scholarly journals COVID ‐19 and immunosuppressants: An opinion pool of practicing dermatologists of India

2020 ◽  
Vol 33 (6) ◽  
Author(s):  
Arunima Ray ◽  
Ipsita Debata ◽  
Ishan Agrawal ◽  
Maitreyee Panda
Keyword(s):  
Opinion Pool

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

scholarly journals The Opinion Pool

1961 ◽  
Vol 32 (4) ◽  
pp. 1339-1342 ◽  
Author(s):  
M. Stone
Keyword(s):  
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.

scholarly journals Large Number of Rare Events: Diversity Analysis in Multiple Choice Questionnaires and Related Topics

2021 ◽  
Author(s):  
◽  
Giorgi Kvizhinadze
Keyword(s):  
Statistical Theory ◽  
Limit Theorems ◽  
Edgeworth Expansion ◽  
Rare Events ◽  
Large Deviation ◽  
Diversity Analysis ◽  
Almost Everywhere ◽  
Large Numbers ◽  
The Subject ◽  
Opinion Pool

<p>The statistical analysis of a large number of rare events, (LNRE), which can also be called statistical theory of diversity, is the subject of acute interest both in statistical theory and in numerous applications. A careful eye will quickly see the presence of a large number of very rare objects almost everywhere: large numbers of rare species in ecosystems, large numbers of rare opinions in any opinion pool, large numbers of small admixtures in any solution and large numbers of rare words in any text are only few examples. In studying such objects, the interest for mathematical statisticians lies in the fact that most of the frequencies are small and, therefore, difficult to deal with. It is not immediately clear how one should be able to derive consistent and reliable inference from a large number of such frequencies. In this thesis we study the diversity of questionnaires with multiple answers. It has been demonstrated that this is a particular model of LNRE theory. In our analysis, the theories of large deviation, contiguity and Edgeworth expansion were employed, and limit theorems have been established.</p>

A telemedicine communication exchange network in Gorno-Badakhshan Autonomic Oblast in collaboration with Swiss Surgical Teams

2020 ◽  
pp. 1357633X2094898
Author(s):  
Intizor Mamadnabiev ◽  
Stephan Imfeld ◽  
Lorenz Gürke ◽  
Markus Aschwanden ◽  
Andrej Isaak
Keyword(s):  
Response Times ◽  
Remote Area ◽  
Second Opinion ◽  
Treatment Quality ◽  
Tertiary Referral Hospital ◽  
Vascular Surgeon ◽  
Surgical Teams ◽  
Exchange Network ◽  
To Receive ◽  
Opinion Pool

Introduction The purpose of this article is to report on the implementation of a telemedicine network serving as a second opinion pool for a surgeon in a remote area of a developing country. Methods This study involved an international collaboration between two members of Swiss Surgical Teams at a tertiary referral hospital and a surgeon in a remote area in Gorno-Badakhshan Autonomic Oblast, Tajikistan, which established a second opinion pool discussing diagnostics and therapeutic options via a messenger application. A retrospective analysis of response times was performed using a series of 50 challenging cases. Results The median time to receive a first telemedical response from any of the two contacts was 24 min (interquartile range 6–73). Urgent and emergent pathologies accounted for 57% of cases. The suggested treatment was carried out in 90% ( n = 44) of cases. Conclusions Timely and convenient telemedicine support to provide diagnostic and therapeutic reassurance and improve treatment quality for patients presenting to a general and vascular surgeon in the large and remote region of Gorno-Badakhshan Autonomic Oblast can be installed via a messenger application.

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

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.

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