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Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships [PDF]
Scientific ReportsThe universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some invertebrates such as sea urchins, clams, and barnacles.
Gorka Bidegain +6 more
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Nonparametric C- and D-vine-based quantile regression
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach [PDF]
EntropySpatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions.
Rongshang Chen, Zhiyong Chen
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Quantile processes for semi and nonparametric regression
Electronic Journal of Statistics, 2017 A collection of quantile curves provides a complete picture of conditional distributions. Properly centered and scaled versions of estimated curves at various quantile levels give rise to the so-called quantile regression process (QRP). In this paper, we
Shih-Kang Chao +2 more
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Reference Charts for Fetal Cerebellar Vermis Height: A Prospective Cross-Sectional Study of 10605 Fetuses. [PDF]
PLoS ONE, 2016 OBJECTIVE:To establish reference charts for fetal cerebellar vermis height in an unselected population. METHODS:A prospective cross-sectional study between September 2009 and December 2014 was carried out at ALTAMEDICA Fetal-Maternal Medical Centre, Rome,
Pietro Cignini +5 more
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Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data
Revstat Statistical Journal, 2021 In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile.
Takuma Yoshida
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How Do Financial Development and Renewable Energy Affect Consumption-Based Carbon Emissions?
Mathematical and Computational Applications, 2022 This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-
Abraham Ayobamiji Awosusi +3 more
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Mathematics, 2023 The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression.
Jingxuan Guo +7 more
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Mathematics, 2023 Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful
Bertho Tantular +3 more
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Fuzzy Semi-Parametric Logistic Quantile Regression Model
Wasit Journal for Pure Sciences, 2023 In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models.
Ahmed Razzaq, Ayad H. shemaila
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