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8 results for “quantile regression”
Code and Data to "Quantile regression for temporal streamflow modeling"
<p>This is the accompanying code to "Quantile regression for temporal streamflow modeling", which is part of the manuscript "The Role of Process Heterogeneity in Statistical Modeling", which was submitted to the Austrian Journal of Statistics. </p> <p> </p> <p>The data used in this publication is fully accessible through the <a href="https://doi.org/10.5194/essd-13-4529-2021" target="_blank" rel="noopener">LamaH-CE</a> dataset. The two scripts "functions_create_data.R" and "create_data.R" will create the final dataset used for modelling. </p> <p>"functions_modelling.R" provide the functions for tuning the XGBoost model and computing the SHAP values. An example script is also attached (calc_predictions_shap.R). "analyzing_results.R" and "error_metrics.R" will produce the final output used in the manuscript. Finally, two plots produced in the script are added as pdf. </p> <p>All data analysis was performed in R, and we want to acknowledge the following packages: <a href="https://dplyr.tidyverse.org/">dplyr</a>, <a href="https://tidyr.tidyverse.org/">tidyr</a>, <a href="https://www.jstatsoft.org/v40/i03/">lubridate</a>, <a href="https://purrr.tidyverse.org/">purrr</a>, <a href="https://doi.org/10.18637/jss.v033.i01">glmnet</a>, <a href="https://cran.r-project.org/web/packages/xgboost/index.html">xgboost</a>, <a href="https://CRAN.R-project.org/package=shapr">shapr</a>, <a href="https://CRAN.R-project.org/package=Metrics">Metrics</a>, <a href="https://CRAN.R-project.org/package=gridExtra" target="_blank" rel="noopener">gridExtra</a>, <a href="https://doi.org/10.18637/jss.v014.i06">zoo</a> and <a href="https://CRAN.R-project.org/package=wesanderson">wesanderson</a>. </p> <p> </p>
Data from: On the use of double quantile regression and visual assessment to estimate performance constraints
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Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study
<p>This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F<sub>2</sub> population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F<sub>6</sub>) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10,0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.</p>
Data from: Managing more than the mean: using quantile regression to identify factors related to large elk groups
1. Animal group size distributions are often right-skewed, whereby most groups are small, but most individuals occur in larger groups that may also disproportionately affect ecology and policy. In this case, examining covariates associated with upper quantiles of the group size distribution could facilitate better understanding and management of large animal groups. 2. We studied wintering elk groups in Wyoming, where group sizes span several orders of magnitude, and issues of disease, predation and property damage are affected by larger group sizes. We used quantile regression to evaluate relationships between the group size distribution and variables of land use, habitat, elk density and wolf abundance to identify conditions important to larger elk groups. 3. We recorded 1263 groups ranging from 1 to 1952 elk and found that across all quantiles of group size, group sizes were larger in open habitat and on private land, but the largest effect occurred between irrigated and non-irrigated land [e.g. the 90th quantile group size increased by 135 elk (95% CI = 42, 227) on irrigation]. 4. Only upper quantile group sizes were positively related to broad-scale measures of elk density and wolf abundance. For wolf abundance, this effect was greater on elk groups found in open habitats and private land than those in closed habitats or public land. If we had limited our analysis to mean or median group sizes, we would not have detected these effects. 5. Synthesis and applications. Our analysis of elk group size distributions using quantile regression suggests that private land, irrigation, open habitat, elk density and wolf abundance can affect large elk group sizes. Thus, to manage larger groups by removal or dispersal of individuals, we recommend incentivizing hunting on private land (particularly if irrigated) during the regular and late hunting seasons, promoting tolerance of wolves on private land (if elk aggregate in these areas to avoid wolves) and creating more winter range and varied habitats. Relationships to the variables of interest also differed by quantile, highlighting the importance of using quantile regression to examine response variables more completely to uncover relationships important to conservation and management.
Does Investor Sentiment Predict Bitcoin Return and Volatility? - A Quantile Regression Approach
<p>This dataset was used in generating findings for the paper titled "<strong>Does Investor Sentiment Predict Bitcoin Return and Volatility? - A Quantile Regression Approach".</strong></p>
Ice water path retrievals from Meteosat-9 with quantile regression neural networks: video supplement
<p>Supplementary videos used from in A. Amell, P. Eriksson, S. Pfreundschuh: Ice water path retrievals form Meteosat-9 with quantile regression neural networks.</p>
Data from: Managing more than the mean: using quantile regression to identify factors related to large elk groups
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Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression
<p><strong>Purpose:</strong> This study aims to quantify the influence of urbanization on housing prices at the districtbased level, while also investigating the heterogeneous impacts across different quantiles of housing prices.</p> <p><br><strong>Design/methodology/approach:</strong> The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system Generalized Method of Moments is employed to evaluate how urbanization influences district-based housing prices through three spectrometrics: Urban Index (𝑈𝐼), Normalized Difference Built-up Index (𝑁𝐷𝐵𝐼), and Built-Up Index (𝐵𝑈𝐼). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo Simulation.</p> <p><br><strong>Findings:</strong> The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60𝑡ℎ percentile of housing prices, while its effect on the 10𝑡ℎ and 90𝑡ℎ percentile is comparatively weaker.</p> <p><br><strong>Originality/value:</strong> This study employs a novel method of utilizing remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.<br><br></p>
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International Brain Laboratory public data
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OpenNeuro
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