A New Quartet-Based Statistical Method for Comparing Sets of Gene Trees Is Developed Using a Generalized Hoeffding Inequality

2019 ◽  
Vol 26 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Eliran Avni ◽  
Sagi Snir
Keyword(s):  
Statistical Method ◽  
Gene Trees ◽  
Hoeffding Inequality

Gene trees: A powerful tool for exploring the evolutionary biology of species and speciation

2000 ◽  
Vol 15 (3) ◽  
pp. 211-222 ◽  
Author(s):  
Alan R. Templeton ◽  
Stephanie D. Maskas ◽  
Mitchell B. Cruzan
Keyword(s):  
Evolutionary Biology ◽  
Gene Trees

Income and Life Quality of Farmer Households Suffering Social Exclusion

2012 ◽  
pp. 63-87
Author(s):  
Anh Mai Ngoc ◽  
Ha Do Thi Hai ◽  
Huyen Nguyen Thi Ngoc
Keyword(s):  
Labor Market ◽  
Statistical Method ◽  
Social Exclusion ◽  
Central Government ◽  
Life Quality ◽  
Economic Aspect ◽  
Local Authorities ◽  
The Impact

This study uses descriptive statistical method to analyze the income and life qual- ity of 397 farmer households who are suffering social exclusion in an economic aspect out of a total of 725 households surveyed in five Northern provinces of Vietnam in 2010. The farmers’ opinions of the impact of the policies currently prac- ticed by the central government and local authorities to give them access to the labor market are also analyzed in this study to help management officers see how the poli- cies affect the beneficiaries so that they can later make appropriate adjustments.

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

MINIMUM CONSUMER BASKET: WHAT SHOULD IT BE IN TODAY’S RUSSIA?

2020 ◽  
pp. 54-73
Author(s):  
V.N. Bobkov ◽  
A.A. Gulyugina ◽  
Ye.V. Odintsova
Keyword(s):  
Social Support ◽  
Statistical Method ◽  
Food Products ◽  
Russian Population ◽  
Entire System ◽  
Quantitative Structure ◽  
Social State ◽  
Qualitative And Quantitative ◽  
Subsistence Minimum

The article argues for a proposal for such a step in the direction of strengthening social support for the least protected groups of the Russian population and the development of the entire system of social state guarantees as a whole, as the introduction of a socially acceptable consumer basket instead of a consumer basket of the subsistence minimum. The advantages of the normative method of forming consumer baskets over the normative-statistical method are revealed. Based on the analysis of actual consumption of both food and non-food products and services, the qualitative and quantitative structure of the socially acceptable consumer basket is determined (using natural and value indicators).

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