Szkoła Główna Handlowa w Warszawie - Centralny System Uwierzytelniania
Strona główna

Duration Analysis

Informacje ogólne

Kod przedmiotu: 229081-D
Kod Erasmus / ISCED: 11.2 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0542) Statystyka Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Duration Analysis
Jednostka: Szkoła Główna Handlowa w Warszawie
Grupy: Major courses for AAB - masters
Przedmioty obowiązkowe na programie SMMD-ADA
Punkty ECTS i inne: 3.00 (zmienne w czasie) Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.

zobacz reguły punktacji
Język prowadzenia: angielski
Efekty uczenia się:

Wiedza:

Student should be able to:

know history, philosophy and specific of non-parametric, parametric and semi-parametric event history models and other special cases of the models as: discrete time event history models and competing risk models; Survival Data Mining methods and Bayesian approach to duration analysis.

Student should be able to:

theory in the field of basic model types: single episode, multiple episodes; competitive risks models; models based on staging process; non-parametric, parametric and semi-parametric models; Survival Data Mining methods.

Student should be able to:

know the estimation methods of the duration analysis models: the maximum likelihood method, the partial likelihood method and their modifications and their basic algorithms; methods of estimating survival analysis models in the Bayesian approach and methods of estimating Survival Data Mining models.

Umiejętności:

Student should be able to:

define and prepare a target variable for estimating the models of duration analysis, define the event(s) to be investigated, define and specify the type of truncation and censoring, and construct correctly a set of data for estimation of survival models containing time-variant and time-invariant variables.

Student should be able to:

estimate, verify and evaluate the quality of models; correctly interpret the estimation results of different types of survival analysis models and Survival Data Mining models in relation to time-variant and time-invariant variables.

Student should be able to:

correctly assess the predictive value of the estimated model on the basis of the estimation results; build a predictive model and perform full model diagnostics, and take further actions to improve the quality of predictions.

Kompetencje społeczne:

Knowledge on how to estimate duration analysis models using SAS and R programmes for business modeling.

Acquisition of "hard and soft" skills in the field of duration analysis modelling; team work skills (through participation in a team preparing projects); skills to encourage and substantively persuade to use the survival models in the business intelligence environment.

Zajęcia w cyklu "Preferencje - Semestr letni 2024/25" (jeszcze nie rozpoczęty)

Okres: 2025-02-15 - 2025-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Zajęcia prowadzącego więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Wioletta Grzenda
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Zajęcia prowadzącego - Ocena

Zajęcia w cyklu "Semestr letni 2024/25" (jeszcze nie rozpoczęty)

Okres: 2025-02-15 - 2025-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach.

Pełny opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach. Various areas of application of the duration analysis are indicated, with a particular focus on gaining practical skills in using these models. As a part of the laboratory classes, the full process of building survival models is practised, including the following stages of their construction: data preparation, definition and selection of variables, estimation and verification of the model, assessment of the usability of the model for the prediction purposes. Examples of application will be presented in SAS and R softwares. These classes are a part of the Data Scientist with the SAS System Certificate.

Literatura:

Literatura podstawowa:

Allison P.D. (2018), Survival Analysis Using the SAS?: A Practical Guide, Second Edition, SAS Institute Inc., Cary, NC.

Ibrahim J.G., Chen M.H., Sinha D. (2001), Bayesian Survival Analysis, Springer-Verlag, New York.

Jenkins S. P. (2005), Survival analysis. Unpublished manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK, 42, 54-56.

Klein J.P., Moeschberger M.L. (2005), Survival Analysis: Techniques for Censored and Truncated Data, Springer Science & Business Media, New York.

Kleinbaum D.G., Klein M. (2006), Survival Analysis: A Self-Learning Text, Springer Science & Business Media, USA.

Moore, D. F. (2016), Applied survival analysis using R. New York, NY: Springer.

Literatura uzupełniająca:

Blossfeld H.P., Rohwer G. (1995), Techniques of Event History Modeling. New Approaches to Causal Analysis, L. Erlbaum, Mahwah, NJ.

Broström, G. (2021), Event history analysis with R. Chapman and Hall/CRC.

Coleman J.S. (1981), Longitudinal Data Analysis, Basic Books, Inc. Publisher, New York.

