Stochastic dominance and mean-standard deviation analysis: some critical issues

1995 ◽  
Vol 46 (7) ◽  
pp. 1487 ◽  
Author(s):  
KA Parton ◽  
PS Carberry
Keyword(s):  
Standard Deviation ◽  
Stochastic Dominance ◽  
Probability Distributions ◽  
Dominance Analysis ◽  
Deviation Analysis ◽  
Critical Issues ◽  
Effective Procedures

Stochastic dominance and mean-standard deviation analysis can be effective procedures for ranking risky alternatives that are expressed in terms of probability distributions of outcomes. However, the conditions applying to their use and their limitations need to be understood. These are set out in the paper, together with an extension that overcomes some constraints to the use of stochastic dominance analysis.

A Stochastic Dominance Analysis of Alternative Marketing Strategies for Mixed Crop Farms in North Florida

1986 ◽  
Vol 18 (2) ◽  
pp. 257-266 ◽  
Author(s):  
Kwabena A. Anaman ◽  
William G. Boggess
Keyword(s):  
Stochastic Dominance ◽  
Futures Markets ◽  
Probability Distributions ◽  
Marketing Strategies ◽  
Cumulative Probability ◽  
Risk Averse ◽  
Management Scenarios ◽  
Dominance Analysis ◽  
Mixed Crop ◽  
Forward Contracting

AbstractCumulative probability distributions of income for management scenarios involving four preharvest marketing strategies are subjected to stochastic dominance analysis to determine risk-efficient sets of strategies for different groups of farmers in North Florida. Results indicate that farmers should behave differently in their choice of marketing strategies according to their risk attitudes. Highly risk-averse farmers should prefer some forward contracting while low risk-averse and risk-loving farmers should prefer cash sales at harvest. Use of the futures markets leads to both higher income and greater risk than forward contracting but lower income and risk than cash sales.

Exploring multi-modalities in weather prediction using a univariate graph based on machine learning techniques

2021 ◽  
Author(s):  
Natacha Galmiche ◽  
Nello Blaser ◽  
Morten Brun ◽  
Helwig Hauser ◽  
Thomas Spengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Probability Distributions ◽  
Weather Prediction ◽  
A Priori ◽  
Clustering Algorithms ◽  
Quantitative Information ◽  
Machine Learning Techniques ◽  
Topological Data Analysis ◽  
Learning Techniques

<p>Probability distributions based on ensemble forecasts are commonly used to assess uncertainty in weather prediction. However, interpreting these distributions is not trivial, especially in the case of multimodality with distinct likely outcomes. The conventional summary employs mean and standard deviation across ensemble members, which works well for unimodal, Gaussian-like distributions. In the case of multimodality this misleads, discarding crucial information. </p><p>We aim at combining previously developed clustering algorithms in machine learning and topological data analysis to extract useful information such as the number of clusters in an ensemble. Given the chaotic behaviour of the atmosphere, machine learning techniques can provide relevant results even if no, or very little, a priori information about the data is available. In addition, topological methods that analyse the shape of the data can make results explainable.</p><p>Given an ensemble of univariate time series, a graph is generated whose edges and vertices represent clusters of members, including additional information for each cluster such as the members belonging to them, their uncertainty, and their relevance according to the graph. In the case of multimodality, this approach provides relevant and quantitative information beyond the commonly used mean and standard deviation approach that helps to further characterise the predictability.</p>

Stochastic Dominance Analysis for More Profitable and Less Risky Irrigation of Corn

jpa ◽  
1992 ◽  
Vol 5 (2) ◽  
pp. 243-247 ◽  
Author(s):  
J.E. Epperson ◽  
H.E. Hook ◽  
Y.R. Mustafa
Keyword(s):  
Stochastic Dominance ◽  
Dominance Analysis

scholarly journals Northern Hemisphere Climatology and Trends of Statistical Moments Documented from GHCN-Daily Surface Air Temperature Station Data from 1950 to 2010

2014 ◽  
Vol 27 (14) ◽  
pp. 5396-5410 ◽  
Author(s):  
Nicholas R. Cavanaugh ◽  
Samuel S. P. Shen
Keyword(s):  
Standard Deviation ◽  
Northern Hemisphere ◽  
Spatial Patterns ◽  
Air Temperature ◽  
Probability Distributions ◽  
Temporal Trends ◽  
Surface Air Temperature ◽  
Statistical Moments ◽  
Station Data ◽  
Skewness And Kurtosis

Abstract The first four statistical moments and their trends are calculated for the average daily surface air temperature (SAT) from 1950 to 2010 using the Global Historical Climatology Network–Daily station data for each season relative to the 1961–90 climatology over the Northern Hemisphere. Temporal variation of daily SAT probability distributions are represented as generalized linear regression coefficients on the mean, standard deviation, skewness, and kurtosis calculated for each 10-yr moving time window from 1950–59 to 2001–10. The climatology and trends of these statistical moments suggest that daily SAT probability distributions are non-Gaussian and are changing in time. The climatology of the first four statistical moments has distinct spatial patterns with large coherent structure for mean and standard deviation and relatively smaller and more regionalized patterns for skewness and kurtosis. The linear temporal trends from 1950 to 2010 of the first four moments also have coherent spatial patterns. The linear temporal trends in the characterizing statistical moments are statistically significant at most locations and have differing spatial patterns for different moments. The regionalized variations specific to higher moments may be related to the climate dynamics that contribute to extremes. The nonzero skewness and kurtosis makes this detailed documentation on the higher statistical moments useful for quantifying climate changes and assessing climate model uncertainties.

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