16 papers · ranked by Valyu relevance
Jonathan D. Cook, David M. Williams, Daniel P. Walsh, Trevor J. Hefley
'Trevor J. Hefley'] Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the…
Bruce M. Hill
This article develops new theory and methodology for the forecasting of extreme and/or record values in an exchangeable sequence of random variables. The Hill tail index estimator for long-tailed distributions is modified so as to be appropriate for prediction of future variables. Some basic issues regarding the use of…
Clark Kogan, Leonid Kalachev, Hans P. A. Van Dongen
In study designs with repeated measures for multiple subjects, population models capturing within- and between-subjects variances enable efficient individualized prediction of outcome measures (response variables) by incorporating individuals response data through Bayesian forecasting. When measurement constraints…
Jakub Bijak, John Bryant
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally…
Andrea Carriero, Todd E. Clark, Massimiliano Marcellino
Title: Summary The paper develops a method for producing current quarter forecasts of gross domestic product growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and…
Maximilian Zellner, Ali E. Abbas, David V. Budescu, Aram Galstyan
This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and…
Hannah E. Correia
Fisheries management is dominated by the need to forecast catch and abundance of commercially and ecologically important species. The influence of spatial information and environmental factors on forecasting error is not often considered. I propose a forecasting method called spatiotemporally explicit model averaging…
Zoubin Ghahramani
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous…
Gunther Eysenbach, Diego Hidalgo-Mazzei, Hossein Hassani, Michael Ostacher + 7 more
Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the…
J. Edward Russo
The rational status of the Bayesian calculus for revising likelihoods is compromised by the common but still unfamiliar phenomenon of information distortion. This bias is the distortion in the evaluation of a new datum toward favoring the currently preferred option in a decision or judgment. While the Bayesian calculus…
Eric-Jan Wagenmakers, Maarten Marsman, Tahira Jamil, Alexander Ly + 9 more
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic…
Patrick L. McDermott, Christopher K. Wikle, Joshua Millspaugh
Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical…
Massimiliano Giacalone
A well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451-468, 1969). The only case in which no improvements are possible is when one of the single forecasts is…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 6 more
'Tatsuji Takahashi' 'Hiroshi Okamoto' 'Yukio Pegio Gunji' 'Yoshihiro Nakajima' 'Shunji Mitsuyoshi' 'Enrico Scalas'] Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is…
Katie Steele, Charlotte Werndl
This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too…
Dag Tjøstheim, Andrea Murari
Machine learning forecasting methods are compared to more traditional parametric statistical models. This comparison is carried out regarding a number of different situations and settings. A survey of the most used parametric models is given. Machine learning methods, such as convolutional networks, TCNs, LSTM…