astronomy

Michael Schemper

Michael Schemper is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Michael Schemper rather than just read about it. In short: Michael Schemper is a biostatistician and an academic, serving as professor emeritus at the Medical University of Vienna. Schemper's research focuses on nonparametric estimation and testing methods, survival analysis, and particularly Cox regression models, logistic regression models, and explained variation in statistical models.

Key takeaways

Michael Schemper belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Michael Schemper to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Michael Schemper from memory before moving on to harder problems.

Reference excerpt

Michael Schemper is a biostatistician and an academic, serving as professor emeritus at the Medical University of Vienna. Schemper's research focuses on nonparametric estimation and testing methods, survival analysis, and particularly Cox regression models, logistic regression models, and explained variation in statistical models. He has been awarded Lifetime Honorary Memberships by both the International Society for Clinical Biostatistics (ISCB) and the Austro-Swiss Region (ROeS) of the International Biometric Society (IBS). According to Google Scholar, his work has been cited more than 21,000 times.

Education Schemper studied statistics at the Vienna University from 1972 to 1977, earning an M.Sc. degree in 1976 and a Ph.D. in 1977. He later completed his Habilitation in Medical Statistics and Documentation at Vienna University in 1985.

Career Schemper's academic career started as a biostatistician in 1977 and as an associate professor from 1985 at the (former) Medical Faculty of Vienna University. He was a visiting associate professor at the University of Texas in Houston, where he remained from 1987 to 1988. In 1991, he became a professor of clinical biostatistics at the Medical University of Vienna, and later became an emeritus professor there. In 1991, he founded the Institute of Clinical Biometrics at the Medical University of Vienna and remained its head until 2015.

Research Schemper has authored more than 300 publications, which have collectively received over 21,000 citations. His work spans both the application of statistics in medical research and the development of biostatistical methods. His research has focused on quantifying the variation in outcomes explained by prognostic factors, contributing conceptually to understanding the degrees of necessity and sufficiency of such factors in outcome modeling. His methodological work has addressed the analysis of survival data under non-proportional hazards and the development of solutions to the monotone likelihood problem in risk (Cox) regression. He has also examined residuals in survival analysis and the quantification of follow-up in studies of failure time. In addition, his work includes the development of statistical methods for assessing the correlation between bivariate failure times under censoring, as well as contributions to the treatment of missing data in regression analysis and to nonparametric estimation and testing in survival analysis.

Awards and honors 2016 – Honorary lifetime membership, International Society for Clinical Biostatistics 2023 – Honorary lifetime membership, Austro-Swiss Region of the International Biometric Society (ROeS)

Selected articles Mittlböck, Martina; Schemper, Michael (1996). "Explained Variation for Logistic Regression". Statistics in Medicine. 15 (19): 1987–1997. doi:10.1002/(SICI)1097-0258(19961015)15:19<1987::AID-SIM318>3.0.CO;2-9. ISSN 1097-0258. Schemper, Michael; Smith, Terry L. (1996). "A note on quantifying follow-up in studies of failure time". Controlled Clinical Trials. 17 (4): 343–346. doi:10.1016/0197-2456(96)00075-X. ISSN 0197-2456. PMID 8889347. Schemper, Michael; Heinze, Georg (1997). "Probability Imputation Revisited for Prognostic Factor Studies". Statistics in Medicine. 16 (1): 73–80. doi:10.1002/(SICI)1097-0258(19970115)16:1<73::AID-SIM472>3.0.CO;2-Z. ISSN 1097-0258. Schemper, Michael; Henderson, Robin (2000). "Predictive Accuracy and Explained Variation in Cox Regression". Biometrics. 56 (1): 249-255. doi:10.1111/j.0006-341X.2000.00249.x. Heinze, Georg; Schemper, Michael (2002). "A solution to the problem of separation in logistic regression". Statistics in Medicine. 21 (16): 2409–2419. doi:10.1002/sim.1047. ISSN 1097-0258. PMID 12210625. Heinze, Georg; Gnant, Michael; Schemper, Michael (2003). "Exact log-rank tests for unequal follow-up". Biometrics. 59 (4): 1151–1157. doi:10.1111/j.0006-341x.2003.00132.x. ISSN 0006-341X. Schemper, Michael (2003). "Predictive accuracy and explained variation". Statistics in Medicine. 22 (14): 2299–2308. doi:10.1002/sim.1486. ISSN 1097-0258. Wakounig, Samo; Heinze, Georg; Schemper, Michael (2015). "Non-parametric estimation of relative risk in survival and associated tests". Statistical Methods in Medical Research. 24 (6): 856–870. doi:10.1177/0962280211431022. ISSN 0962-2802. Gleiss, Andreas; Schemper, Michael (2019). "Quantifying degrees of necessity and of sufficiency in cause-effect relationships with dichotomous and survival outcomes". Statistics in Medicine. 38 (23): 4733–4748. doi:10.1002/sim.8331. ISSN 1097-0258. PMC 6771968. PMID 31386230. Gleiss, Andreas; Henderson, Robin; Schemper, Michael (2021). "Degrees of necessity and of sufficiency: Further results and extensions, with an application to covid-19 mortality in Austria". Statistics in Medicine. 40 (14): 3352–3366. doi:10.1002/sim.8961. ISSN 1097-0258. PMC 8207017. PMID 33942333.

References

External links Michael Schemper's page at Medical University of Vienna

Illustrations

Worked examples

Example 1 — a first encounter with Michael Schemper

Start with the simplest possible case. Write down what Michael Schemper claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Michael Schemper before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Michael Schemper ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Michael Schemper

In research: Michael Schemper appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Michael Schemper in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Michael Schemper is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic staff of the University of Vienna, Austrian academics, Biostatisticians, so understanding it makes those chapters shorter.
In everyday life: Look for Michael Schemper outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Michael Schemper in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Michael Schemper means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Michael Schemper out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Michael Schemper in simple terms?

Why does Michael Schemper matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Michael Schemper?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Michael Schemper.