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Relevant bibliographies by topics / Regularized quantiles

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Journal articles

Academic literature on the topic 'Regularized quantiles'

Author: Grafiati

Published: 7 December 2024

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Journal articles on the topic "Regularized quantiles"

1

Santos, Patricia Mendes dos, Ana Carolina Campana Nascimento, Moysés Nascimento, et al. "Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits." Pesquisa Agropecuária Brasileira 53, no. 9 (2018): 1011–17. http://dx.doi.org/10.1590/s0100-204x2018000900004.

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Abstract: The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method. The genetic data of the traits carcass yield, bacon thickness, and backfat thickness from a F2 population composed of 345 individuals, generated by crossing animals from the Piau breed with those of a commercial breed, were used. RQR was evaluated considering different quantiles (τ = 0.05 to 0.95). The RQR model used to estimate the genetic merit showed accuracies higher than o

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2

Bang, Sungwan, and Myoungshic Jhun. "Adaptive sup-norm regularized simultaneous multiple quantiles regression." Statistics 48, no. 1 (2012): 17–33. http://dx.doi.org/10.1080/02331888.2012.719512.

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3

Zou, Hui, and Ming Yuan. "Regularized simultaneous model selection in multiple quantiles regression." Computational Statistics & Data Analysis 52, no. 12 (2008): 5296–304. http://dx.doi.org/10.1016/j.csda.2008.05.013.

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4

Nascimento, Ana Carolina Campana, Camila Ferreira Azevedo, Cynthia Aparecida Valiati Barreto, Gabriela França Oliveira, and Moysés Nascimento. "Quantile regression for genomic selection of growth curves." Acta Scientiarum. Agronomy 46, no. 1 (2023): e65081. http://dx.doi.org/10.4025/actasciagron.v46i1.65081.

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This study evaluated the efficiency of genome-wide selection (GWS) based on regularized quantile regression (RQR) to obtain genomic growth curves based on genomic estimated breeding values (GEBV) of individuals with different probability distributions. The data were simulated and composed of 2,025 individuals from two generations and 435 markers randomly distributed across five chromosomes. The simulated phenotypes presented symmetrical, skewed, positive, and negative distributions. Data were analyzed using RQR considering nine quantiles (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) and tr

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5

Li, Jia, Viktor Todorov, and George Tauchen. "ESTIMATING THE VOLATILITY OCCUPATION TIME VIA REGULARIZED LAPLACE INVERSION." Econometric Theory 32, no. 5 (2015): 1253–88. http://dx.doi.org/10.1017/s0266466615000171.

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We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has

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6

Oliveira, Gabriela França, Ana Carolina Campana Nascimento, Moysés Nascimento, et al. "Quantile regression in genomic selection for oligogenic traits in autogamous plants: A simulation study." PLOS ONE 16, no. 1 (2021): e0243666. http://dx.doi.org/10.1371/journal.pone.0243666.

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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 F2 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 F6) 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

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7

Sun, Pengju, Meng Li, and Hongwei Sun. "Quantile Regression Learning with Coefficient Dependent lq-Regularizer." MATEC Web of Conferences 173 (2018): 03033. http://dx.doi.org/10.1051/matecconf/201817303033.

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In this paper, We focus on conditional quantile regression learning algorithms based on the pinball loss and lq-regularizer with 1≤q≤2. Our main goal is to study the consistency of this kind of regularized quantile regression learning. With concentration inequality and operator decomposition techniques, we obtained satisfied error bounds and convergence rates.

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Papp, Gábor, Imre Kondor та Fabio Caccioli. "Optimizing Expected Shortfall under an ℓ1 Constraint—An Analytic Approach". Entropy 23, № 5 (2021): 523. http://dx.doi.org/10.3390/e23050523.

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Expected Shortfall (ES), the average loss above a high quantile, is the current financial regulatory market risk measure. Its estimation and optimization are highly unstable against sample fluctuations and become impossible above a critical ratio r=N/T, where N is the number of different assets in the portfolio, and T is the length of the available time series. The critical ratio depends on the confidence level α, which means we have a line of critical points on the α−r plane. The large fluctuations in the estimation of ES can be attenuated by the application of regularizers. In this paper, we

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9

Wu, Hanwei, and Markus Flierl. "Vector Quantization-Based Regularization for Autoencoders." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6380–87. http://dx.doi.org/10.1609/aaai.v34i04.6108.

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Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models are susceptible to the overfitting problem and the so-called posterior collapse phenomenon. In this paper, we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations. We combine both perspectives of Vector Quantized-Variational AutoEncoders (VQ-VAE) and classical denoising regularization methods of neural networks. We interpret quantizers as r

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10

Li, Meng, and Hong-Wei Sun. "Asymptotic analysis of quantile regression learning based on coefficient dependent regularization." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 04 (2015): 1550018. http://dx.doi.org/10.1142/s0219691315500186.

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In this paper, we consider conditional quantile regression learning algorithms based on the pinball loss with data dependent hypothesis space and ℓ2-regularizer. Functions in this hypothesis space are linear combination of basis functions generated by a kernel function and sample data. The only conditions imposed on the kernel function are the continuity and boundedness which are pretty weak. Our main goal is to study the consistency of this regularized quantile regression learning. By concentration inequality with ℓ2-empirical covering numbers and operator decomposition techniques, satisfied

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