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This study investigates consumer switching behavior factors in platform businesses. This study reports a two‐phase study of switching behavior. The first phase, Latent Dirichlet Allocation topic analysis, was conducted by collecting reviews and comments to find out consumers' potential switching behavior factors. The second phase was the interview with the consumer who did the switching behavior as to why they made the switching behavior. Besides, among the potential switching behavior factors, actual switching behavior factors were compared. It links quantitative and qualitative phases in exploratory sequential mixed methods design. The switching behavior factors were identified by applying the push‐pull‐mooring framework. The result of the study on music platforms' push effects is price perception, pull effects are promotion and algorithm recommendation, and mooring effects are payment convenience, switching cost, and reward. In Over‐the‐top (OTT), platforms' push effect is a lack of content, pull effects are promotion, WOM, and diversity of content, and mooring effects are content expectations, social networks, payment convenience, and convenience of watching. In e‐commerce platforms' push effects are technical issues, delivery cost, and lack of trust, pull effects are service line extension, and convenience of use, mooring effects are reward, payment convenience, and convenience of access to my data. This work contributes to the growing body of studies investigating the switching behavior factor in each platform business. Also, the study makes recommendations to aid platform marketing managers in understanding the impact of push‐pull‐mooring variables. [ABSTRACT FROM AUTHOR]

The Gibbs sampler is a simple but very powerful algorithm used to simulate from a complex high‐dimensional distribution. It is particularly useful in Bayesian analysis when a complex Bayesian model involves a number of model parameters and the conditional posterior distribution of each component given the others can be derived as a standard distribution. In the presence of a strong correlation structure among components, however, the Gibbs sampler can be criticized for its slow convergence. Here we discuss several algorithmic strategies such as blocking, collapsing, and partial collapsing that are available for improving the convergence characteristics of the Gibbs sampler. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Markov Chain Monte CarloStatistical and Graphical Methods of Data Analysis > Bayesian Methods and TheoryStatistical and Graphical Methods of Data Analysis > Sampling [ABSTRACT FROM AUTHOR]

Color calibration of digital microscope images of stained biological slides may use a special slide (called a calibration slide) with known color samples that are usually filters. To evaluate quantitatively how well the calibration has been done by a particular system, another set of different known samples is needed, with transmittance spectra that are typical of stained biological material. A slide with such colors comprises what is called a reference slide. In this article, a method is described for selecting the colors for such a slide. A reference slide was created by this prescription and then used to evaluate two embodiments of a color management system for several microscope and illumination combinations. It was found that a set of 20 reference colors spans the gamut of colors produced by a particular kind of stain. Using the reference slide, Datacolor's ChromaCal™ system performance was quantitatively evaluated. The performance is robust to change of microscope and illumination and does not degrade much when a tungsten light is increased in intensity (hence in correlated color temperature) with only one initial calibration at a single color temperature followed by automatic white balancing of a traditional sort. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 577-584, 2015 [ABSTRACT FROM AUTHOR]

There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the Kaplan-Meier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]