scholarly journals Methods for Computing Marginal Data Densities from the Gibbs Output

2011 ◽  
Author(s):  
Cristina Fuentes-Albero ◽  
Leonardo Melosi
Keyword(s):  
Marginal Data

Production and Sales’ Volume Planning Based on Marginal Data Analysis

10.12737/7813 ◽  
2015 ◽  
Vol 3 (1) ◽  
pp. 51-56
Author(s):  
Аверина ◽  
Tatyana Averina
Keyword(s):  
Economic Theory ◽  
Regression Analysis ◽  
Marginal Cost ◽  
Comparison Method ◽  
Cost Comparison ◽  
Sales Volume ◽  
Marginal Revenue ◽  
Marginal Data ◽  
The Cost ◽  
Optimum Volume

Logical and convenient methods of economic theory often remain unclaimed in the area of management accounting and analysis. For example an obstacle to the use of marginal revenue and marginal cost comparison method may be difficulties associated with the assembly of these indicators’ equations. This paper contains an example of using regression analysis for separation the cost related to constant and variable components, for assembly of marginal data equations in order to determine the optimum volume of output.

Testing independence through conditional and marginal data

1982 ◽  
Vol 11 (3) ◽  
pp. 291-296
Author(s):  
Hanzoor Ahmad
Keyword(s):  
Testing Independence ◽  
Marginal Data

scholarly journals Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data

2019 ◽  
Vol 27 (3) ◽  
pp. 388-396 ◽  
Author(s):  
Devin Caughey ◽  
Mallory Wang
Keyword(s):  
Population Distribution ◽  
Auxiliary Information ◽  
Population Data ◽  
Target Population ◽  
Small Subset ◽  
Ecological Inference ◽  
Joint Distributions ◽  
Marginal Data ◽  
Time Periods ◽  
Over Time

Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables’ joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data—which variables are observed and whether their marginal or joint distributions are known—differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.

Multichannel L filters based on marginal data ordering

1994 ◽  
Vol 42 (10) ◽  
pp. 2581-2595 ◽  
Author(s):  
C. Kotropoulos ◽  
I. Pitas
Keyword(s):  
Marginal Data

scholarly journals Methods for computing marginal data densities from the Gibbs output

2013 ◽  
Vol 175 (2) ◽  
pp. 132-141 ◽  
Author(s):  
Cristina Fuentes-Albero ◽  
Leonardo Melosi
Keyword(s):  
Marginal Data

Estimating Nonnegative Matrices from Marginal Data

1965 ◽  
Vol 6 (3) ◽  
pp. 294 ◽  
Author(s):  
Michael Bacharach
Keyword(s):  
Nonnegative Matrices ◽  
Marginal Data

scholarly journals Marginal technology based on consequential life cycle assessment. The case of Colombia

2019 ◽  
pp. 51-61 ◽  
Author(s):  
Johan Andrés Vélez-Henao ◽  
Claudia María Garcia-Mazo
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Fossil Fuels ◽  
Power Grid ◽  
Key Factors ◽  
Consequential Life Cycle Assessment ◽  
Marginal Technology ◽  
National Power ◽  
Electricity Power ◽  
Marginal Data

Electricity data is one of the key factors in life cycle assessment (LCA). There are two different approaches to model electricity and to apply average or marginal data in LCA studies. Marginal data is used in consequential whereas average data is considered in attributional studies. The aim of this study is to provide the long-term marginal technology for electricity power generation in Colombia until 2030. This technology is one capable of responding to small changes in demand on the market and is an important issue when assessing the environmental impacts of providing electricity. Colombia is a developing country with a national power grid, which historically has been dominated by Hydropower rather than fossil fuels. This particularity makes Colombian national power grid vulnerable to climatic variations; therefore, the country needs to introduce renewable resources into the power grid. This study uses consequential life cycle assessment and data from Colombian national plans for capacity changes in the power grid. The results show that whereas marginal electricity technology would most probably be Hydropower, Wind and Solar power are projected to reach more than 1% of the national power grid by 2030.

A Bayesian analysis of the multinomial probit model using marginal data augmentation

2005 ◽  
Vol 124 (2) ◽  
pp. 311-334 ◽  
Author(s):  
Kosuke Imai ◽  
David A. van Dyk
Keyword(s):  
Bayesian Analysis ◽  
Data Augmentation ◽  
Probit Model ◽  
Multinomial Probit ◽  
Multinomial Probit Model ◽  
Marginal Data
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