| Nome |
# |
|
GLASSO Estimation of Commodity Risks, file e3835328-b536-15e8-e053-a505fe0a3de9
|
537
|
|
Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach, file e383532c-53f9-15e8-e053-a505fe0a3de9
|
426
|
|
Estimation and Inference of Skew–Stable distributions using the Multivariate Method of Simulated Quantiles, file e383531c-9318-15e8-e053-a505fe0a3de9
|
302
|
|
Bayesian Quantile Regression using the Skew Exponential Power Distribution, file e383531d-8f5a-15e8-e053-a505fe0a3de9
|
269
|
|
Using expectile regression with latent variables for digital assets, file 7ae62fa4-7e8e-4a09-97ee-9bcc783ef4d8
|
246
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Hidden semi-Markov-switching quantile regression for time series, file e383532c-7109-15e8-e053-a505fe0a3de9
|
208
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|
A two-part finite mixture quantile regression model for semi-continuous longitudinal data, file e3835323-2eb7-15e8-e053-a505fe0a3de9
|
199
|
|
Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation, file e383532c-f9a6-15e8-e053-a505fe0a3de9
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195
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Adding MIDAS terms to Linear ARCH models in a Quantile Regression framework, file e3835328-4059-15e8-e053-a505fe0a3de9
|
192
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|
Dynamic Quantile Regression Forest, file e3835328-b1b7-15e8-e053-a505fe0a3de9
|
183
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Estimation of dynamic quantile models via the MM algorithm, file e3835323-3731-15e8-e053-a505fe0a3de9
|
170
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Marginal M-quantile regression for multivariate dependent data, file e383532e-cb5c-15e8-e053-a505fe0a3de9
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169
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Hypotheses testing in mixed–frequency volatility models: a bootstrap approach, file e383532c-6dce-15e8-e053-a505fe0a3de9
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154
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|
3D Reconstruction Model of an Extra-Abdominal Desmoid Tumor: A Case Study, file e3835327-5bed-15e8-e053-a505fe0a3de9
|
132
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Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach, file e383532c-f093-15e8-e053-a505fe0a3de9
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130
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COVID-19 After Lung Resection in Northern Italy, file e383532c-7b6c-15e8-e053-a505fe0a3de9
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126
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Interconnected risk contributions: a heavy tail approach to analyze U.S. financial sectors, file e383531b-77ec-15e8-e053-a505fe0a3de9
|
103
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Efficacy of biological agents administered as monotherapy in rheumatoid arthritis: a Bayesian mixed-treatment comparison analysis, file e3835313-dad6-15e8-e053-a505fe0a3de9
|
99
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Unconditional M-quantile regression, file e383532d-1119-15e8-e053-a505fe0a3de9
|
88
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Sparse indirect inference, file e383531d-354e-15e8-e053-a505fe0a3de9
|
72
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|
Nonthyroidal illness syndrome (NTIS) in severe COVID-19 patients: role of T3 on the Na/K pump gene expression and on hydroelectrolytic equilibrium, file e383532d-9b86-15e8-e053-a505fe0a3de9
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69
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Bayesian Quantile Regression using the Skew Exponential Power Distribution, file e383531d-5151-15e8-e053-a505fe0a3de9
|
62
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A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities, file e3835315-3489-15e8-e053-a505fe0a3de9
|
54
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Analyzing the Correlation Structure of Financial Markets Using a Quantile Graphical Model, file f33628ae-3e15-4965-aabf-6b847db4e0db
|
50
