Michal Pesta

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Associate Professor

Charles University

Czech Republic

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About

Dr. Michal Pesta is an Associate Professor at Charles University in the Czech Republic. His research focuses on statistical prediction, machine learning, and advanced modeling techniques including GARCH models and tensor changepoint detection. He has contributed to significant topics such as the application of bagging and regression trees in claims reserving and statistical challenges in the context of data science. Dr. Pesta is also involved in applied studies regarding the detection of ovarian cancer using ultrasound techniques.

Recent Grants

Grant: Close

Advanced Econometric Models for Option Pricing - AdEMOP

Open Date: 2018-01-01

Close Date: 2020-01-01

Grant: Close

Dynamic and granular loss reserving with copulae - DaGLoRCo

Open Date: 2018-01-01

Close Date: 2020-01-01

Grant: Close

Using copulas for modelling dependency structure of variables in the presence of covariates

Open Date: 2015-01-01

Close Date: 2017-01-01

Grant: Close

Stochastic claims reserving and statistical inference in insurance

Open Date: 2013-01-01

Close Date: 2015-01-01

Grant: Close

DYME - Dynamic Models in Economics

Open Date: 2013-01-01

Close Date: 2018-01-01

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2020

Collaborators (7)

Jakub Rohlena

Institute of Biotechnology of the Czech Academy of Sciences

CZECH REPUBLIC

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Matus Maciak

Assistant Professor

Charles University, Faculty of Mathematics and Physics

CZECH REPUBLIC

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Ludmila Krizova

Charles University

CZECH REPUBLIC

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Zuzana Bielcikova

Charles University

CZECH REPUBLIC

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Barbara Hammer

Professor at University of Bielefeld

Bielefeld University

GERMANY

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Martin Špaček

General University Hospital in Prague

CZECH REPUBLIC

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Gabriela Ciuperca

Université Claude Bernard Lyon 1

FRANCE

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Michal Pesta

Research Interests

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About

Recent Grants

Articles (20)

Special issue on “PROBASTAT 2024”

Statistical Prediction and Machine Learning

Tensor Changepoint Detection and Eigenbootstrap

Bagging and regression trees in individual claims reserving

Copula hurdle GARCH models for multivariate non-negative time series

Ultrasound examiners' ability to describe ovarian cancer spread using preacquired ultrasound videoclips from a selected patient sample with high prevalence of cancer spread

Ultrasound examiners' ability to describe ovarian cancer spread using preacquired ultrasound videoclips from a selected patient sample with high prevalence of cancer spread

Challenges and Opportunities for Statistics in the Era of Data Science

Hurdle GARCH models for nonnegative time series

Hurdle GARCH models for nonnegative time series

Real-time changepoint detection in a nonlinear expectile model

Mitochondrially targeted tamoxifen in patients with metastatic solid tumours: an open-label, phase I/Ib single-centre trial

Bootstrapping Not Independent and Not Identically Distributed Data

Stressful Daylight: Differences in Diel Rhythmicity Between Albino and Pigmented Fish

G-CSF plus azacitidine versus azacitidine alone for patients with high-risk myelodysplastic syndrome: academic, open label, randomized trial

Circulating microRNAs in Cerebrospinal Fluid and Plasma: Sensitive Tool for Detection of Secondary CNS Involvement, Monitoring of Therapy and Prediction of CNS Relapse in Aggressive B-NHL Lymphomas

FUNCTIONAL PROFILE TECHNIQUES FOR CLAIMS RESERVING

Infinitely stochastic micro reserving

Changepoint in Error-Prone Relations

Changepoint in dependent and non-stationary panels

Collaborators (7)

Jakub Rohlena

Matus Maciak

Ludmila Krizova

Zuzana Bielcikova

Barbara Hammer

Martin Špaček

Gabriela Ciuperca

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