Results 51 to 60 of about 229,123 (176)
Nonparametric Estimation of Range Value at Risk
Computation, 2023 Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The
Suparna Biswas, Rituparna Sen
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Minimax and Adaptive Inference in Nonparametric Function Estimation [PDF]
, 2012 Since Stein's 1956 seminal paper, shrinkage has played a fundamental role in both parametric and nonparametric inference. This article discusses minimaxity and adaptive minimaxity in nonparametric function estimation.
Cai, T. Tony
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Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
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Nonparametric Estimation of The Variogram an Application [PDF]
Kirkuk Journal of Science, 2017 This Research Deals with Non Parametric Estimation of Variogram Function . As it is known The Variogram Function is Considered As a very Important Parameter in Investigating The Spatial Dependence for The Spatial Stochastic Process .The Non ...
Taha Yaseen H, Mohammed N.I.Qassim
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PEMILIHAN THRESHOLD OPTIMAL PADA ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN METODE CROSS VALIDASI
Media Statistika, 2009 If x is a predictor variable and y is a response variable of the regression model y = f (x)+ Î with f is a regression function which not yet been known and Î is independent random variable with mean 0 and variance , hence function f can be estimated ...
Suparti Suparti +2 more
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Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method
Journal of Function Spaces, 2023 The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option ...
Xiangyu Ge +4 more
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Yield Curve Estimation by Kernel Smoothing Methods [PDF]
We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we ...
C Tanggaard +3 more
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Improved nonparametric estimation of the drift in diffusion processes
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 In this paper, we have considered the robust adaptive nonparametric estimation problem for the drift coefficient in diffusion processes. It has been shown that the initial estimation problem can be reduced to the estimation problem in a discrete time ...
E.A. Pchelintsev +2 more
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Moment Restriction-based Econometric Methods: An Overview [PDF]
Moment restriction-based econometric modelling is a broad class which includes the parametric, semiparametric and nonparametric approaches. Moments and conditional moments themselves are nonparametric quantities.
Kunitomo, N. +2 more
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IEEE Access, 2019 In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to model clustering features in emergency calls. Gaussian kernel density functions are considered.
Chenlong Li, Zhanjie Song, Xu Wang
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