Collinearity Diagnostics for Complex Survey Data

Survey data are often used to fit models. The values of covariates used in modeling are not controlled as they might be in an experiment. Thus, collinearity among the covariates is an inevitable problem in the analysis of survey data. Although many books and articles have described the collinearity problem and proposed strategies to understand, assess and handle its presence, the survey literature has not provided appropriate diagnostic tools to evaluate its impact on the regression estimation when the survey complexities are considered. The goal of this research is to extend and adapt the conventional ordinary least squares collinearity diagnostics to complex survey data when a linear model or generalized linear model is used. In this dissertation we have developed methods that generally have either a model-based or design-based interpretation. We assume that an analyst uses survey-weighted regression estimators to estimate both underlying model parameters (assuming a correctly specified model) and census-fit parameters in the finite population. Diagnostics statistics, variance inflation factors (VIFs), condition indexes and variance decomposition proportions are constructed to evaluate the impact of collinearity and determine which variables are involved. Survey weights are components of the diagnostic statistics and the estimated variances of the coefficients are obtained from design-consistent estimators which account for complex design features, e.g. clustering and stratification. Illustrations of these methods are given using data from a survey of mental health organizations and a household survey of health and nutrition. We demonstrate that specialized collinearity diagnostic statistics are needed to account for survey weights and complex finite population features that are reflected in the sample design and considered in the regression analysis

Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling

We evaluate the impact of probabilistically-constructed digital identity data collected from Sep. to Dec. 2017 (approx.), in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed identities in marketing. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.Comment: Accepted by WSDM 201

Itk and Fyn Make Independent Contributions to T Cell Activation

Itk is a member of the Btk/Tec/Itk family of nonreceptor protein tyrosine kinases (PTKs), and has been implicated in T cell antigen receptor (TCR) signal transduction. Lck and Fyn are the Src-family nonreceptor PTKs that are involved in TCR signaling. To address the question of how these members of different families of PTKs functionally contribute to T cell development and to T cell activation, mice deficient for both Itk and either Lck or Fyn were generated. The Itk/Lck doubly deficient mice exhibited a phenotype similar to that of Lck-deficient mice. The phenotype of the Itk/Fyn doubly deficient mice was similar to that of Itk deficient mice. However the Itk/Fyn doubly deficient mice exhibited a more severe defect in TCR-induced proliferation of thymocytes and peripheral T cells than did mice deficient in either kinase alone. These data support the notion that Itk and Fyn both make independent contributions to TCR-induced T cell activation

A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

A novel fifth-degree cubature Kalman filter is proposed to improve the accuracy of real-time orbit determination by ground-based radar. A fully symmetric cubature rule, approaching the Gaussian weighted integral of a nonlinear function in general form, is introduced, and the specific points and weights are calculated by matching the monomials of degree not greater than five with the exact values. On the basis of the above rule, a novel fifth-degree cubature Kalman filter, which can achieve a higher accuracy than UKF and CKF, is derived under the Bayesian filtering framework. Then, to describe the nonlinear system more accurately, the orbital dynamics equation with J2 perturbation is used as the state equation, and the nonlinear relationship between the radar measurement elements and orbital states is built as the measurement equation. The simulation results show that, compared with the traditional third-degree algorithm, the proposed fifth-degree algorithm has a higher accuracy of orbit determination

Competition in defense acquisition: Myths and facts

The article of record as published may be found at https://doi.org/10.1080/0743017890840540

8-Chloro-4-cyclo­hexyl-2H-1,4-benzoxazin-3(4H)-one

In the crystal structure of title compound, C14H16ClNO2, the cyclo­hexyl ring is in a chair conformation. The molecules are connected into centrosymmetric dimers via weak C—H⋯O hydrogen bonds

Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning

Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The proposed approach is extensively evaluated in a system-level simulator. The numerical results show that the proposed approach with inter-agent coordination outperforms the centralized approach in terms of delay and convergence. The proposed approach improves more than two-fold increase in resource efficiency as compared to the baseline approach