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Showing 1 to 14 of 14 for “"Conditional Quantiles"”.
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Nonparametric Estimate for Conditional Quantiles of Time Series
… a nonparametric approach for estimating conditional quantiles of time series for dependent data. The considered estimate is obtained by inverting a kernel estimate of the conditional distribution function. We implement the technique on four simulated samples with light and heavy-tailed …
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Prediction of Conditional Quantiles in Ordinal Regression With Applications to Aging Research
Key words: Ordinal data; Quantile regression; Nonparametric transformation model; Second Longitudinal Study of Aging.
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Quantile Regression in a Varying Coefficient Model
… the statistical quantities of interest beyond conditional means. The regression has been well developed for linear models but less explored for nonparametric models. In this thesis, we consider the estimation of conditional quantiles in a varying-coefficient model. Quantile functions are …
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Quantile Regression Methods for Recursive Structural Equation Models
… This effect is most pronounced at the lower conditional quantiles of the performance distribution, but persists even into the upper tail. In contrast, better performance by high-achieving classmates, has a somewhat more modest effect on language performance, while for math, it has little or …
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Longitudinal Growth Charts Based on Semi-Parametric Quantile Regression
… In this thesis, we propose to construct conditional growth charts based on two longitudinal quantile regression models, one parametric and one semi-parametric. Both models incorporate individual prior growth and other covariates without the assumption of normality. The semiparametric …
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Quantile regression and survival analysis
… analysis. Firstly, direct use of regression quantiles to construct confidence intervals and confidence bands for conditional quantiles and prediction intervals for future response variables under homoscedastic linear models and heteroscedastic linear models is proposed. Comparison of the …
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A Nonparametric Bounds Approach for Hedonic Housing Price Indexes With an Application to the Southern California Market 1996 to 2008
… correlation and propose estimators of extreme conditional quantiles. The estimated bounds are then used to generate price indexes in the form of a time-series of intervals. In contrast to conventional housing price indexes, these interval-valued indexes do not suffer from sample selection bias. …
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Parametric modeling of dependence of bivariate quantile regression residuals' signs
… method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The …
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Conditional Quantile Estimation With Ordinal Data
… a new regression method for estimating the conditional quantiles of the ordinal response variable. By assuming a continuous latent variable underlying the observed ordinal response variable and utilizing the equivalence property of quantile regression we obtain the estimates of the …
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Three essays in econometrics
… regressions with persistent predictors and conditionally heteroskedastic errors. Confidence intervals based on conventional quantile regression techniques are not valid when predictors are highly persistent. Moreover, the conditional heteroskedasticity introduces rather complicated nuisance …
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Essays on Time Series Modeling
… and inflation uncertainty (measured here as the conditional scale of inflation). However, it is shown that this relationship only holds for certain ranges (higher quantiles) of the conditional quantiles of our measure of scale. Finally, the fourth chapter evaluates the predictive accuracy of …
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Modified mean and quantile regression models for citation analysis
… Unlike linear regression, where only the conditional mean of a dependent variable is modeled, in QR the different conditional quantiles of the dependent variable, such as the median, are modeled based on a set of independent variables, presenting a deep description of the relationship …
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Change point detection for high dimensional data and valid inference for Bayesian linear models
… which can perform inference for multiple conditional quantiles of an arbitrary number. We show that the proposed likelihood can be used in a Bayesian framework leading to valid frequentist inference, whereas the commonly used asymmetric Laplace working likelihood leads to invalid interval …
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Essays on the Econometrics of Option Prices
… series of the risk-neutral return distribution's quantiles. Analogous quantiles for the objective-measure distribution are estimated using available methods in the literature for forecasting conditional quantiles from historical data. The essay documents the time-series properties for a range of …