M-Estimators
Robust M-Estimators of the Population Mean.
... There are many cases in real life when a researcher encounters data that may have some outlying observations that cannot be done away with or that cannot simply be replaced with others for the sake of coming up with an ...
63
Robust regression trees based on M-estimators
... an M-estimator of the location parameter θ obtained using a bounded and odd ( ) ψ ⋅ function is still equal to ½, while the situation is more complex for redescending M-estimators with a bounded ρ ( ...
18
Regularized M-estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima
... Our current paper is framed in a more general setting, and we focus on various M - estimators coupled with (nonconvex) regularizers of interest. On the statistical side, we establish bounds on the distance ...
58
Bahadur representations of M estimators and their applications in general linear models
... the m-dependent sequence satisfies condi- tions (A6) and (A7) and secondly obtain the asymptotic normal distribution and strong convergence rates for M-estimators of the ...of ...
32
M Estimators of Roughness and Scale for Modelled SAR Imagery
... These estimators offer a useful alternative when small propor- tions of values may be far from the bulk of ...these estimators and their use in practical situations can be seen in ...likelihood ...
10
A forward regression algorithm based on M estimators
... This paper introduces an orthogonal forward regression (OFR) model structure selection algorithm based on the M- estimators. The basic idea of the proposed approach is to incorporate an IRLS inner loop into ...
5
The calculus of M-estimation
... M-estimators represent a very large class of statistics, including for example, maximum likelihood estimators and basic sample statistics like sample moments and sample quantiles as well as com- plex ...
30
Robust Inference for Time Varying Coefficient Models with Longitudinal Data
... local M-estimation method and the robustified GLR ...local M-estimators are robust against outliers and error distributions, and the proposed robustified GLR test is more powerful than its ...
12
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
... SGD-based estimators have been well established, statistical inference such as interval estimation remains much ...models, M-estimators and quantile regressions as special ...
21
Lower Bounds and Aggregation in Density Estimation
... solved by using a kind of cross-validation procedure. Namely, the aggregation is based on splitting the sample in two independent subsamples D 1 m and D 2 l of sizes m and l respectively, where m ≫ l ...
11
Correcting data for measurement error in generalized linear models
... As all of the bias-adjusted estimators are modified M-estimators or what might be called pseudo M-estimators, a routine though tedious application of the delta method yields standard lar[r] ...
20
A COMPARATIVE STUDY OF SOME ROBUST RIDGE AND LIU ESTIMATORS
... robust M-estimator for ridge regression to handle the problem of multicollinearity and ...Generalized M estimators instead of M ...Generalized M estimator with fully iterated ridge ...
5
New Robust MEWMA Control Chart for Monitoring Contaminated Data
... Abstract: Multivariate Exponential Weighted Moving Average (MEWMA), E 2 control chart is a popular multivariate control chart for monitoring the stability of time series data (non-random pattern). However, in this paper, ...
8
Combined estimators as alternative to multicollinearity estimation methods
... combined estimators use the FGLS estimators, Cochrane and Orchutt (CORC) estimator (1949) and the Maximum Likelihood (ML) estimator (Beach and Mackinnon, 1978), by regressing the extracted components (PCs) ...
6
On comparison some estimators in small area study
... these estimators have similar efficiency as direct estimators but with smaller absolute average ...composite estimators have heavier weight on direct estimator ...
6
LQ-Moments: application to the log-normal distribution
... The RMSE and BIAS of quantile estimators for the LN3 distribution for different sample size and T = 100- year return period are compared and shown in Table 2. The results are quite similar. For sample size, n < ...
8
Joint Distribution of Minimum of N Iid Exponential Random Variables and Poisson Marginal
... We introduced a random vector , where has Poisson distribution and are minimum of independent and identically distributed exponential random variables. We present fundamental properties of this vector such as PDF, CDF ...
7
Mean Estimation under Imputation based on Two-Phase Sampling Design using an Auxiliary Variable
... proposed estimators being more efficient, under each design as compared with ratio, product estimator, exponential ratio, exponential product, combined ratio and combined product estimators are shown in ...
20
Bayesian Estimation Of Shift Point In Poisson Model Under Asymmetric Loss Functions
... Bayes estimators of and shift point ‘m’ are calculated under Squared error loss function, Linex loss function, Precautionary loss function and General entropy loss function by making programs in ...Bayes ...
12
Empirical Bayes Estimation in Cross-Classified Gaussian Models With Unbalanced Design
... An advantage of the empirical Bayes approach is that in using the data to estimate the hyperparameter, one can appeal to frequentist considerations. In other words, use the Bayesian formalism to obtain a parametric ...
99