Extremizing and Antiextremizing in Bayesian Ensembles of Binary-Event Forecasts

2022 ◽  
Author(s):  
Kenneth C. Lichtendahl ◽  
Yael Grushka-Cockayne ◽  
Victor Richmond Jose ◽  
Robert L. Winkler
Keyword(s):  
Base Rate ◽  
New Method ◽  
Data Sets ◽  
Business Decisions ◽  
Binary Event ◽  
Average Probability

Many organizations combine forecasts of probabilities of binary events to support critical business decisions, such as the approval of credit or the recommendation of a drug. To aggregate individual probabilities, we offer a new method based on Bayesian principles that can help identify why and when combined probabilities need to be extremized. Extremizing is typically viewed as shifting the average probability farther from one half; we emphasize that it is more suitable to define extremizing as shifting it farther from the base rate. We introduce the notion of antiextremizing, cases in which it might be beneficial to make average probabilities less extreme. Analytically, we find that our Bayesian ensembles often extremize the average forecast but sometimes antiextremize instead. On several publicly available data sets, we demonstrate that our Bayesian ensemble performs well and antiextremizes anywhere from 18% to 73% of the cases. Antiextremizing is required more often when there is bracketing with respect to the base rate among the probabilities being aggregated than with no bracketing.

scholarly journals A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Growth Regulation ◽  
Pattern Analysis ◽  
Developmental Time ◽  
Dynamical Stability ◽  
New Method ◽  
Data Sets ◽  
Data Set ◽  
Landmark Data ◽  
Study Designs ◽  
Changes Over Time

AbstractA matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

A New Method for Characterizing Replacement Rate Variation in Molecular Sequences: Application of the Fourier and Wavelet Models to Drosophila and Mammalian Proteins

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 381-395
Author(s):  
Pavel Morozov ◽  
Tatyana Sitnikova ◽  
Gary Churchill ◽  
Francisco José Ayala ◽  
Andrey Rzhetsky
Keyword(s):  
Wavelet Transforms ◽  
Parametric Bootstrap ◽  
Real Data ◽  
New Method ◽  
Rate Variation ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Ratio Test ◽  
Replacement Rate ◽  
New Models

Abstract We propose models for describing replacement rate variation in genes and proteins, in which the profile of relative replacement rates along the length of a given sequence is defined as a function of the site number. We consider here two types of functions, one derived from the cosine Fourier series, and the other from discrete wavelet transforms. The number of parameters used for characterizing the substitution rates along the sequences can be flexibly changed and in their most parameter-rich versions, both Fourier and wavelet models become equivalent to the unrestricted-rates model, in which each site of a sequence alignment evolves at a unique rate. When applied to a few real data sets, the new models appeared to fit data better than the discrete gamma model when compared with the Akaike information criterion and the likelihood-ratio test, although the parametric bootstrap version of the Cox test performed for one of the data sets indicated that the difference in likelihoods between the two models is not significant. The new models are applicable to testing biological hypotheses such as the statistical identity of rate variation profiles among homologous protein families. These models are also useful for determining regions in genes and proteins that evolve significantly faster or slower than the sequence average. We illustrate the application of the new method by analyzing human immunoglobulin and Drosophilid alcohol dehydrogenase sequences.

scholarly journals A new method for measuring the distribution consistency of mixed-attribute data sets

2021 ◽  
Vol 38 (02) ◽  
pp. 170-179
Author(s):  
Yulin HE ◽  
Yi JIN ◽  
Dexin DAI ◽  
Baihao HUANG ◽  
Jiajie HUANG
Keyword(s):  
New Method ◽  
Data Sets ◽  
Attribute Data

scholarly journals Using control charts for on-line video summarisation

2019 ◽  
Vol 277 ◽  
pp. 01012 ◽  
Author(s):  
Clare E. Matthews ◽  
Paria Yousefi ◽  
Ludmila I. Kuncheva
Keyword(s):  
Feature Extraction ◽  
Control Chart ◽  
Control Charts ◽  
State Of The Art ◽  
Synthetic Data ◽  
New Method ◽  
Data Sets ◽  
On Line ◽  
Memory Constraints ◽  
Video Summarisation

Many existing methods for video summarisation are not suitable for on-line applications, where computational and memory constraints mean that feature extraction and frame selection must be simple and efficient. Our proposed method uses RGB moments to represent frames, and a control-chart procedure to identify shots from which keyframes are then selected. The new method produces summaries of higher quality than two state-of-the-art on-line video summarisation methods identified as the best among nine such methods in our previous study. The summary quality is measured against an objective ideal for synthetic data sets, and compared to user-generated summaries of real videos.

