Calibration estimator for Head Count Index

This paper considers the problem of estimating a poverty measure, the Head Count Index, using the auxiliary information available, which is incorporated into the estimation procedure by calibration techniques. The proposed method does not directly use the auxiliary information provided by auxiliary variables related to the variable of interest in the calibration process, but the auxiliary information, after a transformation, is incorporated by calibration techniques applied to the distribution function of the study variable. Monte Carlo experiments were carried out for simulated data and for real data taken from the Spanish living conditions survey to explore the performance of the new estimation methods of the Head Count Index

Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem

Ministerio de Economía y Competitivida

Reduction of optimal calibration dimension with a new optimal auxiliary vector for calibrated estimators of the distribution function

The calibration method has been widely used to incorporate auxiliary information in the estimation of various parameters. Specifically, adapted this method to estimate the distribution function, although their proposal is computationally simple, its efficiency depends on the selection of an auxiliary vector of points. This work deals with the problem of selecting the calibration auxiliary vector that minimize the asymptotic variance of the calibration estimator of distribution function. The optimal dimension of the optimal auxiliary vector is reduced considerably with respect to previous studies so that with a smaller set of points the minimum of the asymptotic variance can be reached, which in turn allows to improve the efficiency of the estimates

The optimization problem of quantile and poverty measures estimation based on calibration

New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators.Ministerio de Educacion y Cienci

A BAYESIAN ALTERNATIVE TO GENERALIZED CROSS ENTROPY SOLUTIONS FOR UNDERDETERMINED ECONOMETRIC MODELS

This paper presents a Bayesian alternative to Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) methods for deriving solutions to econometric models represented by underdetermined systems of equations. For certain types of econometric model specifications, the Bayesian approach provides fully equivalent results to GME-GCE techniques. However, in its general form, the proposed Bayesian methodology allows a more direct and straightforwardly interpretable formulation of available prior information and can reduce significantly the computational effort involved in finding solutions. The technique can be adapted to provide solutions in situations characterized by either informative or uninformative prior information.Underdetermined Equation Systems, Maximum Entropy, Bayesian Priors, Structural Estimation, Calibration, Research Methods/ Statistical Methods, C11, C13, C51,

Neural Network Parametrization of Deep-Inelastic Structure Functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.Comment: Latex, 43 pages, 22 figures. (v2) Final version, published in JHEP; Sect.5.2 and Fig.9 improved, a few typos corrected and other minor improvements. (v3) Some inconsequential typos in Tab.1 and Tab 5 corrected. Neural parametrization available at http://sophia.ecm.ub.es/f2neura

Calibration estimator for Head Count Index

This paper considers the problem of estimating a poverty measure, the Head Count Index, using the auxiliary information available, which is incorporated into the estimation procedure by calibration techniques. The proposed method does not directly use the auxiliary information provided by auxiliary variables related to the variable of interest in the calibration process, but the auxiliary information, after a transformation, is incorporated by calibration techniques applied to the distribution function of the study variable. Monte Carlo experiments were carried out for simulated data and for real data taken from the Spanish living conditions survey to explore the performance of the new estimation methods of the Head Count Index

The detection and treatment of distance errors in kinematic analyses of stars

We present a new method for detecting and correcting systematic errors in the distances to stars when both proper motions and line-of-sight velocities are available. The method, which is applicable for samples of 200 or more stars that have a significant extension on the sky, exploits correlations between the measured U, V and W velocity components that are introduced by distance errors. We deliver a formalism to describe and interpret the specific imprints of distance errors including spurious velocity correlations and shifts of mean motion in a sample. We take into account correlations introduced by measurement errors, Galactic rotation and changes in the orientation of the velocity ellipsoid with position in the Galaxy. Tests on pseudodata show that the method is more robust and sensitive than traditional approaches to this problem. We investigate approaches to characterising the probability distribution of distance errors, in addition to the mean distance error, which is the main theme of the paper. Stars with the most overestimated distances bias our estimate of the overall distance scale, leading to the corrected distances being slightly too small. We give a formula that can be used to correct for this effect. We apply the method to samples of stars from the SEGUE survey, exploring optimal gravity cuts, sample contamination, and correcting the used distance relations.Comment: published in MNRAS 14 pages, 8 figures, 2 tables, corrected eq.(35), minor editin

Heuristic Strategies in Finance – An Overview

This paper presents a survey on the application of heuristic optimization techniques in the broad field of finance. Heuristic algorithms have been extensively used to tackle complex financial problems, which traditional optimization techniques cannot efficiently solve. Heuristic optimization techniques are suitable for non-linear and non-convex multi-objective optimization problems. Due to their stochastic features and their ability to iteratively update candidate solutions, heuristics can explore the entire search space and reliably approximate the global optimum. This overview reviews the main heuristic strategies and their application to portfolio selection, model estimation, model selection and financial clustering.finance, heuristic optimization techniques, portfolio management, model selection, model estimation, clustering