Disability Statistics Center Reports People Need Statistical Information

2001 ◽  
Author(s):  
Keyword(s):  
Statistical Information

The experience of ranking russian economic journals

2015 ◽  
pp. 99-115 ◽  
Author(s):  
E. Balatsky ◽  
N. Ekimova
Keyword(s):  
Information System ◽  
Citation Index ◽  
Statistical Information ◽  
Expert Evaluation ◽  
Expert Interviews ◽  
Bibliometric Data ◽  
Russian Science ◽  
Ranking Systems ◽  
Economic Journals ◽  
Russian Science Citation Index

The article presents the results of the rating of Russian economic journals, the methodology of which is based on a combination of bibliometric data and expert interviews. Processing of the statistical information system of Russian science citation index (RINC) allows us to form a “primary” list of the best journals in the country. Expert evaluation of the list makes it possible to reorganize it with regard to the scientific level of periodicals and get the “secondary” list. The merger of two ranking systems forms the basis of obtaining the final ranking of economic journals. It is shown that the leading part of the constructed rating forms a kind of the Diamond List of journals, which on the whole agrees with similar lists obtained in earlier studies by other authors.

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 33-42
Author(s):  
Thomas Otter
Keyword(s):  
Confidence Intervals ◽  
Generalized Linear Model ◽  
Statistical Information ◽  
Regression Coefficients ◽  
Model Specification ◽  
Model Parameters ◽  
Exploratory Research ◽  
Empirical Calibration ◽  
Credible Intervals ◽  
Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

The copper industry of the Sverdlovsk region in 1940—1953 (statistical information)

2020 ◽  
Vol 2020 (10-4) ◽  
pp. 85-92
Author(s):  
Alexander Bedel ◽  
Mikhail Mikheev
Keyword(s):  
Statistical Data ◽  
Statistical Information ◽  
Copper Industry ◽  
Territorial Organization

This article introduces statistical data on the development of the copper industry in the Sverdlovsk region in 1940-1953. On the example of the industry, we consider such a phenomenon as indirect damage from the fighting of 1941-1945 for the industrial Urals. The related changes in the territorial organization of the productive forces of the copper industry in the region are shown.

On the system of classification and coding territories in the Russian Federation

2020 ◽  
Vol 959 (5) ◽  
pp. 41-53
Author(s):  
G.G. Pobedinskiy ◽  
M.V. Vyushkov ◽  
Y.R. Belykh
Keyword(s):  
Infectious Disease ◽  
Russian Federation ◽  
Statistical Information ◽  
Spatial Description ◽  
Suspected Infection ◽  
Spatial Reference ◽  
Federal Statistical ◽  
Multifactor Analysis ◽  
The Russian Federation ◽  
Epidemiological Situation

The most common method of statistical analysis in epidemiology is the retrospective analysis of infectious disease cases. Recorded in the system of Federal statistical observation they are not bound on specific localities, but to the areas of territorial bodies’ of Russian Federation Health Care Ministry and Rospotrebnadzor responsibility. In order to load the information into databases and use them in GIS, the spatial reference of statistical information to specific coordinates of suspected infection sites or to a specific administrative or specially designated territory having a spatial description is necessary. The aim of the work is to analyze the system of classification and coding administrative and specially allocated territories of the Russian Federation at various levels for the implementation of multifactor analysis of the epidemiological situation with the involvement of climatic, landscape, land management and other data, as well as to solve other problems of the territories spatial development.

scholarly journals Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 692
Author(s):  
Clara Calvo ◽  
Carlos Ivorra ◽  
Vicente Liern ◽  
Blanca Pérez-Gladish
Keyword(s):  
Risk Measure ◽  
Statistical Information ◽  
Expected Returns ◽  
Financial Assets ◽  
Expected Return ◽  
Additional Information ◽  
Subjective Risk ◽  
The Impact ◽  
Statistical Risk ◽  
Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

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