Tuning The A-Series Engine

Drawing on more than 30 years of experience in engine research and development, backed up with a computer-age workshop, David Vizard gives a comprehensive guide to tuning the A-series engine for either performance or economy. He provides the very latest technical data in the third edition of this best-seller, which has nearly 750 illustrations and an easy-to-follow text. ‘remains the essential work’ Classics‘a spectacularly detailed piece of work’ Cars & Car Conversions‘regarded as the essential reference’ Safety Fast

Daniel has been developing his own car for years and, through this work, has learned much about the practical problems of tuning and modifying cars. A current and former contributor to numerous magazines inc: Top Car, Fast Car, MG Enthusiast, Track & Race Car, Grassroots Motorsport, & British car, Daniel, after a stint in the RAF, is currently a civil servant. He lives in Nuneaton, England. Recently Daniel has completed over two years of training to become a licensed Motor Sport Association (MSA) Scrutineer and officiates at diverse race meetings throughout the Midlands.

Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to understand the underlying issues and difficulties that come about from missing data and their impact on subsequent analysis. There has been a great deal written on the theory developed for analyzing missing data for finite-dimensional parametric models. This includes an extensive literature on likelihood-based methods and multiple imputation. More recently, there has been increasing interest in semiparametric models which, roughly speaking, are models that include both a parametric and nonparametric component. Such models are popular because estimators in such models are more robust than in traditional parametric models. The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject. This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is at a level that is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible. Anastasios A. Tsiatis is the Drexel Professor of Statistics at North Carolina State University. His research has focused on developing statistical methods for the design and analysis of clinical trials, censored survival analysis, group sequential methods, surrogate markers, semiparametric methods with missing and censored data and causal inference and has been the major Ph.D. advisor for more than 30 students working in these areas. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the recipient of the Spiegelman Award and the Snedecor Award. He has been an Associate Editor of the Annals of Statistics and Statistics and Probability Letters and is currently an Associate Editor for Biometrika.

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