Hard thresholding regression

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ?₀‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.     

Ordering results for series and parallel systems comprising heterogeneous exponentiated Weibull components

In this paper, we discuss the usual stochastic and reversed hazard rate orders between the series and parallel systems from two sets of independent heterogeneous exponentiated Weibull components. We also obtain the results concerning the convex transform orders between parallel systems and obtain necessary and sufficient conditions under which the dispersive and usual stochastic orders, and the right spread and increasing convex orders between the lifetimes of the two systems are equivalent. Finally, in the multiple-outlier exponentiated Weibull models, based on weak majorization and p-larger orders between the vectors of scale and shape parameters, some characterization results for comparing the lifetimes of parallel and series systems are also established, respectively. The results of this paper can be used in practical situations to find various bounds for the important aging characteristics of these systems.     

Building Virtual Age-Friendly Communities in Minecraft

A key theme of Age-Friendly Honolulu’s initiative is intergenerational exchange. The initiative believes that youth need to be aware of aging issues, demographic change, and think critically about the potential impact on their lives. The goal of the Age-Friendly Honolulu Youth Engagement Initiative is to teach youth about the role of the built environment and community features in promoting active aging and engagement among older adults. This article describes a new program in the field that teaches youth and older adults to assess the built environment and then use design thinking and Minecraft (a popular video game in which players use 3-D blocks to create virtual lands) to develop and envision ideas to improve the livability of their neighborhood.     

COST-VOLUME-PROFIT ANALYSIS IN STOCHASTIC PROGRAMMING MODELS

This paper applies mathematical programming to cost-volume-profit (CVP) analysis under contribution margin uncertainty. Three CVP probabilistic chance-constraint models based on various safety-first criteria for decisions under uncertainty are presented and compared. It is shown that a break-even segment of the mean-standard deviation frontier is a set of optimal solutions for the proposed models. An operational parametric quadratic programming (QP) model is constructed, and the efficiency frontier is generated. The procedures for locating an optimal solution on the efficiency frontier are then presented. The recommended QP procedure offers both technical relief from the computational difficulties posed by the probabilistic constraints and a desired flexibility in generating and presenting the relevant information for decisions under uncertainty.     

走出误区——浅论酒店公关策划的媒介选择

媒介选择是酒店公关策划的重要内容之一,但在中国酒店公关策划的实践中,此环节往往出现种种偏误,从而妨碍了公关功能的有效发挥.造成此种状况的原因,既有意识、理念方面的因素,也有运作模式上的障碍.为更有效利用各种媒介,实现酒店公关工作的目标,在进行媒介选择时,酒店公关策划者应依据公关策划目标、公关对象特点、媒介传播规律和效益原则来选择酒店公关信息的传播媒介.     

USING CONJOINT ANALYSIS TO PREDICT TRIAL AND REPEAT-PURCHASE PATTERNS OF NEW FREQUENTLY PURCHASED PRODUCTS*

A new approach is proposed to evaluate new-product opportunities. This approach uses the distribution of brand-purchase probability of the new product over a population of potential customers and the outputs from conjoint analysis. The heterogeneous distribution of brand-purchase probability is expressed by a beta binomial brand-choice model compounded with a negative binomial product-class purchase-incidence model. The resulting model provides a way to predict trial and repeat-purchase patterns of new-product concepts. The paper discusses the development of the model. It also discusses issues of measurement, estimation, testing, and implementation of the proposed approach based on actual empirical data.     

A Multi-Dimensional Conceptual Framework for Trauma-Informed Practice in Addictions Programming

Previous research has conceptualized trauma-informed practice in relation to five key values: safety, trust, choice, collaboration, and empowerment. This research identifies key organizational, programmatic, and interpersonal characteristics in community-based residential addictions treatment programming that exemplify each of these principles. Utilizing qualitative research methods, involving open-ended, one to one interviews with clients in residential substance misuse treatment (n = 41), respondents identified the importance of experiencing “safety” in relation to physical safety, confidentiality, reassurance, rule enforcement, and peer relationships. “Trust” was manifested in sharing, staff availability, nonjudgmental interactions, positive relationship dynamics, and caring. “Choice” was articulated in relation to individual needs, participation, opportunities, and focus of efforts. “Collaboration” was characterized in relation to opportunities for feedback, planning, goal setting, specificity, and support. Finally, “empowerment” was characterized by comfort in sharing, trigger management, trauma awareness, and understanding. The findings provide a conceptual framework for a trauma-informed social services organizational practice environment. Findings can inform adaptations to social service delivery processes and programs to become aligned with the values of trauma-informed practice. Future research can build on this framework by testing the study findings with quantitative methods along with replicating current methods in other social service delivery sectors.     

Semi-parametric segmentation of multiple series using a DP-Lasso strategy

We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions.     

Computing A-optimal and E-optimal designs for regression models via semidefinite programming

In semidefinite programming (SDP), we minimize a linear objective function subject to a linear matrix being positive semidefinite. A powerful program, SeDuMi, has been developed in MATLAB to solve SDP problems. In this article, we show in detail how to formulate A-optimal and E-optimal design problems as SDP problems and solve them by SeDuMi. This technique can be used to construct approximate A-optimal and E-optimal designs for all linear and nonlinear regression models with discrete design spaces. In addition, the results on discrete design spaces provide useful guidance for finding optimal designs on any continuous design space, and a convergence result is derived. Moreover, restrictions in the designs can be easily incorporated in the SDP problems and solved by SeDuMi. Several representative examples and one MATLAB program are given.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.