scholarly journals More faithfulness graph embedding

2015 ◽  
Vol 4 (2) ◽  
pp. 336
Author(s):  
Alaa Najim
Keyword(s):  
Dimensionality Reduction ◽  
Graph Embedding ◽  
New Method ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Visualization ◽  
Graph Data ◽  
Original Space ◽  
Data Points ◽  
Effectiveness And Efficiency

<p><span lang="EN-GB">Using dimensionality reduction idea to visualize graph data sets can preserve the properties of the original space and reveal the underlying information shared among data points. Continuity Trustworthy Graph Embedding (CTGE) is new method we have introduced in this paper to improve the faithfulness of the graph visualization. We will use CTGE in graph field to find new understandable representation to be more easy to analyze and study. Several experiments on real graph data sets are applied to test the effectiveness and efficiency of the proposed method, which showed CTGE generates highly faithfulness graph representation when compared its representation with other methods.</span></p>

scholarly journals Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Dong Liang ◽  
Chen Qiao ◽  
Zongben Xu
Keyword(s):  
Manifold Learning ◽  
Computational Efficiency ◽  
New Method ◽  
Data Sets ◽  
Learning Approaches ◽  
Real World Data ◽  
Efficiency Property ◽  
Data Points ◽  
Low Dimensional ◽  
High Computational Efficiency

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically.

Westermann Morphospace displays ammonoid shell shape and hypothetical paleoecology

Paleobiology ◽  
2012 ◽  
Vol 38 (3) ◽  
pp. 424-446 ◽  
Author(s):  
Kathleen A. Ritterbush ◽  
David J. Bottjer
Keyword(s):  
Temporal Variation ◽  
Water Column ◽  
Ternary Diagram ◽  
New Method ◽  
Shell Shape ◽  
Ontogenetic Variation ◽  
Data Sets ◽  
Intraspecies Variation ◽  
Ideal Tool

The Westermann Morphospace method displays fundamental morphotypes and hypothesized life modes of measured ammonoid fossils in a ternary diagram. It quantitatively describes shell shape, without assumption of theoretical coiling laws, in a single, easy-to-read diagram. This allows direct comparison between data sets presented in Westermann Morphospace, making it an ideal tool to communicate morphology. By linking measured shells to hypothesized life modes, the diagram estimates ecospace occupation of the water column. Application of this new method is demonstrated with Mesozoic data sets from monographs. Temporal variation, intraspecies variation, and ontogenetic variation are considered. This method can address hypothetical ecospace occupation in collections with tight stratigraphic, lithologic, and abundance control, even when taxonomy is in dispute.

scholarly journals Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Rodney A Sparapani ◽  
Lisa E Rein ◽  
Sergey S Tarima ◽  
Tourette A Jackson ◽  
John R Meurer
Keyword(s):  
Survival Analysis ◽  
Recurrent Events ◽  
Hospital Admissions ◽  
Simulated Data ◽  
New Method ◽  
Data Sets ◽  
Single Event ◽  
Patients With Diabetes ◽  
Additive Regression ◽  
Simulated Data Sets

Summary Much of survival analysis is concerned with absorbing events, i.e., subjects can only experience a single event such as mortality. This article is focused on non-absorbing or recurrent events, i.e., subjects are capable of experiencing multiple events. Recurrent events have been studied by many; however, most rely on the restrictive assumptions of linearity and proportionality. We propose a new method for analyzing recurrent events with Bayesian Additive Regression Trees (BART) avoiding such restrictive assumptions. We explore this new method via a motivating example of hospital admissions for diabetes patients and simulated data sets.

Beamtrees: Compact Visualization of Large Hierarchies

2003 ◽  
Vol 2 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Frank van Ham ◽  
Jarke J. van Wijk
Keyword(s):  
Hierarchical Structure ◽  
User Study ◽  
Three Dimensional ◽  
New Method ◽  
Data Sets ◽  
Two Dimensional ◽  
Hierarchical Data ◽  
Organization Structures ◽  
The Hierarchical Structure

Beamtrees are a new method for the visualization of large hierarchical data sets, such as directory structures and organization structures. Nodes are shown as stacked circular beams such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. Both a two-dimensional and a three-dimensional variant are presented. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

scholarly journals An ɴ-ary λ-averaging based similarity classifier

2016 ◽  
Vol 26 (2) ◽  
pp. 407-421 ◽  
Author(s):  
Onesfole Kurama ◽  
Pasi Luukka ◽  
Mikael Collan
Keyword(s):  
Natural Extension ◽  
Medical Data ◽  
New Method ◽  
Data Sets ◽  
Owa Operator ◽  
Averaging Operator

Abstract We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.

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