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Showing 1 to 13 of 13 for “"quantile function"”.
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Regression Analysis of Censored Data with Applications in Perimetry
… independent variable is censored. We deal with quantile regression when the dependent variable is censored. Using the independence between the true values and the censoring limits the quantile function for the true values can be rewritten as another quantile function of the observed, censored …
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Generalized Modified Weibull Poisson Distribution with Applications to Lifetime Data
… these classes of distributions, including hazard function, reverse hazard function, moments, quantile function, entropy and distribution of order statistics are derived. Method of maximum likelihood is used to estimate the parameters of the new distribution. Finally, a real data example is …
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Methods For Constructing Confidence Intervals For Quantile Regression Coefficients
… for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the …
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Generalized Classes of Lindley Distributions With Applications to Lifetime data
… these classes of distributions, including hazard function, reverse hazard function, monotonicity property, shapes, moments, reliability, quantile function, mean deviations, Bonferroni and Lorenz curves, entropy and Fisher information are derived. Method of maximum likelihood is used to estimate …
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Two New Classes of Generalized Modified Weibull Distributions with Application to Lifetime Data
… of distributions, such as expansion of density function, hazard and reverse hazard functions, quantile function, moments, incomplete moments, generating functions, mean deviations, Bonferroni and Lorenz curves and order statistics are presented. For the GEMW, we also present Renyi entropy, …
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Three essays on nonlinear panel data models and quantile regression analysis
… Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
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Power Transformation Towards Linear or Partially Linear Quantile Regression Models
… variable such that a linear or partially linear quantile regression model holds after transformation. The two models being considered are the power-transformed linear quantile regression model and power-transformed partially linear quantile regression model, respectively. We use a cusum process …
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Modified half logistic - exponentiated Kumaraswamy distribution and applications
… will study its statistical properties, such as quantile function, moments, incomplete moments, etc. Next, maximum likelihood estimation will be used for the parameter estimation of the MHL-EK model. We will also present a related simulation study. Finally, we apply this model to three real data …
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Some extended Alpha models: properties and applications
… We study some mathematical properties, such as quantile function, moments, moment generating function, mean deviations and order statistics. In addition, we use the maximum likelihood method to estimate the parameters of the proposed models. We perform Monte Carlo simulation studies to analyze …
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Recent Advances in Bayesian Copula Models for Mixed Data and Quantile Regression
… towards joint modeling of mixed data types and quantile regression. In the first part of this work, we advance methodological and theoretical properties of the Bayesian Gaussian copula, and deploy these models in a variety of applications. Copula models link arbitrary univariate marginal …
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L’apprentissage par renforcement pour la robotique en essaim.
… testé un AQP, un AQP arc-en-ciel et un Fonction quantile entièrement paramétrée (FQE), connue en anglais comme Fully Parameterized Quantile Function (FQF), à l’aide d’une simulation et de quatre scénarios dans Air-Sim. Avec notre approche, nous obtenons des résultats impressionnants pour le …
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Three Essays on Robust Estimation of Key Factors Underlying the Changes to the U.S. Income Distribution
… explaining the changes of U.S. wage distribution function over the period of 1990-2000. The effects of these factors are estimated by using the Chamberlain's two stage Box-Cox quantile regression approach. One of the main contributions of this chapter is that it relaxes the linearity assumption of …
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Solving the Differential Equation for the Probit Function Using a Variant of the Carleman Embedding Technique.
<p>The probit function is the inverse of the cumulative distribution function associated with the standard normal distribution. It is of great utility in statistical modelling. The Carleman embedding technique has been shown to be effective in solving first order and, less efficiently, second order …