Search results for "Nonparametric statistics"

Directional distance functions and environmental regulation

2005

Abstract In this paper we use directional technology distance functions to evaluate the impact of environmental regulations on firms’ performance. Following Fare et al. [Fare, R., Grosskopf, S., Lovell, C.A.K., Pasurka, C., 1989. Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The Review of Economics and Statistics 71, 90–98.], we construct an index that measures opportunity costs for individual firms arising from regulations that prevent free disposal of wastes. The methodology is applied to a sample of Spanish producers of ceramic pavements. We assume that firms maximise desirable output simultaneously reducing inputs, with no change in t…

Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance

2009

The main purpose of this paper is to present a theoretically sound portfolio performance measure that takes into account higher moments of the distribution of returns. First, we perform a study of the investor's preferences to higher moments of distribution within expected utility theory and discuss the performance measurement. To illustrate the investor's preferences to higher moments and the computation of a performance measure, we provide an approximation analysis of the optimal capital allocation problem and derive a formula for the Sharpe ratio adjusted for skewness of distribution. This performance measure justifies the notion of the Generalized Sharpe Ratio (GSR) introduced by Hodges…

Space-time analysis of GDP disparities among European regions : a Markov chains approach

2004

The purpose of this paper is to study the evolution of the disparities between 138 European regions over the 1980-1995 period. We characterize the regional per capita GDP cross-sectional distribution by means of nonparametric estimations of density functions and we model the growth process as a first-order stationary Markov chain. Spatial effects are then introduced within the Markov chain framework using regional conditioning (Quah, 1996b) and spatial Markov chains (Rey, 2001). The results of the analysis indicate the persistence of regional disparities, a progressive bias toward a poverty trap and the importance of geography to explain the convergence process.

Spatial pattern analysis using hybrid models: an application to the Hellenic seismicity

2016

Earthquakes are one of the most destructive natural disasters and the spatial distribution of their epi- centres generally shows diverse interaction structures at different spatial scales. In this paper, we use a multi-scale point pattern model to describe the main seismicity in the Hellenic area over the last 10 years. We analyze the interaction between events and the relationship with geo- logical information of the study area, using hybrid models as proposed by Baddeley et al. ( 2013 ). In our analysis, we find two competing suitable hybrid models, one with a full parametric structure and the other one based on nonpara- metric kernel estimators for the spatial inhomogeneity.

Nonparametric estimation of quantile versions of the Lorenz curve

2018

Non-parametric probabilistic forecasting of academic performance in Spanish high school using an epidemiological modelling approach

2013

Academic underachievement is a concern of paramount importance in Europe, and particularly in Spain, where around of 30% of the students in the last two courses in high school do not achieve the minimum knowledge academic requirement. In order to analyse this problem, we propose a mathematical model via a system of ordinary differential equations to study the dynamics of the academic performance in Spain. Our approach is based on the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. Moreover, in order to consider the uncertainty in the estimation of model parameters, a bootstrapping approach is employed. This technique permits to for…

Bayesian inference for the extremal dependence

2016

A simple approach for modeling multivariate extremes is to consider the vector of component-wise maxima and their max-stable distributions. The extremal dependence can be inferred by estimating the angular measure or, alternatively, the Pickands dependence function. We propose a nonparametric Bayesian model that allows, in the bivariate case, the simultaneous estimation of both functional representations through the use of polynomials in the Bernstein form. The constraints required to provide a valid extremal dependence are addressed in a straightforward manner, by placing a prior on the coefficients of the Bernstein polynomials which gives probability one to the set of valid functions. The…

Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially o…

2014

In the near future, millions of load curves measuring the electricity consumption of French households in small time grids (probably half hours) will be available. All these collected load curves represent a huge amount of information which could be exploited using survey sampling techniques. In particular, the total consumption of a specific cus- tomer group (for example all the customers of an electricity supplier) could be estimated using unequal probability random sampling methods. Unfortunately, data collection may undergo technical problems resulting in missing values. In this paper we study a new estimation method for the mean curve in the presence of missing values which consists in…

Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison

2020

Abstract Nonparametric and machine learning methods are flexible methods for obtaining accurate predictions. Nowadays, data sets with a large number of predictors and complex structures are fairly common. In the presence of item nonresponse, nonparametric and machine learning procedures may thus provide a useful alternative to traditional imputation procedures for deriving a set of imputed values used next for the estimation of study parameters defined as solution of population estimating equation. In this paper, we conduct an extensive empirical investigation that compares a number of imputation procedures in terms of bias and efficiency in a wide variety of settings, including high-dimens…

A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps

2021

We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approach outperforms the existing nonparametric quartic B-spline and cubic spline methods, as well as the parametric method based on the Normal Inverse Gaussian distribution. As an application, we use the proposed density estimator to price long-term variance swaps, and the model-implied prices match reasonably well with those of the variance future downloaded from the CBOE websi…