Browsing DiSES Working Papers by Author "Giordano, Francesco"
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A locally adaptive bandwidth selector for kernel based regression
Giordano, Francesco; Parrella, Maria Lucia (2009)The selection of the smoothing parameter represents a crucial step in the local polynomial regression, because of the implications on the consistency of the nonparametric regression estimator and because of the difficulties ... 
Biascorrected inference for multivariate nonparametric regression: model selection and oracle property
Giordano, Francesco; Parrella, Maria Lucia (2014)The local polynomial estimator is particularly affected by the curse of di mensionality. So, the potentialities of such a tool become ineffective for large dimensional applications. Motivated by this, we propose a new ... 
GRID for model structure discovering in high dimensional regression
Giordano, Francesco; Lahiri, Soumendra Nath; Parrella, Maria Lucia (2014)Given a nonparametric regression model, we assume that the number of covariates d → ∞ but only some of these covariates are relevant for the model. Our goal is to identify the relevant covariates and to obtain some ... 
Parameter estimation in continuous stochastic volatility models
Albano, Giuseppina; Giordano, Francesco; Perna, Cira (2009)Continuoustime di usion processes are often used in literature to model dynamics of nancial markets. In such kinds of models a rel evant role is played by the variance of the process. So assumptions on the functional ... 
Weak consistent moving block bootstrap estimator for the variance of CLS estimators in a class of bilinear models
Giordano, Francesco (2008)Grahn (1995) introduced the Conditional Least Squares estimators for the class (I) of bilinear models. These estimators have a variance which is difficulty to derive analytically. In this paper we derive the conditions ... 
Weak consistent moving block bootstrap estimator of sampling distribution of CLS estimators in a class of bilinear models
Giordano, Francesco (2005)Grahn, (1995) introduced the Conditional Least Squares estimators for the class (I) of bilinear models. Such estimators have a variance which is difficulty to derive analytically. The aim of the present paper is to ...