Search results for "Parametric statistics"
showing 10 items of 354 documents
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…
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
2020
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replica…
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…
Nonlinear quantum Langevin equations for bosonic modes in solid-state systems
2017
Based on the experimental evidence that impurities contribute to the dissipation properties of solid-state open quantum systems, we provide here a description in terms of nonlinear quantum Langevin equations of the role played by two-level systems in the dynamics of a bosonic degree of freedom. Our starting point is represented by the description of the system/environment coupling in terms of coupling to two separate reservoirs, modelling the interaction with external bosonic modes and two level systems, respectively. Furthermore, we show how this model represents a specific example of a class of open quantum systems that can be described by nonlinear quantum Langevin equations. Our analysi…
Avoiding strange attractors in efficient parametric families of iterative methods for solving nonlinear problems
2019
[EN] Searching zeros of nonlinear functions often employs iterative procedures. In this paper, we construct several families of iterative methods with memory from one without memory, that is, we have increased the order of convergence without adding new functional evaluations. The main aim of this manuscript yields in the advantage that the use of real multidimensional dynamics gives us to decide among the different classes designed and, afterwards, to select its most stable members. Moreover, we have found some elements of the family whose behavior includes strange attractors of different kinds that must be avoided in practice. In this sense, Feigenbaum diagrams have resulted an extremely …
Assessing Spillover Effects of Spatial Policies with Semiparametric Zero-Inflated Models and Random Forests
2021
The aim of this work is to estimate the variation over time of the spatial spillover effects of a public policy that was devoted to boost rural development in France over the period 1993–2002. At a micro data level, it is often observed that the dependent variable, such as local employment in a municipality, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a classical continuous response model or propensity score approaches. We consider two recent non parametric techniques that are able to deal with that estimation issue. The first approach consists in fitting two generalized additive models to estimate both the probability of no variati…
Multiplicative cases from additive cases: Extension of Kolmogorov–Feller equation to parametric Poisson white noise processes
2007
Abstract In this paper the response of nonlinear systems driven by parametric Poissonian white noise is examined. As is well known, the response sample function or the response statistics of a system driven by external white noise processes is completely defined. Starting from the system driven by external white noise processes, when an invertible nonlinear transformation is applied, the transformed system in the new state variable is driven by a parametric type excitation. So this latter artificial system may be used as a tool to find out the proper solution to solve systems driven by parametric white noises. In fact, solving this new system, being the nonlinear transformation invertible, …
A method for detecting malfunctions in PV solar panels based on electricity production monitoring
2017
In this paper a new method is developed for automatically detecting outliers or faults in the solar energy production of identical sets (sister arrays) of photovoltaic (PV) solar panels. The method involves a two-stage unsupervised approach. In the first stage, "in control" energy production data are created by using outlier detection methods and functional principal component analysis in order to remove global and local outliers from the data set. In the second stage, control charts for the "in control" data are constructed using both a parametric method and three non-parametric methods. The control charts can be used to detect outliers or faults in the production data in real-time or at t…