Collett D. (2014), Modelling Survival Data in Medical Research, Chapman and Hall, CRC Press Book, London.

Finkelstein M.S. (2008), Failure Rate Modelling for Reliability and Risk, Springer, London.

Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000), Bayesian Data Analysis, Chapman & Hall/CRC, London.

Greenberg E. (2013), Introduction to Bayesian Econometrics, Cambridge University Press, New York.

Grzenda W. (2021), Direct adjusted survival probabilities in the analysis of finding a job by the unemployed depending on their individual characteristics. In: Jajuga K., Najman K., Walesiak M. (eds) Data Analysis and Classification, Methods and Applications. Springer, Cham. 229-244

Grzenda W. (2020), Prediction of the probability of employment termination by people over the age of 50 using parametric survival models. In M. Papież and S. Śmiech (Eds.), The 14th Professor Aleksander Zeliaś International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings. Krakow, 52-60.

Grzenda W. (2019), Survival Modelling of Repeated Events Using the Example of Changes in the Place of Employment, Acta Universitatis Lodziensis. Folia Oeconomica, 3(342), 183-197.

Grzenda W. (2017), Modelling the duration of the first job using Bayesian accelerated failure time models, Acta Universitatis Lodziensis, Folia Oeconomica, 4(330), 19-38.

Grzenda W., Buczyński M. K. (2015), Estimation of employee turnover with competing risks models, Folia Oeconomica Stetinensia, 15(2), 53-65.

Grzenda W. (2013), The significance of prior information in Bayesian parametric survival models, Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31-39.

Hosmer D.W., Lemeshow S., May S. (2008), Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York.

Kalbfleisch J.D., Prentice R.L. (2011), The Statistical Analysis of Failure Time Data, Second Edition, Wiley, USA.

Legrand C. (2021), Advanced Survival Models, Chapman and Hall/CRC, USA

Liu X. (2012), Survival Analysis: Models and Applications, John Wiley & Sons, United Kingdom.

Miller R.G. (2011), Survival Analysis, Vol. 66, John Wiley & Sons.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)

Okres: 2024-10-01 - 2025-02-14
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach.

Pełny opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach. Various areas of application of the duration analysis are indicated, with a particular focus on gaining practical skills in using these models. As a part of the laboratory classes, the full process of building survival models is practised, including the following stages of their construction: data preparation, definition and selection of variables, estimation and verification of the model, assessment of the usability of the model for the prediction purposes. Examples of application will be presented in SAS and R softwares. These classes are a part of the Data Scientist with the SAS System Certificate.

Literatura:

Literatura podstawowa:

Allison P.D. (2018), Survival Analysis Using the SAS?: A Practical Guide, Second Edition, SAS Institute Inc., Cary, NC.

Ibrahim J.G., Chen M.H., Sinha D. (2001), Bayesian Survival Analysis, Springer-Verlag, New York.

Jenkins S. P. (2005), Survival analysis. Unpublished manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK, 42, 54-56.

Klein J.P., Moeschberger M.L. (2005), Survival Analysis: Techniques for Censored and Truncated Data, Springer Science & Business Media, New York.

Kleinbaum D.G., Klein M. (2006), Survival Analysis: A Self-Learning Text, Springer Science & Business Media, USA.

Moore, D. F. (2016), Applied survival analysis using R. New York, NY: Springer.

Literatura uzupełniająca:

Blossfeld H.P., Rohwer G. (1995), Techniques of Event History Modeling. New Approaches to Causal Analysis, L. Erlbaum, Mahwah, NJ.

Broström, G. (2021), Event history analysis with R. Chapman and Hall/CRC.

Coleman J.S. (1981), Longitudinal Data Analysis, Basic Books, Inc. Publisher, New York.

Collett D. (2014), Modelling Survival Data in Medical Research, Chapman and Hall, CRC Press Book, London.

Finkelstein M.S. (2008), Failure Rate Modelling for Reliability and Risk, Springer, London.

Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000), Bayesian Data Analysis, Chapman & Hall/CRC, London.

Greenberg E. (2013), Introduction to Bayesian Econometrics, Cambridge University Press, New York.

