JSTOR Daily

A Fast Algorithm for the Exact Likelihood of Stationary and Partially Nonstationary Vector Autoregressive-Moving Average Processes

This is a preview. Log in through your library . Abstract An expression for the likelihood function of a stationary vector autoregressive-moving average process is developed. The expression is very ...
ascopubs.org

Is the incidence of breast cancer increasing? Using the NCDB and autoregressive integrated moving average models (ARIMA) to forecast breast cancer to 2030.

Meta-analysis and single-center experience on the comprehensive genomic characterization and landscape of BRCA1 and BRCA2 in Turkey. This is an ASCO Meeting Abstract from the 2020 ASCO Annual Meeting ...
InfoWorld

Time series forecasting with ARMA and InfluxDB

Autoregressive moving average models have a number of advantages including simplicity. Here’s how to use an ARMA model with InfluxDB. An ARMA or autoregressive moving average model is a forecasting ...
JSTOR Daily

Least Absolute Deviation Estimation for Fractionally Integrated Autoregressive Moving Average Time Series Models with Conditional Heteroscedasticity

We consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its ...
inc42

What Are Autoregressive Models? Here’s All You Need to Know

Autoregressive models are a statistical technique used to predict future values in a sequence based on its past values. It is essentially a fancy way of saying that it uses the past to predict the ...
ascopubs.org

Forecasting incidence and mortality rates of bladder cancer in older adults using autoregressive integrated moving average (ARIMA).

Impact of trimodal therapy on survival outcomes in non-urothelial muscle-invasive bladder cancer: A nationwide analysis.