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History by numbers : an introduction to quantitative approaches
\"Fully updated and carefully revised, this new 2nd edition of History by Numbers still stands alone as the only textbook on quantitative methods suitable for students of history. Even the numerically challenged will find inspiration. Taking a problem-solving approach and using authentic historical data, it describes each method in turn, including its origin, purpose, usefulness and associated pitfalls. The problems are developed gradually and with narrative skill, allowing readers to experience the moment of discovery for each of the interpretative outcomes. Quantitative methods are essential for the modern historian, and this lively and accessible text will prove an invaluable guide for anyone entering the discipline\"-- Provided by publisher.
Book
Meta-Regression Analysis in Economics and Business
The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising \"Tower of Babel\" that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis-meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods.
eBook
Style, computers, and early modern drama : beyond authorship
\"Hugh Craig and Brett Greatley-Hirsch extend the computational analysis introduced in Shakespeare, Computers, and the Mystery of Authorship (edited by Hugh Craig and Arthur F. Kinney; Cambridge, 2009) beyond problems of authorship attribution to address broader issues of literary history. Using new methods to answer long-standing questions and challenge traditional assumptions about the underlying patterns and contrasts in the plays of Shakespeare and his contemporaries, Style, Computers, and Early Modern Drama sheds light on, for example, different linguistic usages between plays written in verse and prose, company styles and different character types. As a shift from a canonical survey to a corpus-based literary history founded on a statistical analysis of language, this book represents a fundamentally new approach to the study of English Renaissance literature and proposes a new model and rationale for future computational scholarship in early modern literary studies\"-- Provided by publisher.
Book
Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery
Wayne Powell and colleagues compare the different tools and approaches used by the plant breeding community versus the animal breeding community for crop and livestock improvement. They argue that the two disciplines can be united via adoption of genomic selection along with the exchange of resources and techniques between the two areas.
The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
Journal Article
Learning and Expectations in Macroeconomics
A crucial challenge for economists is figuring out how people interpret the world and form expectations that will likely influence their economic activity. Inflation, asset prices, exchange rates, investment, and consumption are just some of the economic variables that are largely explained by expectations. Here George Evans and Seppo Honkapohja bring new explanatory power to a variety of expectation formation models by focusing on the learning factor. Whereas the rational expectations paradigm offers the prevailing method to determining expectations, it assumes very theoretical knowledge on the part of economic actors. Evans and Honkapohja contribute to a growing body of research positing that households and firms learn by making forecasts using observed data, updating their forecast rules over time in response to errors. This book is the first systematic development of the new statistical learning approach.
Depending on the particular economic structure, the economy may converge to a standard rational-expectations or a \"rational bubble\" solution, or exhibit persistent learning dynamics. The learning approach also provides tools to assess the importance of new models with expectational indeterminacy, in which expectations are an independent cause of macroeconomic fluctuations. Moreover, learning dynamics provide a theory for the evolution of expectations and selection between alternative equilibria, with implications for business cycles, asset price volatility, and policy. This book provides an authoritative treatment of this emerging field, developing the analytical techniques in detail and using them to synthesize and extend existing research.
eBook
On the Use of Biomineral Oxygen Isotope Data to Identify Human Migrants in the Archaeological Record: Intra-Sample Variation, Statistical Methods and Geographical Considerations
Oxygen isotope analysis of archaeological skeletal remains is an increasingly popular tool to study past human migrations. It is based on the assumption that human body chemistry preserves the δ18O of precipitation in such a way as to be a useful technique for identifying migrants and, potentially, their homelands. In this study, the first such global survey, we draw on published human tooth enamel and bone bioapatite data to explore the validity of using oxygen isotope analyses to identify migrants in the archaeological record. We use human δ18O results to show that there are large variations in human oxygen isotope values within a population sample. This may relate to physiological factors influencing the preservation of the primary isotope signal, or due to human activities (such as brewing, boiling, stewing, differential access to water sources and so on) causing variation in ingested water and food isotope values. We compare the number of outliers identified using various statistical methods. We determine that the most appropriate method for identifying migrants is dependent on the data but is likely to be the IQR or median absolute deviation from the median under most archaeological circumstances. Finally, through a spatial assessment of the dataset, we show that the degree of overlap in human isotope values from different locations across Europe is such that identifying individuals' homelands on the basis of oxygen isotope analysis alone is not possible for the regions analysed to date. Oxygen isotope analysis is a valid method for identifying first-generation migrants from an archaeological site when used appropriately, however it is difficult to identify migrants using statistical methods for a sample size of less than c. 25 individuals. In the absence of local previous analyses, each sample should be treated as an individual dataset and statistical techniques can be used to identify migrants, but in most cases pinpointing a specific homeland should not be attempted.
Journal Article
Towards a rigorous understanding of societal responses to climate change
A large scholarship currently holds that before the onset of anthropogenic global warming, natural climatic changes long provoked subsistence crises and, occasionally, civilizational collapses among human societies. This scholarship, which we term the ‘history of climate and society’ (HCS), is pursued by researchers from a wide range of disciplines, including archaeologists, economists, geneticists, geographers, historians, linguists and palaeoclimatologists. We argue that, despite the wide interest in HCS, the field suffers from numerous biases, and often does not account for the local effects and spatiotemporal heterogeneity of past climate changes or the challenges of interpreting historical sources. Here we propose an interdisciplinary framework for uncovering climate–society interactions that emphasizes the mechanics by which climate change has influenced human history, and the uncertainties inherent in discerning that influence across different spatiotemporal scales. Although we acknowledge that climate change has sometimes had destructive effects on past societies, the application of our framework to numerous case studies uncovers five pathways by which populations survived—and often thrived—in the face of climatic pressures.
This Review proposes an interdisciplinary framework for researching climate–society interactions that focuses on the mechanisms through which climate change has influenced societies, and the uncertainties of discerning this influence across different spatiotemporal scales.
Journal Article
Predicting hospital admission at emergency department triage using machine learning
To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.
This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.
A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).
Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
Journal Article
A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications
Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).
Journal Article
ECONOMETRICS FOR DECISION MAKING
Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald.
Journal Article