Grzenda W. (2021), Direct adjusted survival probabilities in the analysis of finding a job by the unemployed depending on their individual characteristics. In: Jajuga K., Najman K., Walesiak M. (eds) Data Analysis and Classification, Methods and Applications. Springer, Cham. 229-244

Grzenda W. (2020), Prediction of the probability of employment termination by people over the age of 50 using parametric survival models. In M. Papież and S. Śmiech (Eds.), The 14th Professor Aleksander Zeliaś International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings. Krakow, 52-60.

Grzenda W. (2019), Survival Modelling of Repeated Events Using the Example of Changes in the Place of Employment, Acta Universitatis Lodziensis. Folia Oeconomica, 3(342), 183-197.

Grzenda W. (2017), Modelling the duration of the first job using Bayesian accelerated failure time models, Acta Universitatis Lodziensis, Folia Oeconomica, 4(330), 19-38.

Grzenda W., Buczyński M. K. (2015), Estimation of employee turnover with competing risks models, Folia Oeconomica Stetinensia, 15(2), 53-65.

Grzenda W. (2013), The significance of prior information in Bayesian parametric survival models, Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31-39.

Hosmer D.W., Lemeshow S., May S. (2008), Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York.

Kalbfleisch J.D., Prentice R.L. (2011), The Statistical Analysis of Failure Time Data, Second Edition, Wiley, USA.

Legrand C. (2021), Advanced Survival Models, Chapman and Hall/CRC, USA

Liu X. (2012), Survival Analysis: Models and Applications, John Wiley & Sons, United Kingdom.

Miller R.G. (2011), Survival Analysis, Vol. 66, John Wiley & Sons.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)

Okres: 2024-02-24 - 2024-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Aleksandra Iwanicka
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach.

Pełny opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach. Various areas of application of the duration analysis are indicated, with a particular focus on gaining practical skills in using these models. As a part of the laboratory classes, the full process of building survival models is practised, including the following stages of their construction: data preparation, definition and selection of variables, estimation and verification of the model, assessment of the usability of the model for the prediction purposes. Examples of application will be presented in SAS and R softwares. These classes are a part of the Data Scientist with the SAS System Certificate.

Literatura:

Literatura podstawowa:

Allison P.D. (2018), Survival Analysis Using the SAS?: A Practical Guide, Second Edition, SAS Institute Inc., Cary, NC.

Frątczak E., Gach-Ciepiela U., Babiker H. (2005), Analiza historii zdarzeń. Elementy teorii, wybrane przykłady zastosowań, Oficyna Wydawnicza SGH, Warszawa.

Grzenda W. (2019), Modelowanie karier zawodowej i rodzinnej z wykorzystaniem podejścia bayesowskiego, Wydawnictwo Naukowe PWN, Warszawa.

Ibrahim J.G., Chen M.H., Sinha D. (2001), Bayesian Survival Analysis, Springer-Verlag, New York.

Jenkins S. P. (2005), Survival analysis. Unpublished manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK, 42, 54-56.

Klein J.P., Moeschberger M.L. (2005), Survival Analysis: Techniques for Censored and Truncated Data, Springer Science & Business Media, New York.

Kleinbaum D.G., Klein M. (2006), Survival Analysis: A Self-Learning Text, Springer Science & Business Media, USA.

Landmesser J. (2013), Wykorzystanie metod analizy czasu trwania do badania aktywności ekonomicznej ludności w Polsce, Wydawnictwo SGGW, Warszawa.

Moore, D. F. (2016), Applied survival analysis using R. New York, NY: Springer.

Literatura uzupełniająca:

Balicki A. (2006), Analiza przeżycia i tablice wymieralności, PWE, Warszawa.

Bieszk-Stolorz B., Landmesser J., Markowicz I. (2020), Analiza trwania w badaniach ekonomicznych. Modele parametryczne, CeDeWu, Warszawa.

Bieszk-Stolorz B., Markowicz I. (2019), Analiza trwania w badaniach ekonomicznych. Modele nieparametryczne i semiparametryczne, CeDeWu, Warszawa.

Blossfeld H.P., Rohwer G. (1995), Techniques of Event History Modeling. New Approaches to Causal Analysis, L. Erlbaum, Mahwah, NJ.

