What is the real difference between competent leader and extraordinary executive? Is it pedigree, experience, intelligence? The answer is yes...and much more. Exceptional leadership hinges on a complex interaction between individual psychology and unique business needs. At the top rung of the ladder, where the dynamics are most complicated, subtle adjustments in style can produce outstanding results.
In his new book, The Intangibles of Leadership, Management Psychologist Richard Davis, Ph.D., uncovers patterns in the attributes that truly distinguish those who succeed at the top. What he found was that extraordinary leaders possess certain characteristics that fall between the lines of existing leadership models, yet are fundamental to executive success. Davis explains each of these qualities, the people who exemplify them, how to detect them in others, and most importantly, how to develop the subtle characteristics that will enable them to stand out from the pack.
RICHARD A. DAVIS, Ph.D., is an Industrial/Organizational Psychologist and partner at the Toronto office of RHR International. Dr. Davis is called upon by senior leaders to help execute their business strategy through smart decisions about people. He advises executives on senior hiring decisions, integrating leaders into new roles, CEO succession, optimizing teams, M&A decisions and increasing leadership effectiveness. He has a particular specialty in personality psychology and leadership.
Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series.
Explore a Balanced Treatment of Frequentist and Bayesian Perspectives
Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series.
Get Guidance from Masters in the Field
Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting.