|
Abstract:
|
Many series of data record individual observations as intervals, such as stock market values with daily high-low values, or minimum and maximum monthly temperatures, recorded over time. Moreover, with the advent of supercomputers, datasets can be extremely large, and it is frequently the case that observations are aggregated into intervals (or histograms, or other forms of so-called symbolic data). Taking the average of the intervals results in a loss of information. Therefore, in comparison with classical data, they are more complex and can have internal structures that impose complications that are not evident in classical data. In particular, the time dependency makes it more difficult to deal with and incorporate their complex structures and internal variations. In this talk, we present our proposed autocovariance/autocorrelation functions for interval-valued autoregressive series models. Maximum likelihood estimators are derived by using the ideas of composite likelihood and the pairwise likelihood functions. Asymptotic properties of these estimators are obtained. The results of simulation studies will be presented that show the new estimators perform considerably well.
|
