A Tale of Twin Dependence: A New Multivariate Regression Model and an FGLS Estimator for Analyzing Outcomes With Network Dependence

In this article, I present a new multivariate regression model for analyzing outcomes with network dependence. The model is capable to account for two types of outcome dependence including the mean dependence that allows the outcome to depend on selected features of a known dependence network and the error dependence that allows the outcome to be additionally correlated based on patterned connections in the dependence network (e.g., according to whether the ties are asymmetric, mutual, or triadic). For example, when predicting a group of students’ smoking status, the outcome can depend on the students’ positions in their friendship network and also be correlated among friends. I show that analyses ignoring the mean dependence can lead to severe bias in the estimated coefficients while analyses ignoring the error dependence can lead to inefficient inferences and failures in recognizing unmeasured social processes. I compare the new model with related models such as multilevel models, spatial regression models, and exponential random graph models and show their connections and differences. I propose a two-step, feasible generalized least squares estimator to estimate the model that is computationally fast and robust. Simulations show the validity of the new model (and the estimator) while four empirical examples demonstrate its versatility. Associated R package “fglsnet” is available for public use.

Growth Econometrics

Growth econometrics is the application of statistical methods to the study of economic growth and levels of national output or income per head. Researchers often seek to understand why growth rates differ across countries. The field developed rapidly in the 1980s and 1990s, but the early work often proved fragile. Cross-section analyses are limited by the relatively small number of countries in the world and problems of endogeneity, parameter heterogeneity, model uncertainty, and cross-section error dependence. The long-term prospects look better for approaches using panel data. Overall, the quality of the evidence has improved over time, due to better measurement, more data, and new methods. As longer spans of data become available, the methods of growth econometrics will shed light on fundamental questions that are hard to answer any other way.

Modelling Error Dependence in Categorical Longitudinal Data

Hidden Markov models (HMMs) offer an attractive way of accounting and correcting for measurement error in longitudinal data as they do not require the use of a ‘gold standard’ data source as a benchmark. However, while the standard HMM assumes the errors to be independent or random, some common situations in survey and register data cause measurement error to be systematic. HMMs can correct for systematic error as well if the local independence assumption is relaxed. In this chapter, we present several (mixed) HMMs that relax this assumption with the use of two independent indicators for the variable of interest. Finally, we illustrate the results of some of these HMMs with the use of an example of employment mobility. For this purpose, we use linked survey-register data from the Netherlands.

Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement

In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images. The routine consists of a feature tracking module (autoRIFT) that enhances computational efficiency and a geocoding module (Geogrid) that mitigates problems found in existing geocoding algorithms. When applied to satellite imagery, autoRIFT can run on a grid in the native image coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in geographic Cartesian coordinates such as Universal Transverse Mercator or Polar Stereographic. To validate the efficiency and accuracy of this approach, we demonstrate its use for tracking ice motion by using ESA’s Sentinel-1A/B radar data (seven pairs) and NASA’s Landsat-8 optical data (seven pairs) collected over Greenland’s Jakobshavn Isbræ glacier in 2017. Feature-tracked velocity errors are characterized over stable surfaces, where the best Sentinel-1A/B pair with a 6 day separation has errors in X/Y of 12 m/year or 39 m/year, compared to 22 m/year or 31 m/year for Landsat-8 with a 16-day separation. Different error sources for radar and optical image pairs are investigated, where the seasonal variation and the error dependence on the temporal baseline are analyzed. Estimated velocities were compared with reference velocities derived from DLR’s TanDEM-X SAR/InSAR data over the fast-moving glacier outlet, where Sentinel-1 results agree within 4% compared to 3–7% for Landsat-8. A comprehensive apples-to-apples comparison is made with regard to runtime and accuracy between multiple implementations of the proposed routine and the widely-used “dense ampcor" program from NASA/JPL’s ISCE software. autoRIFT is shown to provide two orders of magnitude of runtime improvement with a 20% improvement in accuracy.

Geometric Modeling of Stress Visualization Tools Based on the Functional-Voxel Method

One of the approaches to the construction of graphic images of the stress state for the force vector applied to a point is considered in this work. Has been proposed a geometric model for a continuous medium, formed by a bunch of projection planes for each point of the examined object’s space. This permits to obtain a model for a volume vector in the form of a distributed decomposition into stress components at each point specified by a bunch of projection planes. The building a model for a volume vector, defined as a set of specified laws of direction and length, in the context of modeling stress from an applied force vector to a selected point, is based on strength of materials’ classical laws for calculation the stress state values at an inclined section. Such approach allows use a voxel graphic structure for computer representation of the simulated stress, rather than a finite element mesh. In such a case, there is no obtained result’s error dependence on the spatial position of the mesh nodal points, which is often a problem in FEM calculations. The resulting functional-voxel computer model of the volume stress vector is a structural unit for modeling the distributed load on areas of complex configuration. In this case, the elementary summation of such vectors allows any uneven distribution of the load relative to each point on the specified area. The considered approach works well with geometric models initially represented analytically in the form of a function space (for example, models obtained by the R-functional modelling – RFM-method), and reduced to functional-voxel computer models. A method for deformation modeling based on obtained stresses by means of local transformations of the function space, describing the investigated geometric object, is demonstrated.