Are the pooled estimation results robust?
Robust Bayesian Estimation
... are robust against outliers, where we define an outlier to be an observation which is unlikely to have been generated by the assumed sampling ...a robust sampling ...a robust model has to be adopted? ...
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Robust estimation of biometric data
... In lieu of the synthetic data portraying as actual facial data, the PD-enforcing scheme has not been utilised. This has four reasons: 1. Facial data often contains pixels that are lin- early dependent on their neighbour ...
11
Model-Robust Interval Estimation
... interval estimation, length-optimal confidence intervals, which is a general methodology for modifying score and likelihood-type confidence intervals that yields intervals with shorter ...theoretical ...
125
Robust small area estimation
... 1. Robust Small Area Estimation for the Fay-Herriot Model Small area estimation has long been a popular and important research topic in survey ...ter estimation, study its statistical ...
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Robust estimation of (partial) autocorrelation
... In time series we often face distributions with tails heavier than the Gaussian (Davis and Resnick, 1986; Loretan and Phillips, 1994; Politis, 2009; Rojo, 2013). Estimators should remain reliable in case of such ...
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Robust estimation for ordinal regression.
... To the best of our knowledge, this is the first paper where robustness for ordinal regression is studied. First we study the robustness of the classical Maximum Likelihood Estimator, and show that the slope estimator is ...
35
Robust Region-of-Attraction Estimation
... Fig. 3. Closed-loop system with the uncertain first-order dynamics between the controller and the plant ( = ( ; )). Here, 3 2 [01; 1] and 4 2 [10 02 ; 10 2 ] are uncertain parameters and G(s; 3 ; 4 ) is ...
6
Robust designs for series estimation
... title Robust Designs for Series Estimation Abstract We discuss optimal design problems for a popular method of series estimation in regression ...constructing robust and efficient designs for ...
22
Robust Kernel Density Estimation
... We remark that the definition of the RKDE does not require that k σ be a reproducing kernel, only that the functions Φ(x) = k σ ( · , x) belong to a Hilbert space. Indeed, almost all of the results presented below ...
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Notes on Regularization and Robust Estimation
... k + 1]) which is identical to Eq. 5. Robust formulations typically result in nonconvex optimization problems. To find a globally optimal solution when the objective function is nonconvex we choose a ro- bust ...
14
Robust designs for series estimation
... some results on the method of direct estimation, and present two motivating examples which arise from applications in shape and image ...are robust with respect to the model assumptions, in ...
21
Diversification with idiosyncratic credit spreads: a pooled estimation on heterogeneous panels
... As a robustness check against the above results, we have added an arbitrary approach to contrast them. The arbitrary scheme in column of Table 4 adopts arbitrarily a value of 1.5 as the long run coefficient for SP ...
27
Diversification with idiosyncratic credit spreads: a pooled estimation on heterogeneous panels
... As a robustness check against the above results, we have added an arbitrary approach to contrast them. The arbitrary scheme in column of Table 4 adopts arbitrarily a value of 1.5 as the long run coefficient for SP ...
27
Robust estimation : limit theorems and their applications
... Frechet differentiability of the M-estimator that is a solution to the minimizing equations of the Mean Squared Error distance indicates a robustness of this statistic. This combined with the consistently good small ...
226
Robust estimation of hydrological model parameters
... The estimation of hydrological model parameters is a challenging ...of robust parameter vectors for a hydrological ...of robust parameter vectors, a geometrical approach based on Tukey’s half space ...
11
Robust and sparse estimation in high-dimensions.
... In this thesis, different aspects of robust estimation are studied. Answering several applied and theoretical problems opens up new directions for future research. Due to the novelty of the topic of ...
187
Outlier robust small area estimation
... presence of outliers in the population data. Turning to the median RRMSE results, we see that claims in the literature (e.g. Chambers and Tzavidis, 2006) about the superior outlier robustness of MQ compared with ...
34
Outlier Robust Small Area Estimation
... ‘optimal’ cut-off value c for the function and improve the efficiency of the BC estimators remains to be done. A cross-validation approach could be a possible method. The pseudo-linearization and linearization-based MSE ...
42
Robust Estimation for Fisher Discriminant Analysis
... worst-case estimation of the covariances leads one to believe that it is only useful for a small sample size: given a data set with a small sample size, one should expect that, if we were to take more samples from ...
15
Model Averaged Double Robust Estimation
... Methods for causal inference are predicated on knowledge of the covariates necessary to satisfy the no unmeasured confounding assumption, but the exact set of covariates needed to control confounding is rarely known. ...
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