Robert B Miller
Biographic Data
| ID | 8920899 |
|---|---|
| NAME | Robert B Miller |
| GIVEN NAMES | Robert B |
| FAMILY NAME | Miller |
| SIGNATURE | MILLER R B |
| AFFILIATIONS | The Ohio State University |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1985 |
| LATEST PUBLICATION YEAR | 1990 |
| H-INDEX | 0 |
Repeated Time Series Analysis of Arima–Noise Models
This article develops a theory and methodology for repeated time series (RTS) measurements on autoregressive integrated moving average–noise (ARIMAN) process. The theory enables us to relax the normality assumption in the ARIMAN model and to identify models for each component series of the process. We discuss the properties, estimation, and forecasting of RTS ARIMAN models and illustrate with examples
Repeated Time Series Analysis of Arima-Noise Models
This article develops a theory and methodology for repeated time series (RTS) measurements on autoregressive integrated moving average–noise (ARIMAN) process. The theory enables us to relax the normality assumption in the ARIMAN model and to identify models for each component series of the process. We discuss the properties, estimation, and forecasting of RTS ARIMAN models and illustrate with examples
Sampling the Future
The Box–Jenkins methodology for modeling and forecasting from univariate time series models has long been considered a standard to which other forecasting techniques have been compared. To a Bayesian statistician, however, the method lacks an important facet—a provision for modeling uncertainty about parameter estimates. We present a technique called sampling the future for including this feature in both the estimation and forecasting stages. Alt…
The Modern Forecaster
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The Modern Forecaster
Sampling the Future
The Box–Jenkins methodology for modeling and forecasting from univariate time series models has long been considered a standard to which other forecasting techniques have been compared. To a Bayesian statistician, however, the method lacks an important facet—a provision for modeling uncertainty about parameter estimates. We present a technique called sampling the future for including this feature in both the estimation and forecasting stages. Alt…
Repeated Time Series Analysis of Arima–Noise Models
This article develops a theory and methodology for repeated time series (RTS) measurements on autoregressive integrated moving average–noise (ARIMAN) process. The theory enables us to relax the normality assumption in the ARIMAN model and to identify models for each component series of the process. We discuss the properties, estimation, and forecasting of RTS ARIMAN models and illustrate with examples
Repeated Time Series Analysis of Arima-Noise Models
This article develops a theory and methodology for repeated time series (RTS) measurements on autoregressive integrated moving average–noise (ARIMAN) process. The theory enables us to relax the normality assumption in the ARIMAN model and to identify models for each component series of the process. We discuss the properties, estimation, and forecasting of RTS ARIMAN models and illustrate with examples
Artificial Intelligence (4 works) · Computer Science (4 works) · Econometrics (4 works) · Forecasting Techniques and Applications (4 works) · Autoregressive integrated moving average (3 works) · Financial Risk and Volatility Modeling (3 works) · Mathematics (3 works) · Statistics (3 works) · Time series (3 works) · Applied Mathematics (2 works)