Broström, G. (2021), Event history analysis with R. Chapman and Hall/CRC.

Coleman J.S. (1981), Longitudinal Data Analysis, Basic Books, Inc. Publisher, New York.

Collett D. (2014), Modelling Survival Data in Medical Research, Chapman and Hall, CRC Press Book, London.

Finkelstein M.S. (2008), Failure Rate Modelling for Reliability and Risk, Springer, London.

Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000), Bayesian Data Analysis, Chapman & Hall/CRC, London.

Greenberg E. (2013), Introduction to Bayesian Econometrics, Cambridge University Press, New York.

Grzenda W. (2021), Direct adjusted survival probabilities in the analysis of finding a job by the unemployed depending on their individual characteristics. In: Jajuga K., Najman K., Walesiak M. (eds) Data Analysis and Classification, Methods and Applications. Springer, Cham. 229-244

Grzenda W. (2020), Prediction of the probability of employment termination by people over the age of 50 using parametric survival models. In M. Papież and S. Śmiech (Eds.), The 14th Professor Aleksander Zeliaś International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings. Krakow, 52-60.

Grzenda W. (2019), Survival Modelling of Repeated Events Using the Example of Changes in the Place of Employment, Acta Universitatis Lodziensis. Folia Oeconomica, 3(342), 183-197.

Grzenda W. (2017), Modelling the duration of the first job using Bayesian accelerated failure time models, Acta Universitatis Lodziensis, Folia Oeconomica, 4(330), 19?38.

Grzenda W. (2016), Modelowanie bayesowskie, teoria i przykłady zastosowań, Oficyna Wydawnicza SGH, Warszawa.

Grzenda W., Buczyński M. K. (2015), Estimation of employee turnover with competing risks models, Folia Oeconomica Stetinensia, 15(2), 53-65.

Grzenda W. (2013), The significance of prior information in Bayesian parametric survival models, Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31?39.

Grzenda W, Ptak-Chmielewska A., Przanowski K., Zwierz U. (2012), Przetwarzanie danych w SAS, (Wydanie drugie, poprawione i uzupełnione), Oficyna Wydawnicza Szkoła Główna Handlowa, Warszawa.

Hosmer D.W., Lemeshow S., May S. (2008), Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York.

Kalbfleisch J.D., Prentice R.L. (2011), The Statistical Analysis of Failure Time Data, Second Edition, Wiley, USA.

Legrand C. (2021), Advanced Survival Models, Chapman and Hall/CRC, USA

Liu X. (2012), Survival Analysis: Models and Applications, John Wiley & Sons, United Kingdom.

Miller R.G. (2011), Survival Analysis, Vol. 66, John Wiley & Sons.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)

Okres: 2023-10-01 - 2024-02-23
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach.

Pełny opis:

The aim of this course is to provide students with knowledge in the field of duration analysis, both in frequentist and Bayesian approach. During the course, various kinds of models are discussed i.e. non-parametric, parametric, and semi-parametric models. The theoretical framework of the duration models, methods of their estimation and their verification are presented in frequentist and Bayesian approach. Various areas of application of the duration analysis are indicated, with a particular focus on gaining practical skills in using these models. As a part of the laboratory classes, the full process of building survival models is practised, including the following stages of their construction: data preparation, definition and selection of variables, estimation and verification of the model, assessment of the usability of the model for the prediction purposes. Examples of application will be presented in SAS and R softwares. These classes are a part of the Data Scientist with the SAS System Certificate.

Literatura:

Literatura podstawowa:

Allison P.D. (2018), Survival Analysis Using the SAS?: A Practical Guide, Second Edition, SAS Institute Inc., Cary, NC.

Frątczak E., Gach-Ciepiela U., Babiker H. (2005), Analiza historii zdarzeń. Elementy teorii, wybrane przykłady zastosowań, Oficyna Wydawnicza SGH, Warszawa.

Grzenda W. (2019), Modelowanie karier zawodowej i rodzinnej z wykorzystaniem podejścia bayesowskiego, Wydawnictwo Naukowe PWN, Warszawa.

Ibrahim J.G., Chen M.H., Sinha D. (2001), Bayesian Survival Analysis, Springer-Verlag, New York.

