Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

Nonparametric estimation under shape constraints represents a vibrant field that bridges rigorous mathematical theory with practical applications. This approach leverages inherent qualitative ...

The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 34, No. 4 (Dec., 2006), pp. 535-561 (27 pages) The authors propose a new monotone nonparametric estimate for a regression ...

Nonparametric methods form an important core of statistical techniques and are typically used when data do not meet parametric assumptions. Understanding the foundation of these methods, as well as ...

A brief description of the methods used by the SYSLIN procedure follows. For more information on these methods, see the references at the end of this chapter. There are two fundamental methods of ...