Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

SODA WARS: THE EFFECT OF A SODA TAX ELECTION ON UNIVERSITY BEVERAGE SALES

We examine how soda sales changed due to the campaign attention and election outcome of a local excise tax on sugar‐sweetened beverages. Using panel data of beverage sales from university retailers in Berkeley, California, we estimate that soda purchases relative to control beverages significantly dropped immediately after the election, months before the tax was implemented in the city of Berkeley or on campus. Supplemental scanner data from off‐campus retailers reveal this result is not unique to the university setting. Our findings suggest soda tax media coverage and election outcomes can have larger effects on purchasing behavior than the tax itself. (JEL D12, H20, C23, I38, Q18)     

A reference activity model for smart factory design and improvement

Smart manufacturing systems (SMSs) are envisioned to contain highly automated and IT-driven production systems. To address the complexity that arises in such systems, a standard and holistic model for describing its activities and their interrelationships is needed. This paper introduces a factory design and improvement (FDI) activity model and illustrates a case study of FDI in an electromechanical component factory. In essence, FDI is a reference activity model that encompasses a range of manufacturing system activities for designing and improving a factory during its initial development and also its operational phases. The FDI model shows not only the dependency between activities and manufacturing control levels but also the pieces of information and software functions each activity relies on. We envision that the availability of these pieces of information in digital form to integrate across the software functions will increase the agility of factory design and improvement projects. Therefore, our future work lies in contributing to standards for exchanging such information.     

Employee communication,job engagement,and organizational commitment: A study of members of the Millennial Generation

The purpose of this study was to explore the relationship between job engagement and two key components of employee-organization relationships (EOR). Findings from a survey of members of the Millennial Generation (N = 539) in the United States indicate that job engagement mediates the relationship between employee communication and organizational commitment. It is concluded that when employees are engaged in their work, their commitment to the organization is strengthened and the likelihood of them leaving the organization decreases. Furthermore, an argument is made in light of the study’s findings that engagement and commitment work in concert to strengthen EORs overall. To foster engagement, organizations should remove obstacles to internal information flow and provide ongoing feedback to employees about individual and organizational issues.     

Toddlers Involve Their Caregiver to Help Another Person in Need

Three experiments with 18‐ to 35‐month‐old children (n = 169) studied toddler–caregiver interactions when being confronted with another person in need. In particular, we explored whether toddlers would request their caregiver to help a needy other when they are not able to help themselves. Children observed another person who needed help to accomplish a task, but were either not able to provide help as the object was out of reach (Experiment 1) or because an obstacle prevented children from interacting with the other person (Experiments 2, 2b). The experiments revealed the same developmental trend with 2.5‐year‐olds selectively involving their caregiver to help the needy other. The results are interpreted in terms of toddlers’ motivation to see others helped and with respect to their developing ability to actively involve others to regulate their emotions.     

Objective Bayesian analysis based on upper record values from two-parameter Rayleigh distribution with partial information

In the life test, predicting higher failure times than the largest failure time of the observed is an important issue. Although the Rayleigh distribution is a suitable model for analyzing the lifetime of components that age rapidly over time because its failure rate function is an increasing linear function of time, the inference for a two-parameter Rayleigh distribution based on upper record values has not been addressed from the Bayesian perspective. This paper provides Bayesian analysis methods by proposing a noninformative prior distribution to analyze survival data, using a two-parameter Rayleigh distribution based on record values. In addition, we provide a pivotal quantity and an algorithm based on the pivotal quantity to predict the behavior of future survival records. We show that the proposed method is superior to the frequentist counterpart in terms of the mean-squared error and bias through Monte carlo simulations. For illustrative purposes, survival data on lung cancer patients are analyzed, and it is proved that the proposed model can be a good alternative when prior information is not given.     

Shrinkage estimation of proportion via logit penalty

By releasing the unbiasedness condition, we often obtain more accurate estimators due to the bias–variance trade-off. In this paper, we propose a class of shrinkage proportion estimators which show improved performance over the sample proportion. We provide the “optimal” amount of shrinkage. The advantage of the proposed estimators is given theoretically as well as explored empirically by simulation studies and real data analyses.