Improved Prediction Intervals for ARIMA Processes and Structural Time Series (tsPI)
Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.
Typed JSON (rtson)
TSON, short for Typed JSON, is a binary-encoded serialization of JSON like document that support JavaScript typed data (https://…/TSON).
Piecewise Constant Hazards Models for Censored and Truncated Data (pch)
Using piecewise constant hazards models is a very flexible approach for the analysis of survival data. The time line is divided into sub-intervals; for each interval, a different hazard is estimated using Poisson regression.