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Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children's Strengths and Difficulties Questionnaire scores, file e383532e-bcf0-15e8-e053-a505fe0a3de9
|
48
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Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall, file e029a65c-b58a-4260-9c44-ec837a825a2f
|
47
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Multivariate Mixed Hidden Markov Model for joint estimation of multiple quantiles, file e3835328-466b-15e8-e053-a505fe0a3de9
|
44
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Directional M-quantile regression for multivariate dependent outcomes, file e383532c-3e84-15e8-e053-a505fe0a3de9
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37
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Joint VaR and ES forecasting in a multiple quantile regression framework, file e3835323-1fb3-15e8-e053-a505fe0a3de9
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29
|
|
New advances in Regression Forests, file 0903be73-69e1-4637-b0a3-0223addda837
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26
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Quantile-based graphical models for continuous and discrete variables, file 60c26779-29a1-48c8-b456-e7fab4723000
|
26
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Graphical Models for Commodities: A Quantile Approach, file e383532e-9b75-15e8-e053-a505fe0a3de9
|
23
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Graphical Models for Commodities: A Quantile Approach, file e383532e-6bfe-15e8-e053-a505fe0a3de9
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21
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Quantile hidden semi-Markov models for multivariate time series, file 1a7af893-107a-4bed-ba65-860cf0f74716
|
17
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|
The network of commodity risk, file 55cf0075-4a2b-47c5-8e18-cf394a32f939
|
17
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Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition, file e383532a-054c-15e8-e053-a505fe0a3de9
|
16
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|
Estimation and Inference of Skew–Stable distributions using the Multivariate Method of Simulated Quantiles, file e383531c-4b2c-15e8-e053-a505fe0a3de9
|
14
|
|
Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach, file 54b69345-9eca-4c44-bdfa-74f286fb7636
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13
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Hypotheses testing in mixed–frequency volatility models: a bootstrap approach, file e383532c-6dcd-15e8-e053-a505fe0a3de9
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13
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M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality, file 98926711-acf0-4649-a36e-9127b34ee299
|
9
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Quantile Regression Neural Network for Quantile Claim Amount Estimation, file e383532d-f827-15e8-e053-a505fe0a3de9
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9
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Option Pricing, Zero Lower Bound, and COVID-19, file cc1c2193-2ece-4313-b9a6-b7dac9c4d435
|
8
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|
Sparse Nonparametric Dynamic Graphical Models, file e383531d-cae8-15e8-e053-a505fe0a3de9
|
7
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|
Sparse indirect inference, file e383531d-354a-15e8-e053-a505fe0a3de9
|
6
|
|
Sectoral Decomposition of CO2WorldEmissions: A Joint QuantileRegression Approach, file e3835328-4822-15e8-e053-a505fe0a3de9
|
6
|
|
Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation, file e383532c-b9f7-15e8-e053-a505fe0a3de9
|
6
|
|
Conditional Quantile Estimation for Linear ARCH Models with MIDAS Components, file e383532d-d296-15e8-e053-a505fe0a3de9
|
6
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Bayesian tail risk interedependence using quantile regression, file e3835313-d069-15e8-e053-a505fe0a3de9
|
5
|
|
Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress, file e383531f-e69e-15e8-e053-a505fe0a3de9
|
5
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|
Sparse simulation-based estimation built on quantiles, file f0e48061-4e8a-4f26-a92b-56bb6107fbe9
