Forecasting with full use of data without interpolation on logistic curve model with missing data
From MaRDI portal
Publication:2044137
DOI10.1007/S13160-020-00452-WzbMath1470.39041OpenAlexW3121022891MaRDI QIDQ2044137
Ryutaro Matsumura, Daisuke Satoh
Publication date: 4 August 2021
Published in: Japan Journal of Industrial and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13160-020-00452-w
regression analysisexact solutionmissing datadifference equationdiscrete equationlogistic curve model
Linear regression; mixed models (62J05) Economic growth models (91B62) Applications of difference equations (39A60)
Cites Work
- Unnamed Item
- Unnamed Item
- Software reliability modeling. Fundamentals and applications
- Central difference scheme and chaos
- Monotonic decrease of upper limit estimated with Gompertz model for data described using logistic model
- Property of logistic data exposed with Gompertz model and resistance to noise in actual data
- Discrete software reliability assessment with discretized NHPP models
- Period Three Implies Chaos
- A DISCRETE BASS MODEL AND ITS PARAMETER ESTIMATION
- Properties of Gompertz data revealed with non-Gompertz integrable difference equation
- Model selection among growth curve models that have the same number of parameters
- RANDOM DISPERSAL IN THEORETICAL POPULATIONS
- Parameter estimation of discrete logistic curve models for software reliability assessment
This page was built for publication: Forecasting with full use of data without interpolation on logistic curve model with missing data