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The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can...
Persistent link: https://www.econbiz.de/10014200429
environmental inputs. We also propose a new decision rule that is suitable for adjustable integer decision variables. We illustrate … only experimental data, so it does not need these classic assumptions. Moreover, we develop 'adjustable' robust parameter …
Persistent link: https://www.econbiz.de/10014159513
This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimisation...
Persistent link: https://www.econbiz.de/10012918912
At the present time there is no well accepted test for determining whether or not robust regression parameter estimates are significantly different than least squares estimates. Thus. we propose and demonstrate the efficacy of two Wald-like statistical tests for the above purposes using...
Persistent link: https://www.econbiz.de/10013215762
Abstract This paper is focused on detailed aspects of the loss function rho and its derivative psi for an optimal bias robust regression method that minimizes the maximum asymptotic bias subject to a constraint on normal distribution efficiency. The analytic form of the psi function was...
Persistent link: https://www.econbiz.de/10013216274
We study the out-of-sample properties of robust empirical optimization problems with smooth φ-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative “robustness parameter” δ that controls the size of the deviations...
Persistent link: https://www.econbiz.de/10012833858
In this paper, we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the “robustness parameter” for worst-case maximization problems with concave reward functions. Building on the intuition that robust optimization reduces...
Persistent link: https://www.econbiz.de/10012943295
We propose the relaxation algorithm as a simple and powerful method for simulating the transition process in growth models. This method has a number of important advantages: (1) It can easily deal with a wide range of dynamic systems including multi-dimensional systems with stable eigenvalues...
Persistent link: https://www.econbiz.de/10002521532
This article demonstrates the use of two approaches to parallelizing a Garch(1,1) calibration algorithm. The base serial implementation is a genetic algorithm that uses maximum likelihood in the fitness function. This is written in generic C. The first type of parallelization involves...
Persistent link: https://www.econbiz.de/10014178906
We compare three alternative Maximum Likelihood Multidimensional Scaling methods for pairwise dissimilarity ratings, namely MULTISCALE, MAXSCAL, and gurations very well. The recovery of the true dimensionality depends on the test criterion (likelihood ratio test, AIC, or CAIC), as well as on the...
Persistent link: https://www.econbiz.de/10014045900