scholarly journals Over-indebted Households in Poland: Classification Tree Analysis

2020 ◽  
Author(s):  
Grzegorz Wałęga ◽  
Agnieszka Wałęga
Keyword(s):  
Classification Tree ◽  
Regression Tree ◽  
Poverty Line ◽  
Classification And Regression Tree ◽  
Vulnerable Groups ◽  
Psychological Burden ◽  
Tree Analysis ◽  
Level Data ◽  
Income Ratio ◽  
Definition Of

Abstract Increasing a personal debt burden implies greater financial vulnerability and threats for macroeconomic stability. It also generates a risk of the households over-indebtedness. The assessment of over-indebtedness is conducted with the use of various objective and subjective measures based on the micro-level data. The aim of the study is to investigate over-indebted households in Poland using a unique dataset obtained from the CATI survey. We discuss and compare the usefulness of various over-indebtedness measures across different socio-economic characteristics. Due to the differences in over-indebtedness across single measures, we perform a more complex assessment using a mix of indicators. As an alternative to other commonly criticised over-indebtedness measures, we apply the “below the poverty line” (BPL) measure. In order to obtain the profile of over-indebted households, we use classification and regression tree analysis as an alternative to logit or probit models. We find that DSTI (“debt service to income”) ratio underestimates the extent of over-indebtedness in vulnerable groups of households in comparison with the BPL. We highlight the necessity to use different measures depending on the adopted definition of over-indebtedness. A psychological burden of debts is particularly strong among older and poorly educated respondents. We also find that the age structure of over-indebted households in Poland differs from this structure in countries with a broader access to consumer credits. Our results can be used to enrich the methods of assessing the household over-indebtedness.

Atlantic cod (Gadus morhua) distribution response to environmental variability in the northern Gulf of St. Lawrence

2009 ◽  
Vol 66 (6) ◽  
pp. 909-918 ◽  
Author(s):  
Jonathan L.W. Ruppert ◽  
Marie-Josée Fortin ◽  
George A. Rose ◽  
Rodolphe Devillers
Keyword(s):  
Gadus Morhua ◽  
Environmental Variability ◽  
Atlantic Cod ◽  
Classification Tree ◽  
Regression Tree ◽  
Distribution Patterns ◽  
Classification And Regression Tree ◽  
Bottom Temperature ◽  
Tree Analysis ◽  
Regression Tree Analysis

Atlantic cod ( Gadus morhua ) distribution patterns and the behavioral (site fidelity), biotic (prey and predators), and environmental factors that determine them are fundamental to cod’s historic importance as a commercial species in the North Atlantic. Using classification and regression tree analysis (CART), we compared two periods (1991–1995 and 1998–2004) with contrasting bottom temperature and salinity regimes to determine regional factors that best explained cod distribution and catch weight per tow from summer surveys in the northern Gulf of St. Lawrence (the feeding period of cod). The classification tree analysis indicated that the presence or absence of cod was chiefly determined by depth in both of these periods. In contrast, the regression tree analysis determined that cod catch weight distributions were explained by different variables in each period. In the colder period (1991–1995), the distribution of catch weights was explained well by environmental variables (bottom temperature, salinity, depth); however, in the warmer period (1998–2004), distributions were best explained by variables from the previous year. These results indicate that the spatiotemporal dynamics of environmental conditions are likely to influence the loyalty of cod to specific feeding grounds and imply that cod responses to the environment could be susceptible to long-term environmental (e.g., bottom–habitat) and climate change.

679: Predicting delivery route in nulliparas with classification and regression tree analysis

2011 ◽  
Vol 204 (1) ◽  
pp. S268
Author(s):  
Michelle Kominiarek ◽  
Paul VanVeldhuisen ◽  
Kimberly Gregory ◽  
Moshe Fridman ◽  
Judith Hibbard
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Tree Analysis ◽  
Regression Tree Analysis ◽  
Classification And Regression ◽  
Delivery Route

scholarly journals Cytokine signature and COVID-19 prediction models in the two waves of pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D’Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of ◽  
Second Wave

AbstractIn Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

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