Stochastic Measurement Procedures Based on Stationary Time Series
-
Monica Dumitrescu
Abstract
Stochastic measurement procedures have been proposed and developed in several articles published in EQC. In contrast to the traditional measurement procedures that use the obsolete error propagation formula for taking into account measurement uncertainty, stochastic measurement procedures include the entire uncertainty of the measurement process in a natural and rational way. In this paper the concept of stochastic measurement procedure is extended to the case that the measurement process is given by a stochastic process or more precisely said by a time series. This case appears often for economic phenomena. In this paper, only the feasibility of such measurement procedures shall be demonstrated and the considerations are therefore restricted to some simple and rather unrealistic examples. In a realistic case an appropriate stochastic model must be developed following the rules of stochastic modelling.
© Heldermann Verlag
Articles in the same Issue
- Stochastic Measurement Procedures Based on Stationary Time Series
- Semi-Parametric Estimation of PX,Y (X > Y)
- On The Performance of A New Test of Exponentiality Against IFR Alternatives Based on the L-statistic Approach
- Control Chart for Autocorrelated Processes with Heavy Tailed Distributions
- Designing the Scale Counting Procedure for Large Numbers of Small Parts
- MTBF for K-out-of-N: G Systems with M Failure Modes
- Non-parametric Control Chart for Controlling Variability Based on Rank Test
- Bounds for Distorted Risk Measures
- Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
- Explicit Expressions for Moments of Log Normal Order Statistics
- Partially Specified Prior
- The Need for a Standard for Making Predictions
Articles in the same Issue
- Stochastic Measurement Procedures Based on Stationary Time Series
- Semi-Parametric Estimation of PX,Y (X > Y)
- On The Performance of A New Test of Exponentiality Against IFR Alternatives Based on the L-statistic Approach
- Control Chart for Autocorrelated Processes with Heavy Tailed Distributions
- Designing the Scale Counting Procedure for Large Numbers of Small Parts
- MTBF for K-out-of-N: G Systems with M Failure Modes
- Non-parametric Control Chart for Controlling Variability Based on Rank Test
- Bounds for Distorted Risk Measures
- Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
- Explicit Expressions for Moments of Log Normal Order Statistics
- Partially Specified Prior
- The Need for a Standard for Making Predictions
