Essentials of Pediatric Radiology: A Multimodality Approach provides a concise overview of both basic and complex topics encountered by pediatric radiologists in their daily practice. Written by leading pediatric radiologists from renowned children's hospitals, it focuses particularly on multimodality imaging, covering the full gamut of radiologic diagnostic techniques, including conventional radiography and ultrasound, Doppler ultrasound, up-to-date CT and MRI techniques, and PET-CT. Each chapter is generously illustrated with high quality images, as well as graphs, tables, decision flowcharts and featured cases. Chapters are arranged according to pathologies, rather than organ systems, providing the reader with clinically-oriented information when employing 'whole body' techniques or analysing scans involving multiple anatomical sites. The book is complemented by an outstanding free access website of sample cases containing questions and answers that enable readers to test their diagnostic proficiency - see http://essentials-of-pediatric-radiology.com. A key text for pediatric radiology fellows, radiology residents and general radiologists, this is also essential reading for all pediatricians.
With the substantial advances in the miniaturization of electronic components, wildlife biologists now routinely monitor the movements of free-ranging animals with radio-tracking devices. This book explicates the many analytical techniques and computer programs available to extract biological information from the radio tracking data.
Radio Glow World is the latest gripping novel by the renowned Australian author Doug Moody. Doug has perfectly captured the true essence and spirit of the golden era of radio broadcasting. Prepare to be transported back to a time before loud mouthed opinionated Disc Jockeys and head banging music capable of bursting your ear drums began to pollute the airwaves. Radio Glow World - a magical place where your imagination can run free and where the best of British music is always played. We meet Blake Winton, a flawed man who is haunted by spectres from his past and present. Will he secure a future in the community radio world after being taken under the wing of a veteran of the industry, or will he find himself being the victim of a bludging old fool. What is the unsavoury Black Barry Hughes plotting? Will a major terrorist attack hit Sydney and why are a group of American radio executives sniffing around? All will be revealed in this captivating saga about the wonderful era when the wireless ruled supreme.
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality.
Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful.
Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
In clear, easy-to-grasp language, the author covers many of the topics that you will need to know in order to win your dream job and be the first in line for a promotion.