Tag: Correlated

Spectroscopy of Mott Insulators and Correlated Metals Proceedings of the 17th Taniguchi Symposium Kashikojima, Japan, October


Free Download Spectroscopy of Mott Insulators and Correlated Metals: Proceedings of the 17th Taniguchi Symposium Kashikojima, Japan, October 24-28, 1994 By Professor Atsushi Fujimori, Professor Yoshinori Tokura (auth.)
1995 | 269 Pages | ISBN: 3642633714 | PDF | 12 MB
Spectroscopy of Mott Insulators and Correlated Metals Extensive studies of high-Tc cuprate superconductors have stimualted investigations into various transition-metal oxides. Mott transitions in particular provide fascinating problems and new concepts in condensed matter physics. This book is a collection of short overviews by well-known, active researchers in this field. It deals with the latest developments, with particular emphasis on the theoretical, spectroscopic, and transport aspects.

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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling


Free Download Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
English | 2024 | ISBN: 3031419871 | 525 Pages | PDF EPUB (True) | 23 MB
This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a "likelihood" function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessian matrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models.

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Histology A Text and Atlas With Correlated Cell and Molecular Biology


Free Download Dr. Wojciech Pawlina MD FAAA, "Histology: A Text and Atlas: With Correlated Cell and Molecular Biology"
English | ISBN: 1975181514 | 2023 | 1104 pages | PDF | 120 MB
Combining a reader-friendly textbook and a rich, full-color atlas, Histology: A Text and Atlas: With Correlated Cell and Molecular Biology, 9th Edition, equips medical, dental, health professions, and undergraduate biology and cell biology students with a comprehensive grasp of the clinical and functional correlates of histology and a vivid understanding of the structural and functional details of cells, tissues, and organs.

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Correlated Data Analysis Modeling, Analytics, and Applications


Free Download Correlated Data Analysis: Modeling, Analytics, and Applications by Peter X.-K. Song
English | PDF | 2005 | 356 Pages | ISBN : 0387713921 | 6.7 MB
Thisbook,likemanyotherbooks,wasdeliveredundertremendousinspiration and encouragement from my teachers, research collaborators, and students. My interest in longitudinal data analysis began with a short course taught jointly by K. Y. Liang and S. L. Zeger at the Statistical Society of Canada Conference in Acadia University, Nova Scotia, in the spring of 1993. At that time, I was a ?rst-year PhD student in the Department of Statistics at the University of British Columbia, and was eagerly seeking potential topics for my PhD dissertation. It was my curiosity (driven largely by my terrible c- fusion) with the generalized estimating equations (GEEs) introduced in the short course that attracted me to the ?eld of correlated data analysis. I hope that my experience in learning about it has enabled me to make this book an enjoyable intellectual journey for new researchers entering the ?eld. Thus, the book aims at graduate students and methodology researchers in stat- tics or biostatistics who are interested in learning the theory and methods of correlated data analysis. I have attempted to give a systematic account of regression models and their applications to the modeling and analysis of correlated data. Longitu- nal data, as an important type of correlated data, has been used as a main venue for motivation, methodological development, and illustration throu- out the book. Given the many applied books on longitudinal data analysis – ready available, this book is inclined more towards technical details regarding the underlying theory and methodology used in software-based applications.

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