Nicholas T. Longford Books

Focusing on the critical role of decision-making in both personal and professional contexts, this book positions statistical inference as essential to the decision-making process. It introduces a new paradigm for statistical practice, emphasizing how statistics can guide individuals in selecting the best course of action from various options. By integrating decision-making with statistical theory, it offers insights into improving choices in various aspects of life.

The book explores the application of statistical computing in survey analysis, specifically using R to analyze European Union statistics on income and living conditions. It emphasizes the creation of informative graphics and the development of advanced analytical and programming skills necessary for managing large-scale databases. Additionally, it highlights practical approaches to enhance data interpretation and presentation.

The book focuses on the intersection of missing data and small-area estimation, stemming from the author's experiences as a C-pion Fellow. It emphasizes the importance of integrating academic and industrial statistics to enhance government statistics. The author reflects on the collaborative efforts during lectures and workshops, acknowledging the contributions of colleagues and the supportive environment at Massey University. This work highlights the need for closer ties between academia and industry to drive progress in statistical research and applications.

Designed for graduate students and researchers, this textbook introduces a unique curriculum centered on fundamental statistical activities such as sampling, measurement, and inference. It aims to enhance understanding and application of social statistics, making it a valuable resource for those in related fields.

This monograph presents a radical rethinking of how elementary inferences should be made in statistics, implementing a comprehensive alternative to hypothesis testing in which the control of the probabilities of the errors is replaced by selecting the course of action (one of the available options) associated with the smallest expected loss. Its strength is that the inferences are responsive to the elicited or declared consequences of the erroneous decisions, and so they can be closely tailored to the client’s perspective, priorities, value judgments and other prior information, together with the uncertainty about them.

Focusing on the theme of inferring sources of variation and uncertainty, the author demonstrates how understanding these sources can enhance the estimation of key quantities. Key topics include essay rating, item-level property summarization, test equating, small-area estimation, and addressing incomplete longitudinal studies. The book is enriched with real data set examples that illustrate these applications, providing practical insights into the methodologies discussed.