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Relevant bibliographies by topics / Prediction of survival; Probability; Time models / Books

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Books on the topic 'Prediction of survival; Probability; Time models'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Life time data: Statistical models and methods. Singapore: World Scientific, 2006.

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2

Box, George E. P. Time series analysis: Forecasting and control. 4th ed. Hoboken, N.J: John Wiley, 2008.

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3

Box, George E. P. Time series analysis: Forecasting and control. 3rd ed. Englewood Cliffs, N.J: Prentice Hall, 1994.

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4

Box, George E. P. Time series analysis: Forecasting and control. 4th ed. Hoboken, N.J: John Wiley, 2008.

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5

Lifetime Data: Statistical Models and Methods. World Scientific Publishing Co Pte Ltd, 2015.

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6

Box, George E. P. Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics). 4th ed. Wiley-Interscience, 2008.

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7

Deshpande, Jayant V., and Sudha G. Purohit. Life-time Data: Statistical Models And Methods (Quality, Reliability and Engineering Statistics) (Quality, Reliabiltiy & Engineering Statistics). World Scientific Publishing Company, 2006.

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8

Box, George E. P. Time Series Analysis: Forecasting & Control. Pearson Education Asia Limited, 2005.

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9

Jenkins, Gwilym M., Gregory C. Reinsel, and George E. P. Box. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2011.

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10

Jenkins, Gwilym M., Gregory C. Reinsel, and George E. P. Box. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2013.

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11

Jenkins, Gwilym M., Gregory C. Reinsel, George E. P. Box, and Greta M. Ljung. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2015.

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12

Jenkins, Gwilym M., Gregory C. Reinsel, George E. P. Box, and Greta M. Ljung. Time Series Analysis: Forecasting and Control. Wiley & Sons, Incorporated, John, 2015.

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13

Time Series Analysis: Forecasting and Control. Wiley, 2015.

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14

Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation. It is known that maximum likelihood estimation can still be used provided the range of τ‎ is restricted. It is shown that the alternative maximum product of spacings method is consistent without restriction on τ‎. Censored observations which commonly occur in survival-time data can be accounted for using Kaplan-Meier estimation. A real data numerical example is given.

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15

Clark, James S., Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, et al. Assessing the probability of rare climate events. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.16.

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This article focuses on the use of Bayesian methods in assessing the probability of rare climate events, and more specifically the potential collapse of the meridional overturning circulation (MOC) in the Atlantic Ocean. It first provides an overview of climate models and their use to perform climate simulations, drawing attention to uncertainty in climate simulators and the role of data in climate prediction, before describing an experiment that simulates the evolution of the MOC through the twenty-first century. MOC collapse is predicted by the GENIE-1 (Grid Enabled Integrated Earth system model) for some values of the model inputs, and Bayesian emulation is used for collapse probability analysis. Data comprising a sparse time series of five measurements of the MOC from 1957 to 2004 are analysed. The results demonstrate the utility of Bayesian analysis in dealing with uncertainty in complex models, and in particular in quantifying the risk of extreme outcomes.

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