scholarly journals Lifelong Disadvantage and Late Adulthood Frailty

2022 ◽  
Vol 2 (1) ◽  
pp. 12-25
Author(s):  
Francesca Zanasi ◽  
Gustavo De Santis ◽  
Elena Pirani
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Age Groups ◽  
Adult Life ◽  
Financial Hardship ◽  
Linear Regression Models ◽  
Complex State ◽  
Late Adulthood ◽  
Over Time

Frailty is a complex state of objective and subjective vulnerability. It tends to increase with age, but the process is influenced by previous life course, especially previous disadvantages. The aim of this paper is to examine how the disadvantages suffered in adulthood (25 to 59 years) in four domains (unemployment, financial hardship, stress, and bad health) affect frailty in late adulthood (60 to 79 years). Using linear regression models on data from the Survey of Health, Ageing, and Retirement in Europe (2004–2017), we estimate frailty levels for several age groups (60–64, 65–69, 70–74, 75–79) accounting for both the persistence of these disadvantages over time and their coexistence, i.e., the number of years when they were simultaneously experienced. Results show that while frailty increases with age, as expected, there is also evidence of an accumulation of risks: the longer the periods of adult life affected by unemployment, stress, financial hardship or, most importantly, bad health, the frailer individuals are in their late years. Furthermore, periods of coexisting disadvantages in adulthood translate into additional frailty in late life. Our findings highlight the importance of fighting disadvantages early in life: long-term improvements in terms of reduced frailty (a concept interrelated with health) may be substantial.

scholarly journals Linear Regression Models Used for the Description of Long-Term Epizootic Process in Tuvinian Natural Plague Focus (Karginsk Mezofocus)

2012 ◽  
pp. 43-47
Author(s):  
N. A. Kol ◽  
A. F. Chul'dum ◽  
M. G. Rostovtsev ◽  
Yu. A. Kalush
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Stepwise Regression ◽  
Climatic Conditions ◽  
Linear Regression Models ◽  
Epizootic Process ◽  
Multiple Linear Regression Models

The results of modeling showed the dependence of epizootic activity in Tuvinian natural plague focus on climatic conditions (average monthly amount of precipitations in the current year and the preceding four years and temperatures in the current and the preceding three years). The multiple linear regression models were used to predict the activity of zoonosis development within a year. The models obtained by means of stepwise regression were most approximated to the natural zoonotic process. The amount of precipitations in winter months and temperature in spring and summer were of the greatest significance for epizootic activity.

Nonhyperelastic Nature of an Elastomeric High Strain Material

2010 ◽  
Vol 77 (6) ◽  
Author(s):  
Choon-Sik Jhun ◽  
John C. Criscione
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
High Strain ◽  
Strain History ◽  
Significant Variable ◽  
Inelastic Behavior ◽  
Linear Regression Models ◽  
Testing Protocol ◽  
Inelastic Responses

Rubber materials have mostly been modeled by utilizing hyperelasticity, which have led to greater understanding and acceptable predictability of their stress-strain response. However, inherent inelastic behavior excluded by approximation has never been characterized by time-dependent parameters such as time, strain-rate, and strain history. We hypothesized that time, stretch rate, and stretch history were prominent factors that induce the inelasticity, and we characterized the inelasticity in terms of those factors using a randomized stretch-controlled testing protocol. We applied the custom randomized testing protocol with the fundamental statistical theory to characterize inelastic behavior imbedded in the high strain material. We hypothesized that time spent testing (T), rate-related stretch history (Ht2), and long-term stretch history (Ht1) give rise to the inelastic deviation from hyperelasticity. We examined the significance, effectiveness, and differences of T, Ht2, and Ht1 by looking at the derived multivariable linear regression models. Distribution of prediction deviation was also examined to see if we missed any other significant variable. Predictability of the multivariable linear regression models was validated by utilizing the unused data from the randomized testing protocol and data from the conventional cyclic testing protocol. We found that the inelasticity of the rubber-like material is highly related to T, Ht2, and Ht1, but not equally influential to all stretches. At smaller deformations, greater inelastic deviation occurs. Inelasticity exponentially decreased over stretch and was nonlinearly related to time. This study successfully determined the elastic/inelastic responses and factors that induce the inelastic response of the rubber-like material. This investigation suggests a way to better describe the elastic/inelastic properties and phenomenological models of rubber-like materials.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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