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Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
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Appendix A - Some distributions
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Core Statistics
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Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.
Frontmatter
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Appendix B - Matrix computation
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3 - R
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Summary
Statistical analysis of interesting datasets is conducted using computers. Various specialised computer programmes are available to facilitate statistical work. For using general statistical theory directly with custom-built models, R is probably the most usefully flexible of such programmes.
R (R Core Team, 2012) is a progamming language and environment designed for statistical analysis. It is free (see http://cran.r-project.org to obtain a copy) and is written and maintained by a community of statisticians. A major design feature is extendibility. R makes it very straightforward to code up statistical methods in a way that is easy to distribute and for others to use. The first place to look for information on getting started with R is http://cran.r-project.org/manuals.html. I will assume that you have installed R, can start it to obtain a command console, and have at least discovered the function q() for quitting R.
The following web resources provide excellent guides to the R language at different levels.
• http://cran.r-project.org/doc/contrib/Short-refcard.pdf is a four page summary of key functions and functionality.
• http://cran.r-project.org/doc/contrib/R_language.pdf is a very concise introduction to and reference for the structure of the language.
• http://cran.r-project.org/doc/manuals/R-lang.html is the main reference manual for the language.
A huge amount of statistical functionality is built into R and its extension packages, but the aim of this chapter is simply to give a brief overview of R as a statistical programming language.
Basic structure of R
When you start R (interactively) two important things are created: a command prompt at which to type commands telling R what to do, and an environment, known interchangeably as the ‘global environment’ or ‘user workspace’ to hold the objects created by your commands. Unlike the command prompt, you do not see the global environment directly, but it is there as an extendible chunk of computer memory for holding your data, commands and other objects.
Generically in R an ‘environment’ consists of two things. The first, known in R jargon as a frame, is a set of symbols used to refer to objects, along with the data defining those objects.
Preface
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Summary
This book is aimed at the numerate reader who has probably taken an introductory statistics and probability course at some stage and would like a brief introduction to the core methods of statistics and how they are applied, not necessarily in the context of standard models. The first chapter is a brief review of some basic probability theory needed for what follows. Chapter 2 discusses statistical models and the questions addressed by statistical inference and introduces the maximum likelihood and Bayesian approaches to answering them. Chapter 3 is a short overview of the R programming language. Chapter 4 provides a concise coverage of the large sample theory of maximum likelihood estimation, and Chapter 5 discusses the numerical methods required to use this theory. Chapter 6 covers the numerical methods useful for Bayesian computation, in particular Markov chain Monte Carlo. Chapter 7 provides a brief tour of the theory and practice of linear modelling. Appendices then cover some useful information on common distributions, matrix computation and random number generation. The book is neither an encyclopedia nor a cookbook, and the bibliography aims to provide a compact list of the most useful sources for further reading, rather than being extensive. The aim is to offer a concise coverage of the core knowledge needed to understand and use parametric statistical methods and to build new methods for analysing data. Modern statistics exists at the interface between computation and theory, and this book reflects that fact. I am grateful to Nicole Augustin, Finn Lindgren, the editors at Cambridge University Press and the students on the Bath course ‘Applied Statistical Inference’ and the Academy for PhD Training in Statistics course ‘Statistical Computing’ for many useful comments.
7 - Linear models
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4 - Theory of maximum likelihood estimation
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1 - Random variables
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Summary
Random variables
Statistics is about extracting information from data that contain an inherently unpredictable component. Random variables are the mathematical construct used to build models of such variability. A random variable takes a different value, at random, each time it is observed. We cannot say, in advance, exactly what value will be taken, but we can make probability statements about the values likely to occur. That is, we can characterise the distribution of values taken by a random variable. This chapter briefly reviews the technical constructs used for working with random variables, as well as a number of generally useful related results. See De Groot and Schervish (2002) or Grimmett and Stirzaker (2001) for fuller introductions.
