Allocating the weights in the linear opinion pool

1990 ◽  
Vol 9 (1) ◽  
pp. 53-73 ◽  
Author(s):  
Christian Genest ◽  
Kevin J. McConway
Keyword(s):  
Linear Opinion Pool ◽  
Opinion Pool

scholarly journals Forecast densities for economic aggregates from disaggregate ensembles

2014 ◽  
Vol 18 (4) ◽  
Author(s):  
Francesco Ravazzolo ◽  
Shaun P. Vahey
Keyword(s):  
Moving Average ◽  
Forecast Combination ◽  
Consumption Expenditure ◽  
Post Processing ◽  
Continuous Ranked Probability Score ◽  
Personal Consumption ◽  
Linear Opinion Pool ◽  
Processing Step ◽  
Density Forecasting ◽  
Opinion Pool

AbstractWe extend the “bottom up” approach for forecasting economic aggregates with disaggregates to probability forecasting. Our methodology utilises a linear opinion pool to combine the forecast densities from many disaggregate forecasting specifications, using weights based on the continuous ranked probability score. We also adopt a post-processing step prior to forecast combination. These methods are adapted from the meteorology literature. In our application, we use our approach to forecast US Personal Consumption Expenditure inflation from 1990q1 to 2009q4. Our ensemble combining the evidence from 16 disaggregate PCE series outperforms an integrated moving average specification for aggregate inflation in terms of density forecasting.

Methods For Combining Experts' Probability Assessments

1995 ◽  
Vol 7 (5) ◽  
pp. 867-888 ◽  
Author(s):  
Robert A. Jacobs
Keyword(s):  
Decision Maker ◽  
Probability Distributions ◽  
Upper Bounds ◽  
Bayes Rule ◽  
Bayesian Procedure ◽  
Lower And Upper Bounds ◽  
Aggregation Methods ◽  
Expert Opinions ◽  
Linear Opinion Pool ◽  
Opinion Pool

This article reviews statistical techniques for combining multiple probability distributions. The framework is that of a decision maker who consults several experts regarding some events. The experts express their opinions in the form of probability distributions. The decision maker must aggregate the experts' distributions into a single distribution that can be used for decision making. Two classes of aggregation methods are reviewed. When using a supra Bayesian procedure, the decision maker treats the expert opinions as data that may be combined with its own prior distribution via Bayes' rule. When using a linear opinion pool, the decision maker forms a linear combination of the expert opinions. The major feature that makes the aggregation of expert opinions difficult is the high correlation or dependence that typically occurs among these opinions. A theme of this paper is the need for training procedures that result in experts with relatively independent opinions or for aggregation methods that implicitly or explicitly model the dependence among the experts. Analyses are presented that show that m dependent experts are worth the same as k independent experts where k ≤ m. In some cases, an exact value for k can be given; in other cases, lower and upper bounds can be placed on k.

Calibration, sharpness and the weighting of experts in a linear opinion pool

2015 ◽  
Vol 229 (1) ◽  
pp. 429-450 ◽  
Author(s):  
Stephen C. Hora ◽  
Erim Kardeş
Keyword(s):  
Linear Opinion Pool ◽  
Opinion Pool

An ordinal characterization of the linear opinion pool

2006 ◽  
Vol 33 (3) ◽  
pp. 457-474 ◽  
Author(s):  
Christopher P. Chambers
Keyword(s):  
Linear Opinion Pool ◽  
Opinion Pool

A Review of Probabilistic Opinion Pooling Algorithms with Application to Insider Threat Detection

2020 ◽  
Vol 17 (1) ◽  
pp. 39-55
Author(s):  
Jared A. Beekman ◽  
Ronald F. A. Woodaman ◽  
Dennis M. Buede
Keyword(s):  
Research Effort ◽  
Ground Truth ◽  
Enterprise Modeling ◽  
Insider Threat ◽  
Threat Detection ◽  
Linear Opinion Pool ◽  
Research Sponsor ◽  
Forecast Models ◽  
Modeling Data ◽  
Opinion Pool

We retrospectively explore the effectiveness of various probabilistic opinion pools against a set of insider threat detection modeling data from a recently completed, multiyear, sponsored research effort. We explored four opinion pools: the linear opinion pool (likely the most popular), the beta-transformed linear opinion pool, the geometric opinion pool, and a multiplicative method based on odds called Bordley’s formula. The data for our study came from our recent work in the inference of insider threats for our research sponsor. In this work, we created a multimodeling inference enterprise modeling (MIEM) process to either predict threats within a population or, given the threats, predict how well the enterprise system can detect those threats. As part of larger research challenges designed by the research sponsor, we applied the MIEM process quarterly to respond to a sequence of varying challenge problems (CPs). Via MIEM, we developed multiple, independent computation forecast models. These models generated certainty intervals to answer CP questions. These intervals were fused into a single interval for each question via an expert panel prior to submission. The sponsors scored the responses against ground truth. In this paper, we (a) ask which pooling functions work best on these data and consider why, and (b) compare this performance to the actual submissions to determine if one of the pooling functions performed better than our judgment-based fusion.

scholarly journals How to choose a fair delegation?

2020 ◽  
Author(s):  
Burak Can ◽  
Péter Csóka ◽  
Emre Ergin
Keyword(s):  
Fixed Number ◽  
Pareto Optimal ◽  
Opinion Pool

Abstract This paper analyzes how to choose a delegation, a committee to represent a society such as in a peace conference. We propose normative conditions and seek Pareto optimal, consistent, neutral, and non-manipulable ways to choose a delegation. We show that a class of threshold rules is characterized by these criteria. The rules do not choose a fixed number of delegates, but instead require different sizes of delegations, depending on the heterogeneity in society. Therefore the resulting delegations are very inclusive, and with t delegates the ratio of individuals whose opinions are not included is always below "Equation missing". For instance, a delegation of size two should have at least 75% support from the society and therefore only less than 25% of the opinion pool can be neglected.

Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals

2019 ◽  
Vol 155 ◽  
pp. 187-205 ◽  
Author(s):  
Yiqing Guo ◽  
Xiuping Jia ◽  
David Paull ◽  
Jón Atli Benediktsson
Keyword(s):  
Opinion Pool
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