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Unsupervised mixture estimation via approximate maximum likelihood based on the Cramér - von Mises distance

Computational statistics & data analysis, 2023-09, Vol.185, p.107764, Article 107764

Bee, Marco

2023

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Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?

Risks (Basel), 2022-09, Vol.10 (9), p.169

2022

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A Maximum Entropy Approach to Loss Distribution Analysis

Entropy (Basel, Switzerland), 2013-03, Vol.15 (3), p.1100-1117

Bee, Marco

2013

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Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

Journal of empirical finance, 2016-03, Vol.36, p.86-99

2016

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On discriminating between lognormal and Pareto tail: an unsupervised mixture-based approach

Advances in data analysis and classification, 2024-06, Vol.18 (2), p.251-269

Bee, Marco

2024

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Unsupervised tail modeling via noisy cross‐entropy minimization

Applied stochastic models in business and industry, 2024-07, Vol.40 (4), p.945-959

Bee, Marco

2024

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Estimating and forecasting conditional risk measures with extreme value theory: A review

Risks (Basel), 2018-06, Vol.6 (2), p.1-16

2018

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Agricultural Survey Methods

1. Aufl., 2010

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Estimating the wrapped stable distribution via indirect inference

Communications in statistics. Simulation and computation, 2022-11, Vol.51 (11), p.6371-6387

Bee, Marco

2022

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The truncated g-and-h distribution: estimation and application to loss modeling

Computational statistics, 2022-09, Vol.37 (4), p.1771-1794

Bee, Marco

2022

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Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

Communications in statistics. Simulation and computation, 2015-09, Vol.44 (8), p.2040-2060

Bee, Marco

2015

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Where Gibrat meets Zipf: Scale and scope of French firms

Physica A, 2017-09, Vol.481, p.265-275

2017

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Powerless: gains from trade when firm productivity is not Pareto distributed

Review of world economics, 2018-02, Vol.154 (1), p.15-45

2018

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Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

Journal of operational risk, 2022-03

2022

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Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect

International review of financial analysis, 2023-10, Vol.89, p.102803, Article 102803

2023

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The size distribution of US cities: Not Pareto, even in the tail

Economics letters, 2013-08, Vol.120 (2), p.232-237

2013

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Approximate maximum likelihood estimation of the Bingham distribution

Computational statistics & data analysis, 2017-04, Vol.108, p.84-96

2017

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Realized Peaks over Threshold: A Time-Varying Extreme Value Approach with High-Frequency-Based Measures

Journal of financial econometrics, 2019-03, Vol.17 (2), p.254-283

2019

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Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Journal of applied econometrics (Chichester, England), 2018-04, Vol.33 (3), p.398-415

2018

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Estimating large losses in insurance analytics and operational risk using the g-and-h distribution

Quantitative finance, 2021-07, Vol.21 (7), p.1207-1221

2021

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An extreme value analysis of the last century crises across industries in the U.S. economy

Journal of economic dynamics & control, 2017-08, Vol.81, p.65-78

2017

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Approximate maximum likelihood estimation of the autologistic model

Computational statistics & data analysis, 2015-04, Vol.84, p.14-26

2015

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ATXN1 intermediate-length polyglutamine expansions are associated with amyotrophic lateral sclerosis

Neurobiology of aging, 2018-04, Vol.64, p.157.e1-157.e5

2018

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Likelihood-based risk estimation for variance-gamma models

Statistical methods & applications, 2018-03, Vol.27 (1), p.69-89

2018

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Machine learning techniques for default prediction: an application to small Italian companies

Risk management (Leicestershire, England), 2024-02, Vol.26 (1), p.1, Article 1

2024

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Unsupervised mixture estimation via approximate maximum likelihood based on the Cramér - von Mises distance

Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?

A Maximum Entropy Approach to Loss Distribution Analysis

Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

On discriminating between lognormal and Pareto tail: an unsupervised mixture-based approach

Unsupervised tail modeling via noisy cross‐entropy minimization

Estimating and forecasting conditional risk measures with extreme value theory: A review

Agricultural Survey Methods

Estimating the wrapped stable distribution via indirect inference

The truncated g-and-h distribution: estimation and application to loss modeling

Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

Where Gibrat meets Zipf: Scale and scope of French firms

Powerless: gains from trade when firm productivity is not Pareto distributed

Modeling multivariate operational losses via copula-based distributions with g-and-h marginals

Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect

The size distribution of US cities: Not Pareto, even in the tail

Approximate maximum likelihood estimation of the Bingham distribution

Realized Peaks over Threshold: A Time-Varying Extreme Value Approach with High-Frequency-Based Measures

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Estimating large losses in insurance analytics and operational risk using the g-and-h distribution

An extreme value analysis of the last century crises across industries in the U.S. economy

Approximate maximum likelihood estimation of the autologistic model

ATXN1 intermediate-length polyglutamine expansions are associated with amyotrophic lateral sclerosis

Likelihood-based risk estimation for variance-gamma models

Machine learning techniques for default prediction: an application to small Italian companies

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