimators of Maximum Likelihood
... The behaviour of Delta can be observed by means of simulations. Figure 1 shows the point estimation of the parameter ∆ across the entire range of knowledge in the case of MCT with three alternatives where each indi- ... See full document
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