Cumulative distribution functions
The cumulative distribution function (c.d.f.) of a random variable (r.v.), X, is the function F(x) such that
F(x) = Pr(X ≤ x).
That is, F(x) gives the probability that the value of X will be less than or equal to x. Obviously, F(−∞) = 0, F(∞) = 1 and F(x) is monotonic. A useful consequence of this definition is that if F is continuous then F(x) has a uniform distribution on [0, 1]: it takes any value between 0 and 1 with equal probability. This is because
Pr(X ≤ x) = Pr{F(x) ≤ F(x)} = F(x) ⇒ Pr{F(x) ≤ u} = u
(if F is continuous), the latter being the c.d.f. of a uniform r.v. on [0, 1].
Define the inverse of the c.d.f. as F− (u) = min(x|F(x) ≥ u), which is just the usual inverse function of F if F is continuous. F− is often called the quantile function of X. If U has a uniform distribution on [0, 1], then F− (U) is distributed as X with c.d.f. F. Given some way of generating uniform random deviates, this provides a method for generating random variables from any distribution with a computable F−.
Index
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Appendix C - Random number generation
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Summary
Chapter 6, in particular, took it for granted that we can produce random numbers from various distributions. Actually we can't. The best that can be done is to produce a completely deterministic sequence of numbers that appears indistinguishable from a random sequence with respect to any relevant statistical property that we choose to test. In other words, we may be able to produce a deterministic sequence of numbers that can be very well modelled as being a random sequence from some distribution. Such deterministic sequences are referred to as sequences of pseudorandom numbers, but the pseudo part usually gets dropped at some point.
The fundamental problem, for our purposes, is to generate a pseudorandom sequence that can be extremely well modelled as i.i.d. U(0, 1). Given such a sequence, it is fairly straightforward to generate deviates from other distributions, but the i.i.d. U(0, 1) generation is where the problems lie. Indeed if you read around this topic, most books will largely agree about how to turn uniform random deviates into deviates from a huge range of other distributions, but advice on how to obtain the uniform deviates in the first place is much less consistent.
Simple generators and what can go wrong
Since the 1950s there has been much work on linear congruential generators. The intuitive motivation is something like this. Suppose I take an integer, multiply it by some enormous factor, rewrite it in base – ‘something huge’, and then throw away everything except for the digits after the decimal point. Pretty hard to predict the result, no? So, if I repeat the operation, feeding each step's output into the input for the next step, a more or less random sequence might result. Formally the pseudorandom sequence is defined by
Xi+1 = (aXi + b)modM,
where b is 0 or 1, in practice. This is started with a seed X0.
Contents
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References
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2 - Statistical models and inference
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Statistics aims to extract information from data: specifically, information about the system that generated the data. There are two difficulties with this enterprise. First, it may not be easy to infer what we want to know from the data that can be obtained. Second, most data contain a component of random variability: if we were to replicate the data-gathering process several times we would obtain somewhat different data on each occasion. In the face of such variability, how do we ensure that the conclusions drawn from a single set of data are generally valid, and not a misleading reflection of the random peculiarities of that single set of data?
Statistics provides methods for overcoming these difficulties and making sound inferences from inherently random data. For the most part this involves the use of statistical models, which are like ‘mathematical cartoons’ describing how our data might have been generated, if the unknown features of the data-generating system were actually known. So if the unknowns were known, then a decent model could generate data that resembled the observed data, including reproducing its variability under replication. The purpose of statistical inference is then to use the statistical model to go in the reverse direction: to infer the values of the model unknowns that are consistent with observed data.
Mathematically, let y denote a random vector containing the observed data. Let θ denote a vector of parameters of unknown value. We assume that knowing the values of some of these parameters would answer the questions of interest about the system generating y. So a statistical model is a recipe by which y might have been generated, given appropriate values for θ. At a minimum the model specifies how data like y might be simulated, thereby implicitly defining the distribution of y and how it depends on θ. Often it will provide more, by explicitly defining the p.d.f. of y in terms of θ.