|
5
|
|
Selection of Value at Risk Models for Energy Commodities, file e383531d-df94-15e8-e053-a505fe0a3de9
|
4
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|
Sparse Nonparametric Dynamic Graphical Models, file e383531f-1fbd-15e8-e053-a505fe0a3de9
|
4
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Unified Unconditional Regression for Multivariate Quantiles, M-Quantiles, and Expectiles, file 42cbbd9a-b1d4-42c7-8921-6a2fb42f79cc
|
3
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Neural Networks for quantile claim amount estimation: a quantile regression approach, file c56ac012-6288-4082-88cc-8fbd17533ed5
|
3
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Multiple Risk Measures for Multivariate Dynamic Heavy–Tailed Models, file e3835317-3b32-15e8-e053-a505fe0a3de9
|
3
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On the Lp-quantiles for the Student t distribution, file e3835317-7883-15e8-e053-a505fe0a3de9
|
3
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Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition, file e383531a-9ae5-15e8-e053-a505fe0a3de9
|
3
|
|
Indirect Comparison Between Subcutaneous Biologic Agents in Ankylosing Spondylitis, file e383531d-47d9-15e8-e053-a505fe0a3de9
|
3
|
|
Bayesian binary quantile regression for the analysis of Bachelor-Master transition, file e383531d-fc63-15e8-e053-a505fe0a3de9
|
3
|
|
Likelihood-based inference for regular functions with fractional polynomial approximations, file e3835312-0e5c-15e8-e053-a505fe0a3de9
|
2
|
|
Bayesian quantile regression for tail risk interdependence, file e3835312-52fc-15e8-e053-a505fe0a3de9
|
2
|
|
Skew mixture models for loss distributions: A Bayesian approach, file e3835313-2b52-15e8-e053-a505fe0a3de9
|
2
|
|
The sparse method of simulated quantiles: an application to portfolio optimization, file e383531a-8b45-15e8-e053-a505fe0a3de9
|
2
|
|
Cross‑Country assessment of systemic risk in the European Stock Market: evidence from a CoVaR analysis, file e383531a-95b3-15e8-e053-a505fe0a3de9
|
2
|
|
Bayesian quantile regression using the skew exponential power distribution, file e383531a-de4c-15e8-e053-a505fe0a3de9
|
2
|
|
Bayesian Inference for Lp-quantile regression models, file e3835320-8f37-15e8-e053-a505fe0a3de9
|
2
|
|
Using mixed-frequency and realized measures in quantile regression, file e3835328-33bf-15e8-e053-a505fe0a3de9
|
2
|
|
Large deviations for method-of-quantiles estimators of one-dimensional parameters, file e383532d-0971-15e8-e053-a505fe0a3de9
|
2
|
|
Forecasting Multiple VaR and ES Using a Dynamic Joint Quantile Regression with an Application to Portfolio Optimization, file e383532e-283d-15e8-e053-a505fe0a3de9
|
2
|
|
Marginal M-quantile regression for multivariate dependent data, file e383532e-7947-15e8-e053-a505fe0a3de9
|
2
|
|
Mixture of conjugate prior distributions and large deviations for level crossing probabilities, file e3835311-59c4-15e8-e053-a505fe0a3de9
|
1
|
|
A Correlated Random Effects Model for Longitudinal Data with Non-ignorable Drop-out: an Application to University Student Performance, file e3835313-3a38-15e8-e053-a505fe0a3de9
|
1
|
|
Multiple seasonal cycles forecasting model: the Italian electricity demand, file e3835313-c7b3-15e8-e053-a505fe0a3de9
|
1
|
|
The Rationale for Treatment of Postresectional Bronchopleural Fistula: Analysis of 52 Patients, file e3835313-dad8-15e8-e053-a505fe0a3de9
|
1
|
|
Conditional risk based on multivariate hazard scenarios, file e383531a-b6fb-15e8-e053-a505fe0a3de9
|
1
|
|
Large deviations for risk measures in finite mixture models, file e383531a-de4a-15e8-e053-a505fe0a3de9
|
1
|
|
Are news important to predict the Value at Risk?, file e383531c-071d-15e8-e053-a505fe0a3de9
|
1
|
|
Dynamic Quantile Lasso Regression, file e3835320-8fb0-15e8-e053-a505fe0a3de9
|
1
|
|
On the Lp-quantiles and the Student-t distribution, file e3835320-94c5-15e8-e053-a505fe0a3de9
|
1
|
|
Unified Bayesian conditional autoregressive risk measures using the skew exponential power distribution, file e383532c-741c-15e8-e053-a505fe0a3de9
|
1
|
|
Expectile hidden Markov regression models for analyzing cryptocurrency returns, file e44fa47e-2524-4e2a-93b4-0a74904de6e5
|
1
|
| Totale |
4.760 |