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Wyszukujesz frazę "Logistic Regression" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Informal Housing in Greece: A Multinomial Logistic Regression Analysis at the Regional Level
Autorzy:
POLYZOS, Serafeim
MINETOS, Dionysios
Powiązania:
https://bibliotekanauki.pl/articles/623885.pdf
Data publikacji:
2014-01-22
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
informal housing
land use changes
multinomial logistic regression
housing policy
Greece
Opis:
This paper deals with the primary causes of informal housing in Greece as well as the observed differentiations in informal housing patterns across space. The spatial level of analysis is the prefectural administrative level. The results of the multinomial logistic regression analysis indicate that Greek prefectures differ in the way they experience the informal housing phenomenon. An explanation for the observed differences may be the separate development paths followed and the diverse range of economic activities in each prefecture. The Greek state has not made provisions for creating the necessary ‘urban land stock’ in each prefecture, so that everyone interested can find land parcels at an affordable price. On the contrary, the state encourages the informal housing activity by legalizing large areas of such activity sporadically and by introducing legislative initiatives of limited success in dealing with the problem.
Źródło:
European Spatial Research and Policy; 2013, 20, 2
1231-1952
1896-1525
Pojawia się w:
European Spatial Research and Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multinomial Logistic Regression Approach For The Evaluation Of Binary Diagnostic Test In Medical Research
Autorzy:
Dwivedi, Alok Kumar
Mallawaarachchi, Indika
Figueroa-Casas, Juan B.
Powiązania:
https://bibliotekanauki.pl/articles/465774.pdf
Data publikacji:
2015
Wydawca:
Główny Urząd Statystyczny
Tematy:
multinomial logistic regression predictive values
sensitivity, specificity
acute appendicitis
pulmonary abnormalities
medical diagnostic test
Opis:
Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for modeling diagnostic measures. All methods provided unbiased estimates of diagnostic measures in the absence of any covariate. LR and GEE methods produced more biased estimates as compared to MLR approach especially for small sample size studies. No bias was obtained in predicting sensitivity measure using MLR method for one screening test. Our proposed MLR method is robust for modeling diagnostic measures of a screening test as opposed to LR method. MLR method and GEE method produced similar estimates of diagnostic measures for comparing two screening tests in large sample size studies. The proposed MLR model for diagnostic measures is simple, and available in common statistical software. Our study demonstrates that MLR method should be preferred as an alternative for modeling diagnostic measures.
Źródło:
Statistics in Transition new series; 2015, 16, 2; 203-222
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Explaining Innovation. An Empirical Analysis of Industry Data from Norway
Autorzy:
Lorentzen, Torbjørn
Jakobsen, Stig-Erik
Powiązania:
https://bibliotekanauki.pl/articles/474880.pdf
Data publikacji:
2016
Wydawca:
Fundacja Upowszechniająca Wiedzę i Naukę Cognitione
Tematy:
innovation
location
centre and periphery
firm specific and external resources
networking
Norwegian industry
logistic regression
innowacje
region
lokalizacja
centrum i peryferia
firmowe i zewnętrzne zasoby
przemysł norweski
regresja logistyczna
Opis:
The objective of the paper is to analyse why some firms innovate while others do not. The paper combines different theories of innovation by relating innovation to internal, firm specific assets and external, regional factors. Hypotheses are derived from theories and tested empirically by using logistic regression. The empirical analysis indicates that internal funding of R&D and size of the firm are the most important firm specific attributes for successful innovation. External, regional factors are also important. The analysis shows that firms located in large urban regions have significantly higher innovation rates than firms located in the periphery, and firms involved in regional networking are more likely to innovate compared to firms not involved in networking. The analysis contributes to a theoretical and empirical understanding of factors that influence on innovation and the role innovation plays in the market economy. Innovation policy should be targeted at developing a tax system and building infrastructure which give firms incentives to invest and allocate internal resources to R&D-activities and collaborate with others in innovation. From an economic policy perspective, consideration should be given to allocating more public resources to rural areas in order to compensate for the asymmetric distribution of resources between the centre and periphery. The paper contributes to the scientific literature of innovation by combining the firm oriented perspective with weight on firm specific, internal resources and a system perspective which focuses on external resources and networking as the most important determinants of innovation in firms.
Źródło:
Journal of Entrepreneurship, Management and Innovation; 2016, 12, 2; 5-27
2299-7075
2299-7326
Pojawia się w:
Journal of Entrepreneurship, Management and Innovation
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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