この電子書籍について
- Parametric models with coverage of
- Concept of maximum likelihood estimate (MLE) of a probability distribution parameter
- MLE of the survival function
- Common probability distributions and their analysis
- Analysis of exponential distribution as a survival function
- Analysis of Weibull distribution as a survival function
- Derivation of Gumbel distribution as a survival function from Weibull
- Non-parametric models including
- Kaplan–Meier (KM) estimator, a derivation of expression using MLE
- Fitting KM estimator with an example dataset, Python code and plotting curves
- Greenwood’s formula and its derivation
- Models with covariates explaining
- The concept of time shift and the accelerated failure time (AFT) model
- Weibull-AFT model and derivation of parameters by MLE
- Proportional Hazard (PH) model
- Cox-PH model and Breslow’s method
- Significance of covariates
- Selection of covariates
The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.
著者について
Avishek Nag has a Masters of Technology Degree in data analytics and machine learning from Birla Institute of Technology and Science, Pilani, India. He has more than 15 years of experience in Software Development and Architecting Systems. He also has professional experience in data science and machine learning, Java, Python, Big Data, including Spark and MongoDB. He has worked at VMWare, Cisco, Mobile Iron, and Computer Science Corporation (now called DXC). He is also the author of the book Pragmatic Machine Learning with Python, which is recommended in the ACM Education Digital Library.
