Statistics and Computing
Statistics and Computing is a bi-monthly refereed journal that publishes papers covering the interface between the statistical and computing sciences.
The journal includes techniques for evaluating analytically intractable problems, such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification.
Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification
Andrew C. Titman (November 2017)
- Journal Title
- Statistics and Computing
- Volume 1 / 1991 - Volume 27 / 2017
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Industry Sectors
To view the rest of this content please follow the download PDF link above.