Journal of Data Science

Linear regression models are widely used in empirical studies. When serial correlation is present in the residuals, generalized least squares (GLS) estimation is commonly used to improve estimation efficiency. This paper proposes the use of an alternative estimator, the approximate generalized least squares estimators based on high-order AR(p) processes (GLS-AR). We show that GLS-AR estimators are asymptotically efficient as GLS estimators, as both the number of AR lag, p, and the number of observations, n, increase together so that $p=o({n^{1/4}})$ in the limit. The proposed GLS-AR estimators do not require the identification of the residual serial autocorrelation structure and perform more robust in finite samples than the conventional FGLS-based tests. Finally, we illustrate the usefulness of GLS-AR method by applying it to the global warming data from 1850–2012.

Abstract: A field study was carried out to determine the spatial distribution of air dose rate on grazed grassland after the earthquake on 11 March, 2011 in the Northwest Pacific of Northeastern Japan. Data on air dose rates (µSv h-1) were collected from Ichinoseki, south of Iwate Prefecture, Japan. Air dose rates were collected from each of 1 m interval of 12 ×12 m2 site (L-site). At the center of Lsite, 1.2 ×1.2 m2 site (S-site) was located. One hundred and forty four (144) equal spaced quadrats were defined in the S-site. Again, air dose rates were collected from central point of each of the quadrat. Moran’s I, a measure of autocorrelation was used to test the spatial heterogeneity of air dose rate on grazed grassland. Autocorrelation in S-site area was significantly higher than L-site area. Air dose rate did not show significant autocorrelation at any spatial lag in L-site. In S-site, air dose rate level showed significant autocorrelation in twelve of sixteen spatial lag. Autocorrelograms and Moran’s scatterplot showed that air dose rate was frequently and positively spatially correlated at distance less than 0.1 m.

In this article I analyse motion picture editing as a point process to explore the temporal structure in the timings of cuts in motion pictures, modelling the editing in 134 Hollywood films released between 1935 and 2005 as a Hawkes process with an exponential kernel. The results show that the editing in Hollywood films can be modelled as a Hawkes process and that the conditional intensity function provides a direct description of the instantaneous cutting rate of a film, revealing the structure of a film’s editing at a range of scales. The parameters of the exponential kernel show a clear trend over time to a more rapid editing style with an increase in the rate of exogenous events and small increase in the rate of endogenous events. This is consistent with the shift from a classical to an intensified continuity editing style. There are, however, few differences between genres indicating the consistency of editing practices in Hollywood cinema over time and different types of films.