Prediction in Censored Survival Data: A Comparison of the Proportional Hazards and Linear Regression Models

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

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.

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A Note on the Estimation of Linear Regression Models with Heteroskedastic Measurement Errors

SSRN Electronic Journal ◽

10.2139/ssrn.295567 ◽

2002 ◽

Cited By ~ 2

Author(s):

Daniel G. Sullivan

Keyword(s):

Linear Regression ◽

Measurement Errors ◽

Regression Models ◽

Linear Regression Models

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

Anais do 14. Congresso Brasileiro de Inteligência Computacional ◽

10.21528/cbic2019-3 ◽

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

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

Gadjah Mada International Journal of Business ◽

10.22146/gamaijb.5633 ◽

2003 ◽

Vol 5 (3) ◽

pp. 363 ◽

Cited By ~ 1

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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