Statistical Methods in Graphs: Parameter Estimation, Model Selection, and Hypothesis Test

2016 ◽  
pp. 183-202 ◽  
Author(s):  
Suzana de Siqueira Santos ◽  
Daniel Yasumasa Takahashi ◽  
João Ricardo Sato ◽  
Carlos Eduardo Ferreira ◽  
André Fujita
Keyword(s):  
Parameter Estimation ◽  
Model Selection ◽  
Statistical Methods ◽  
Hypothesis Test ◽  
Estimation Model

scholarly journals Legofit: Estimating Population History from Genetic Data

2019 ◽  
Author(s):  
Alan R. Rogers
Keyword(s):  
Model Selection ◽  
Statistical Methods ◽  
Model Averaging ◽  
Population History ◽  
Simultaneous Estimation ◽  
Estimation Of Parameters ◽  
Estimation Model ◽  
Data Manipulation ◽  
Archaic Admixture ◽  
Parameter Values

AbstractBackgroundOur current understanding of archaic admixture in humans relies on statistical methods with large biases, whose magnitudes depend on the sizes and separation times of ancestral populations. To avoid these biases, it is necessary to estimate these parameters simultaneously with those describing admixture. Genetic estimates of population histories also confront problems of statistical identifiability: different models or different combinations of parameter values may fit the data equally well. To deal with this problem, we need methods of model selection and model averaging, which are lacking from most existing software.ResultsThe Legofit software package allows simultaneous estimation of parameters describing admixture and other aspects of population history. It includes facilities for data manipulation, estimation, model selection, and model averaging. It outperforms several statistical methods that have been widely used to study archaic admixture in humans.

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

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