6 - Bayesian computation
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5 - Numerical maximum likelihood estimation
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Contributors
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- By Zachary W. Adams, Margarita Alegría, Atalay Alem, Jordi Alonso, Victor Aparicio, Rifat Atun, Florence Baingana, Emily Baron, Marco Bertelli, Dinesh Bhugra, Sanchita Biswas, José Miguel Caldas de Almeida, Edwin Cameron, Somnath Chatterji, Erminia Colucci, Janice L. Cooper, Carla Kmett Danielson, Diego De Leo, Mary-Jo DelVecchio Good, Marten W. de Vries, Maureen S. Durkin, Xiangming Fang, Julia W. Felton, Sally Field, Andrea Fiorillo, Lance Gable, Teddy Gafna, Sandro Galea, Patrick Gatonga, Sofia Halperin-Goldstein, Yanling He, Grace A. Herbert, Sabrina Hermosilla, Simone Honikman, Takashi Izutsu, Ruwan M. Jayatunge, Janis H. Jenkins, Rachel Jenkins, Lynne Jones, Jayanthi Karunaratne, Ronald C. Kessler, Rob Keukens, Lincoln I. Khasakhala, Hanna Kienzler, Sarah Kippen Wood, M. Thomas Kishore, Robert Kohn, Natasja Koitzsch Jensen, Sheri Lapatin, Anna Lessios, Isabel Louro Bernal, Feijun Luo, Laura MacPherson, Matthew J. Maenner, Anne W. Mbwayo, David McDaid, Ingrid Meintjes, Victoria N. Mutiso, David M. Ndetei, Samuel O. Okpaku, Lijing Ouyang, Ramachandran Padmavati, Clare Pain, Duncan Pedersen, Jordan Pfau, Felipe Picon, Rodney D. Presley, Reima Pryor, Shoba Raja, Thara Rangaswamy, Jorge Rodriguez, Diana Rose, Moosa Salie, Norman Sartorius, Ester Shapiro, Manuela Silva, Daya Somasundaram, Katherine Sorsdahl, Dan J. Stein, Deborah M. Stone, Heather Stuart, Athula Sumathipala, Hema Tharoor, Rita Thom, Lay San Too, Atsuro Tsutsumi, Chris Underhill, Anne Valentine, Claire van der Westhuizen, Thandi van Heyningen, Robert van Voren, Inka Weissbecker, Gail Wyatt
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- Essentials of Global Mental Health
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- 05 March 2014
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- 27 February 2014, pp x-xiv
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Contributors
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- By Graeme J.M. Alexander, Heung Bae Kim, Michael Burch, Andrew J. Butler, Tanveer Butt, Roy Calne, Edward Cantu, Robert B. Colvin, Paul Corris, Charles Crawley, Hiroshi Date, Francis L. Delmonico, Bimalangshu R. Dey, Kate Drummond, John Dunning, John D. Firth, John Forsythe, Simon M. Gabe, Robert S. Gaston, William Gelson, Paul Gibbs, Alex Gimson, Leo C. Ginns, Samuel Goldfarb, Ryoichi Goto, Walter K. Graham, Simon J.F. Harper, Koji Hashimoto, David G. Healy, Hassan N. Ibrahim, David Ip, Fadi G. Issa, Neville V. Jamieson, David P. Jenkins, Dixon B. Kaufman, Kiran K. Khush, Heung Bae Kim, Andrew A. Klein, John Klinck, Camille Nelson Kotton, Vineeta Kumar, Yael B. Kushner, D. Frank. P. Larkin, Clive J. Lewis, Yvonne H. Luo, Richard S. Luskin, Ernest I. Mandel, James F. Markmann, Lorna Marson, Arthur J. Matas, Mandeep R. Mehra, Stephen J. Middleton, Giorgina Mieli-Vergani, Charles Miller, Sharon Mulroy, Faruk Özalp, Can Ozturk, Jayan Parameshwar, J.S. Parmar, Hari K. Parthasarathy, Nick Pritchard, Cristiano Quintini, Axel O. Rahmel, Chris J. Rudge, Stephan V.B. Schueler, Maria Siemionow, Jacob Simmonds, Peter Slinger, Thomas R. Spitzer, Stuart C. Sweet, Nina E. Tolkoff-Rubin, Steven S.L. Tsui, Khashayar Vakili, R.V. Venkateswaran, Hector Vilca-Melendez, Vladimir Vinarsky, Kathryn J. Wood, Heidi Yeh, David W. Zaas, Jonathan G. Zaroff
