Correlation Analysis among International Soybean Futures Markets Based on D-vine Pair-Copula Method

2012 ◽  
pp. 405-408
Author(s):  
Jianbao Chen ◽  
Ting Yang ◽  
Huobiao Zhou
Keyword(s):  
Correlation Analysis ◽  
Futures Markets ◽  
Copula Method

Effect of Weather on Agricultural Futures Markets on the Basis of DCCA Cross-Correlation Coefficient Analysis

2016 ◽  
Vol 15 (02) ◽  
pp. 1650012 ◽  
Author(s):  
Guangxi Cao ◽  
Cuiting He ◽  
Wei Xu
Keyword(s):  
Correlation Analysis ◽  
Futures Markets ◽  
Cross Correlation ◽  
Moving Average ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Agricultural Futures

This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and [Formula: see text]-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.

scholarly journals Correlation Analysis of Influencing Factors and the Monthly Displacement Increment of a Hydrodynamic Landslide Using a Pseudo-Maximum-Likelihood-Estimation-Mixed-Copula Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Rubin Wang ◽  
Kun Zhang ◽  
Jian Qi ◽  
Weiya Xu ◽  
Huanling Wang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Correlation Analysis ◽  
Influencing Factors ◽  
Likelihood Estimation ◽  
Water Level Fluctuations ◽  
Reservoir Level ◽  
Displacement Increment ◽  
Copula Method ◽  
Pseudo Maximum Likelihood Estimation ◽  
Pseudo Maximum Likelihood

Heavy rainfall and changes in the water levels of reservoirs directly affect the degree of landslide disasters in major hydropower project reservoir areas. Correlation analyses of rainfall- and water-level fluctuations with landslide displacement changes can provide a scientific basis for the prevention and early warning of landslide disasters in reservoir areas. Because of the shortcomings of the traditional correlation analysis based on linear assumptions, this study proposed the use of a pseudo-maximum-likelihood-estimation-mixed-Copula (MLE-M-Copula) method instead of linear assumptions. We used the Bazimen landslide in the Three Gorges Reservoir Area as a case study to carry out the correlation analysis of the rainfall, water-level fluctuations, and landslide displacement. First, we selected several appropriate influencing factors to construct four candidate Copula models and estimated the parameters using the pseudo-MLE method. After the goodness-of-fit test, we selected the M-Copula model as the optimal model and used this model to study correlations between the monthly displacement increment of the landslide and influencing factors. We then established the joint distribution functions of these correlations. We computed and analyzed the overall and tail correlations between the displacement increment and the influencing factors, and we constructed the conditional probability distribution of the monthly displacement increment for different given conditions. The results showed that the pseudo-MLE-M-Copula method effectively quantified the correlation between the rainfall, reservoir-level fluctuations, and landslide displacement changes, and we obtained the return periods and value at risk of the influencing factors of the Bazimen landslide under different rainfall conditions and reservoir-level changes. Furthermore, the tail correlations between the monthly displacement increment of the landslide and the rainfall- and reservoir-level changes were higher than the overall correlations.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

The German Strengths and Difficulties Questionnaire (SDQ)

2010 ◽  
Vol 26 (4) ◽  
pp. 256-262 ◽  
Author(s):  
Ulrike Petermann ◽  
Franz Petermann ◽  
Ina Schreyer
Keyword(s):  
Correlation Analysis ◽  
Teacher Ratings ◽  
Screening Instrument ◽  
Preschool Age ◽  
Strengths And Difficulties Questionnaire ◽  
Preschool Age Children ◽  
Discriminant Analyses ◽  
Strengths And Difficulties ◽  
Behavioral Attributes ◽  
And Behavior

The Strengths and Difficulties Questionnaire (SDQ) is a screening instrument that addresses positive and negative behavioral attributes of children and adolescents. Although this questionnaire has been used in Germany to gather information from parents and teachers of preschoolers, few studies exist that verify the validity of the German SDQ for this age. In the present study, teacher ratings were collected for 282 children aged 36 to 60 months (boys = 156; girls = 126). Likewise, teacher ratings were collected with another German checklist for behavior problems and behavior disorders at preschool age (Verhaltensbeurteilungsbogen für Vorschulkinder, VBV 3–6). Moreover, children’s developmental status was assessed. Evaluation included correlation analysis as well as canonical correlation analysis to assess the multivariate relationship between the set of SDQ variables and the set of VBV variables. Discriminant analyses were used to clarify which SDQ variables are useful to differentiate between children with or without developmental delay in a multivariate model. The results of correlation and discriminant analyses underline the validity of the SDQ for preschoolers. According to these results, the German teacher SDQ is recommended as a convenient and valid screening instrument to assess positive and negative behavior of preschool age children.

Sign in / Sign up

Export Citation Format

Share Document