Modeling and Analysis of Longitudinal Data | Buch | 978-0-443-13651-1 | sack.de

Buch, Englisch, Format (B × H): 152 mm x 229 mm

Modeling and Analysis of Longitudinal Data

Buch, Englisch, Format (B × H): 152 mm x 229 mm

ISBN: 978-0-443-13651-1
Verlag: Elsevier Science & Technology


Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change. Such studies and the data arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates vary with time, and bias can arise if patients drop out of the study.
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Contributors in this volume include: Patrick Heagerty Peter Song Geert Verbeke Ziyue Liu Bernard Roy Frieden Damla Senturk Brian Tom You-Gan Wang Babatunde Gbadamosi Christian Geiser Bonnie Spring James Robert Carey


Martin, Donald E.K.
Donald E.K. Martin was born and raised in Baltimore, Maryland. He attended the University of Maryland, College Park both as an undergraduate (B.S. in Mathematics) and as a graduate student (M.A. and Ph.D. in Mathematical Statistics). He worked as a Mathematical Statistician for the U.S. Department of Energy from 1991 to 1994, and was a NASA-ASEE Summer Faculty Fellow at the Goddard Space Flight Center, Greenbelt, Maryland, during the summers of 1997-1999. From 1994 to 2007 he was a faculty member of the Mathematics Department of Howard University in the nation's capital, and was also a Mathematical Statistician in the Time Series Research Group, Statistical Research Division, U.S. Bureau of the Census, from 2000 to 2007. He has been an Associate Professor in the Department of Statistics of North Carolina State University since 2007.

Dr. Martin has received three National Science Foundation research grants. A major focus of his research is the computation of distributions of patterns in Markovian sequences through an auxiliary Markov chain (AMC). For complicated patterns, the number of states can be extremely large. To mitigate this problem and facilitate the application of AMC-based methods to complicated patterns, he developed an algorithm that allows setting up an AMC with a minimal state space through rules to determine equivalent states during the state space's setup, so that no extraneous states are entered at any point. He has also developed algorithms to compute the distribution of statistics in sparse Markov models and states of hidden sparse Markov models, joining efficiency from the model and minimal state spaces.

Dr. Martin was one of four African Americans in the U.S. to receive a Ph.D. in Mathematics in 1990. He was honored by the Network of Minorities in Mathematical Sciences in February, 2020 https://mathematicallygiftedandblack.com/circle-of-excellence/. He received the NC State College of Sciences Faculty Diversity Professional Development Award in 2018, has received many Thank-a-Teacher awards, and received the NC State the Dennis Boos Citizenship Award for 2021-22 from the Statistics Department. In 2023, he was a research leader in the African Diaspora Joint Mathematics Workshop (ADJOINT, https://www.msri.org/web/msri/scientific/adjoint).


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