- Edited by Andrew A. Klein, Clive J. Lewis, Joren C. Madsen
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- Organ Transplantation
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- 07 September 2011
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Contributors
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- By Isabella Aboderin, W. Andrew Achenbaum, Katherine R. Allen, Toni C. Antonucci, Sara Arber, Claudine Attias‐Donfut, Paul B. Baltes, Sandhi Maria Barreto, Vern L. Bengtson, Simon Biggs, Joanna Bornat, Julie B. Boron, Mike Boulton, Clive E. Bowman, Marjolein Broese van Groenou, Edna Brown, Robert N. Butler, Bill Bytheway, Neena L. Chappell, Neil Charness, Kaare Christensen, Peter G. Coleman, Ingrid Arnet Connidis, Neal E. Cutler, Sara J. Czaja, Svein Olav Daatland, Lia Susana Daichman, Adam Davey, Bleddyn Davies, Freya Dittmann‐Kohli, Glen H. Elder, Carroll L. Estes, Mike Featherstone, Amy Fiske, Alexandra Freund, Daphna Gans, Linda K. George, Roseann Giarrusso, Chris Gilleard, Jay Ginn, Edlira Gjonça, Elena L. Grigorenko, Jaber F. Gubrium, Sarah Harper, Jutta Heckhausen, Akiko Hashimoto, Jon Hendricks, Mike Hepworth, Charlotte Ikels, James S. Jackson, Yuri Jang, Bernard Jeune, Malcolm L. Johnson, Randi S. Jones, Alexandre Kalache, Robert L. Kane, Rosalie A. Kane, Ingrid Keller, Rose Anne Kenny, Thomas B. L. Kirkwood, Kees Knipscheer, Martin Kohli, Gisela Labouvie‐Vief, Kristina Larsson, Shu‐Chen Li, Charles F. Longino, Ariela Lowenstein, Erick McCarthy, Gerald E. McClearn, Brendan McCormack, Elizabeth MacKinlay, Alfons Marcoen, Michael Marmot, Tom Margrain, Victor W. Marshall, Elizabeth A. Maylor, Ruud ter Meulen, Harry R. Moody, Robert A. Neimeyer, Demi Patsios, Margaret J. Penning, Stephen A. Petrill, Chris Phillipson, Leonard W. Poon, Norella M. Putney, Jill Quadagno, Pat Rabbitt, Jennifer Reid Keene, Sandra G. Reynolds, Steven R. Sabat, Clive Seale, Merril Silverstein, Hannes B. Staehelin, Ursula M. Staudinger, Robert J. Sternberg, Debra Street, Philip Taylor, Fleur Thomése, Mats Thorslund, Jinzhou Tian, Theo van Tilburg, Fernando M. Torres‐Gil, Josy Ubachs‐Moust, Christina Victor, K. Warner Shaie, Anthony M. Warnes, James L. Werth, Sherry L. Willis, François‐Charles Wolff, Bob Woods
- Edited by Malcolm L. Johnson, University of Bristol
- Edited in association with Vern L. Bengtson, University of Southern California, Peter G. Coleman, University of Southampton, Thomas B. L. Kirkwood, University of Newcastle upon Tyne
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- Book:
- The Cambridge Handbook of Age and Ageing
- Published online:
- 05 June 2016
- Print publication:
- 01 December 2005, pp xii-xvi
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