Estimating causal effects with the neural autoregressive density estimator
Journal of Causal Inference, 2021 The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional
Garrido Sergio +3 more
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A novel approach to compare the spectral densities of some uncorrelated cyclostationary time series
Alexandria Engineering Journal, 2022 Our primary objective in this article is to compare the spectral densities of some cyclostationary time series. By using the limiting distributions of the discrete Fourier transform, a novel approach is introduced to determine whether the spectral ...
Mohammad Reza Mahmoudi +4 more
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Local linear approach: Conditional density estimate for functional and censored data
Demonstratio Mathematica, 2022 Let YY be a random real response, which is subject to right censoring by another random variable CC. In this paper, we study the nonparametric local linear estimation of the conditional density of a scalar response variable and when the covariable takes ...
Benkhaled Abdelkader, Madani Fethi
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Bayesian estimation of generalized partition of unity copulas
Dependence Modeling, 2020 This paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18].
Masuhr Andreas, Trede Mark
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Detecting and modeling critical dependence structures between random inputs of computer models
Dependence Modeling, 2020 Uncertain information on input parameters of computer models is usually modeled by considering these parameters as random, and described by marginal distributions and a dependence structure of these variables.
Benoumechiara Nazih +3 more
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Bivariate box plots based on quantile regression curves
Dependence Modeling, 2020 In this paper, we propose a procedure to build bivariate box plots (BBP). We first obtain the theoretical BBP for a random vector (X, Y). They are based on the univariate box plot of X and the conditional quantile curves of Y|X. They can be computed from
Navarro Jorge
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Density derivative estimation for stationary and strongly mixing data
Alexandria Engineering Journal, 2020 Estimation of density derivatives has found multiple uses in statistical data analysis. An inefficient two-step method to obtain it is estimating the density and then computing the derivatives.
Marziyeh Mahmoudi +3 more
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Improved parameter estimation of Time Dependent Kernel Density by using Artificial Neural Networks
Journal of Finance and Data Science, 2018 Time Dependent Kernel Density Estimation (TDKDE) used in modelling time-varying phenomenon requires two input parameters known as bandwidth and discount to perform.
Xing Wang +2 more
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Dependence measure for length-biased survival data using copulas
Dependence Modeling, 2019 The linear correlation coefficient of Bravais-Pearson is considered a powerful indicator when the dependency relationship is linear and the error variate is normally distributed.
Bentoumi Rachid +2 more
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Optimal bandwidth selection for recursive Gumbel kernel density estimators
Dependence Modeling, 2019 In this paper, we propose a data driven bandwidth selection of the recursive Gumbel kernel estimators of a probability density function based on a stochastic approximation algorithm.
Slaoui Yousri
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