Nonparametric estimation and U-statistics have emerged as vital tools in modern statistical analysis, offering robust alternatives to traditional parametric methods. Nonparametric techniques bypass ...
Nonparametric estimation under shape constraints represents a vibrant field that bridges rigorous mathematical theory with practical applications. This approach leverages inherent qualitative ...
The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 20, No. 2 (Jun., 1992), pp. 171-185 (15 pages) A method for nonparametric estimation of density based on a randomly ...
The Annals of Statistics, Vol. 37, No. 3 (Jun., 2009), pp. 1360-1404 (45 pages) We study the problem of nonparametric estimation of a multivariate function $g ...
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 ...
Mitchell Grant is a self-taught investor with over 5 years of experience as a financial trader. He is a financial content strategist and creative content editor. Timothy Li is a consultant, accountant ...
We use influence functions as a basic tool to study unconditional nonparametric and parametric expected shortfall (ES) estimators with regard to returns data influence, standard errors and coherence.
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