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A review of kernel density estimation with applications to econometrics
Nonparametric density estimation is of great importance when econometricians want to
model the probabilistic or stochastic structure of a data set. This comprehensive review …
model the probabilistic or stochastic structure of a data set. This comprehensive review …
Kernel density estimation and its application
S Węglarczyk - ITM web of conferences, 2018 - itm-conferences.org
Kernel density estimation is a technique for estimation of probability density function that is a
must-have enabling the user to better analyse the studied probability distribution than when …
must-have enabling the user to better analyse the studied probability distribution than when …
Analysis of randomized comparative clinical trial data for personalized treatment selections
Suppose that under the conventional randomized clinical trial setting, a new therapy is
compared with a standard treatment. In this article, we propose a systematic, 2-stage …
compared with a standard treatment. In this article, we propose a systematic, 2-stage …
Nonparametric estimation of copula functions for dependence modelling
SX Chen, TM Huang - Canadian Journal of Statistics, 2007 - Wiley Online Library
Copulas characterize the dependence among components of random vectors. Unlike
marginal and joint distributions, which are directly observable, the copula of a random vector …
marginal and joint distributions, which are directly observable, the copula of a random vector …
Forecasting value-at-risk of cryptocurrencies with riskmetrics type models
Since the financial crisis, risk management has been of growing interest to investors and the
approach of Value-at-Risk has gained wide acceptance. Investing in Cryptocurrencies …
approach of Value-at-Risk has gained wide acceptance. Investing in Cryptocurrencies …
On estimating distribution functions using Bernstein polynomials
A Leblanc - Annals of the Institute of Statistical Mathematics, 2012 - Springer
It is a known fact that some estimators of smooth distribution functions can outperform the
empirical distribution function in terms of asymptotic (integrated) mean-squared error. In this …
empirical distribution function in terms of asymptotic (integrated) mean-squared error. In this …
Empirical likelihood for estimating equations with missing values
D Wang, SX Chen - 2009 - projecteuclid.org
We consider an empirical likelihood inference for parameters defined by general estimating
equations when some components of the random observations are subject to missingness …
equations when some components of the random observations are subject to missingness …
Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis
Previous studies have shown an association between osteoporosis and automatic
measurements of mandibular cortical width on dental panoramic radiographs (DPRs). In this …
measurements of mandibular cortical width on dental panoramic radiographs (DPRs). In this …
Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory
We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional
expected shortfall (CES) associated with conditional distributions of a series of returns on a …
expected shortfall (CES) associated with conditional distributions of a series of returns on a …
Multistage plug—in bandwidth selection for kernel distribution function estimates
AM Polansky, ER Baker - Journal of Statistical Computation and …, 2000 - Taylor & Francis
The use of a kernel estimator as a smooth estimator for a distribution function has been
suggested by many authors An expression for the bandwidth that minimizes the mean …
suggested by many authors An expression for the bandwidth that minimizes the mean …