scholarly journals The Influence of State-Level Production Outcomes upon U.S. National Corn and Soybean Production: A Novel Application of Correlated Component Regression

2021 ◽  
pp. 1-20
Author(s):  
David W. Bullock
Keyword(s):  
Genetic Modification ◽  
State Level ◽  
Data Sets ◽  
Relative Importance ◽  
Corn Production ◽  
Soybean Production ◽  
Time Periods ◽  
The Empirical Analysis ◽  
Sparse Data Sets ◽  
Level Production

Abstract The relative importance of key state-level outcomes upon U.S. national corn and soybean production was examined using correlated component regression, a recently developed regression technique for application to multicollinear and sparse data sets. Standardized coefficients were used to rank the states’ relative importance. A Herfindahl-Hirschman Index was used to measure the degree of concentration among the top ranked states. The empirical analysis looked at two time periods: a pre-Genetic Modification (1975–1995) and a post-Genetic Modification (1996–2017) period. The results indicate that U.S. corn production is becoming less geographically concentrated in terms of state-level importance while the opposite holds true for soybean production.

Effects of Population Structure and Admixture on Exact Tests for Association Between Loci

Genetics ◽  
2003 ◽  
Vol 164 (1) ◽  
pp. 381-387
Author(s):  
B Law ◽  
J S Buckleton ◽  
C M Triggs ◽  
B S Weir
Keyword(s):  
Population Subdivision ◽  
Data Sets ◽  
Multilocus Genotype ◽  
Test Statistic ◽  
Exact Test ◽  
Testing Strategies ◽  
Different Populations ◽  
Alternative Testing ◽  
Sparse Data Sets ◽  
Alternative Testing Strategies

Abstract The probability of multilocus genotype counts conditional on allelic counts and on allelic independence provides a test statistic for independence within and between loci. As the number of loci increases and each sampled genotype becomes unique, the conditional probability becomes a function of total heterozygosity. In that case, it does not address between-locus dependence directly but only indirectly through detection of the Wahlund effect. Moreover, the test will reject the hypothesis of allelic independence only for small values of heterozygosity. Low heterozygosity is expected for population subdivision but not for population admixture. The test may therefore be inappropriate for admixed populations. If individuals with parents in two different populations are always considered to belong to one of the populations, then heterozygosity is increased in that population and the exact test should not be used for sparse data sets from that population. If such a case is suspected, then alternative testing strategies are suggested.

scholarly journals Estimating Dynamic Games of Electoral Competition to Evaluate Term Limits in US Gubernatorial Elections

2017 ◽  
Vol 107 (7) ◽  
pp. 1824-1857 ◽  
Author(s):  
Holger Sieg ◽  
Chamna Yoon
Keyword(s):  
Political Economy ◽  
Empirical Analysis ◽  
Dynamic Games ◽  
Electoral Competition ◽  
Term Limits ◽  
Relative Importance ◽  
Political Candidates ◽  
Trade Off ◽  
Perfect Monitoring ◽  
The Empirical Analysis

This paper shows how to identify and estimate, using standard semi-parametric techniques, a class of dynamic games with perfect monitoring, that have been at the frontier of recent research in political economy. The empirical analysis provides novel quantitative insights into the trade-off that voters face between ideology and ability, the differences in ability and ideology among parties and states, and the differences in preferences between political candidates and voters. We analyze the consequences of term limits and quantify their relative importance. Specifically, we characterize conditions under which term limits improve voters' welfare. (JEL C57, C73, D72)

scholarly journals Evaluating parameters for ligand-based modeling with random forest on sparse data sets

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander Kensert ◽  
Jonathan Alvarsson ◽  
Ulf Norinder ◽  
Ola Spjuth
Keyword(s):  
Random Forest ◽  
Sparse Data ◽  
Data Sets ◽  
Sparse Data Sets

Estimation of Average Payloads from Weigh-in-Motion Data

2017 ◽  
Vol 2644 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Sarah Hernandez
Keyword(s):  
State Level ◽  
Gaussian Mixture ◽  
Data Sets ◽  
Body Type ◽  
Motion Data ◽  
Weigh In Motion ◽  
Measured Weight ◽  
Gross Vehicle Weight ◽  
Definition Of ◽  
Weight Data

Average payloads define the ton-to-truck conversion factors that are critical inputs to commodity-based freight forecasting models. However, average payloads are derived primarily from outdated, unrepresentative truck surveys. With increasing technological and methodological means of concurrently measuring truck configurations, commodity types, and weights, there are now viable alternatives to truck surveys. In this paper, a method to derive average payloads by truck body type and using weight data from weigh-in-motion (WIM) sensors is presented. Average payloads by truck body type are found by subtracting an estimated average empty weight from an estimated average loaded weight. Empty and loaded weights are derived from a Gaussian mixture model fit to a gross vehicle weight distribution. An analysis of truck body type distributions, loaded weights, empty weights, and resulting payloads of five-axle tractor trailer (FHWA Class 9 or 3-S2) trucks is presented to compare national and state-level Vehicle Inventory and Use Survey (VIUS) data and the WIM-based approach. Results show statistically significant differences between the three data sets in each of the comparison categories. A challenge in this analysis is the definition of a correct set of payloads because the WIM and survey data are subject to their own inherent misrepresentations. WIM data, however, provide a continuous source of measured weight data that overcome the drawback of using out-of-date surveys. Overall, average payloads from measured weights are lower than those for the national or California VIUS estimates.

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