Jenkins S. P. (2005), Survival analysis. Unpublished manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK, 42, 54-56.

Klein J.P., Moeschberger M.L. (2005), Survival Analysis: Techniques for Censored and Truncated Data, Springer Science & Business Media, New York.

Kleinbaum D.G., Klein M. (2006), Survival Analysis: A Self-Learning Text, Springer Science & Business Media, USA.

Landmesser J. (2013), Wykorzystanie metod analizy czasu trwania do badania aktywności ekonomicznej ludności w Polsce, Wydawnictwo SGGW, Warszawa.

Moore, D. F. (2016), Applied survival analysis using R. New York, NY: Springer.

Literatura uzupełniająca:

Balicki A. (2006), Analiza przeżycia i tablice wymieralności, PWE, Warszawa.

Bieszk-Stolorz B., Landmesser J., Markowicz I. (2020), Analiza trwania w badaniach ekonomicznych. Modele parametryczne, CeDeWu, Warszawa.

Bieszk-Stolorz B., Markowicz I. (2019), Analiza trwania w badaniach ekonomicznych. Modele nieparametryczne i semiparametryczne, CeDeWu, Warszawa.

Blossfeld H.P., Rohwer G. (1995), Techniques of Event History Modeling. New Approaches to Causal Analysis, L. Erlbaum, Mahwah, NJ.

Broström, G. (2021), Event history analysis with R. Chapman and Hall/CRC.

Coleman J.S. (1981), Longitudinal Data Analysis, Basic Books, Inc. Publisher, New York.

Collett D. (2014), Modelling Survival Data in Medical Research, Chapman and Hall, CRC Press Book, London.

Finkelstein M.S. (2008), Failure Rate Modelling for Reliability and Risk, Springer, London.

Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (2000), Bayesian Data Analysis, Chapman & Hall/CRC, London.

Greenberg E. (2013), Introduction to Bayesian Econometrics, Cambridge University Press, New York.

Grzenda W. (2021), Direct adjusted survival probabilities in the analysis of finding a job by the unemployed depending on their individual characteristics. In: Jajuga K., Najman K., Walesiak M. (eds) Data Analysis and Classification, Methods and Applications. Springer, Cham. 229-244

Grzenda W. (2020), Prediction of the probability of employment termination by people over the age of 50 using parametric survival models. In M. Papież and S. Śmiech (Eds.), The 14th Professor Aleksander Zeliaś International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings. Krakow, 52-60.

Grzenda W. (2019), Survival Modelling of Repeated Events Using the Example of Changes in the Place of Employment, Acta Universitatis Lodziensis. Folia Oeconomica, 3(342), 183-197.

Grzenda W. (2017), Modelling the duration of the first job using Bayesian accelerated failure time models, Acta Universitatis Lodziensis, Folia Oeconomica, 4(330), 19?38.

Grzenda W. (2016), Modelowanie bayesowskie, teoria i przykłady zastosowań, Oficyna Wydawnicza SGH, Warszawa.

Grzenda W., Buczyński M. K. (2015), Estimation of employee turnover with competing risks models, Folia Oeconomica Stetinensia, 15(2), 53-65.

Grzenda W. (2013), The significance of prior information in Bayesian parametric survival models, Acta Universitatis Lodziensis, Folia Oeconomica, 285, 31?39.

Grzenda W, Ptak-Chmielewska A., Przanowski K., Zwierz U. (2012), Przetwarzanie danych w SAS, (Wydanie drugie, poprawione i uzupełnione), Oficyna Wydawnicza Szkoła Główna Handlowa, Warszawa.

Hosmer D.W., Lemeshow S., May S. (2008), Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York.

Kalbfleisch J.D., Prentice R.L. (2011), The Statistical Analysis of Failure Time Data, Second Edition, Wiley, USA.

Legrand C. (2021), Advanced Survival Models, Chapman and Hall/CRC, USA

Liu X. (2012), Survival Analysis: Models and Applications, John Wiley & Sons, United Kingdom.

Miller R.G. (2011), Survival Analysis, Vol. 66, John Wiley & Sons.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin testowy: 50.00%

projekty: 50.00%

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