Bridge regression: Adaptivity and group selection

In high-dimensional regression problems regularization methods have been a popular choice to address variable selection and multicollinearity. In this paper we study bridge regression that adaptively selects the penalty order from data and produces flexible solutions in various settings. We implement bridge regression based on the local linear and quadratic approximations to circumvent the nonconvex optimization problem. Our numerical study shows that the proposed bridge estimators are a robust choice in various circumstances compared to other penalized regression methods such as the ridge, lasso, and elastic net. In addition, we propose group bridge estimators that select grouped variables and study their asymptotic properties when the number of covariates increases along with the sample size. These estimators are also applied to varying-coefficient models. Numerical examples show superior performances of the proposed group bridge estimators in comparisons with other existing methods.     

Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation

Estimation and prediction in generalized linear mixed models are often hampered by intractable high dimensional integrals. This paper provides a framework to solve this intractability, using asymptotic expansions when the number of random effects is large. To that end, we first derive a modified Laplace approximation when the number of random effects is increasing at a lower rate than the sample size. Second, we propose an approximate likelihood method based on the asymptotic expansion of the log-likelihood using the modified Laplace approximation which is maximized using a quasi-Newton algorithm. Finally, we define the second order plug-in predictive density based on a similar expansion to the plug-in predictive density and show that it is a normal density. Our simulations show that in comparison to other approximations, our method has better performance. Our methods are readily applied to non-Gaussian spatial data and as an example, the analysis of the rhizoctonia root rot data is presented.     

Inference for the generalized Rayleigh distribution based on progressively censored data

In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.     

Detection of structural breaks in a time-varying heteroskedastic regression model

A Bayesian method for estimating a time-varying regression model subject to the presence of structural breaks is proposed. Heteroskedastic dynamics, via both GARCH and stochastic volatility specifications, and an autoregressive factor, subject to breaks, are added to generalize the standard return prediction model, in order to efficiently estimate and examine the relationship and how it changes over time. A Bayesian computational method is employed to identify the locations of structural breaks, and for estimation and inference, simultaneously accounting for heteroskedasticity and autocorrelation. The proposed methods are illustrated using simulated data. Then, an empirical study of the Taiwan and Hong Kong stock markets, using oil and gas price returns as a state variable, provides strong support for oil prices being an important explanatory variable for stock returns.     

The impact of ‘being assessed’ by a disabled children's team: a personal reflective account

The body of ‘service user’ literature confirms the value of parental perceptions of child and family social work and the insight parents and others can offer. This paper lends my voice to the literature regarding parental perceptions, inspired by the work of Pamela Davies, who provided a personal account of the impact of a child protection investigation. This paper draws upon my experiences of being a father of two ‘disabled children’ and undergoing an assessment of need. This paper seeks to draw attention to issues of choice, power imbalances and the role of expertise. My personal experience of undergoing an assessment was that it was an emotionally fraught process, for the duration of the assessment, our family stress increased and we had a sense of having to ‘battle’ for the support we needed. As such, my personal experience fits well with the wider body of literature, which highlights the increased stress of caring for children with additional needs, the challenges of ‘fitting’ disabled children into the frameworks used to assess all children and the difficulty for parents and professionals in distinguishing between ‘normal’ parenting responsibilities and the additional responsibilities of caring for a disabled child.     

Logistic模型的系数比较问题及解决策略：一个综述

本文介绍了Logistic模型中经常被忽视的系数比较问题,包括同一样本在不同模型间的系数比较和在不同样本或子群体间的模型系数比较。研究者往往会沿袭线性回归模型的系数比较方法,但这是不恰当的,因为Logistic模型存在未被观测到的异质性（残差变异）问题,所以模型间系数不能进行简单的直接比较。根据已有研究,本文总结了解决这一问题的五种策略,分别是“y*标准化”、KHB分解、异质选择模型、平均偏效应（APE）和线性概率模型（LPM）,然后利用CGSS2006数据,以教育递进率模型为例,比较这些解决策略的异同,最后总结这些策略的特征及适用情况。     

Where Publics May Touch

ABSTRACT

In response to the unique sensory modes of blind and partially sighted communities, tactile and multisensory tours and exhibition components of museum collections are gradually becoming more common in North America and Europe. Such initiatives are often intended to give equal forms of access for diverse participating publics. This article provides an auto-ethnographic report on the tactile tours offered by The National Gallery of Canada (Ottawa) program “Stimulating the Senses,” and gives context for sensorial models at play in Western museums today. Museum strategies that encourage intersensorial awareness and access are also discussed. Attention is given to the performative properties of these tours, and the variety of encounter they encourage between publics. This article also includes accounts of the program by its supervisor and coordinator, as well as from a participant. Key questions this article explores are: do such tours in fact give equal access; what motivates the development of such programs in Canadian institutions; and what outcomes are realized through these programs?     

Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions

A full Bayesian approach based on ordinary differential equation (ODE)-penalized B-splines and penalized Gaussian mixture is proposed to jointly estimate ODE-parameters, state function and error distribution from the observation of some state functions involved in systems of affine differential equations. Simulations inspired by pharmacokinetic (PK) studies show that the proposed method provides comparable results to the method based on the standard ODE-penalized B-spline approach (i.e. with the Gaussian error distribution assumption) and outperforms the standard ODE-penalized B-splines when the distribution is not Gaussian. This methodology is illustrated on a PK data set.     

Bayesian age-stratified joinpoint regression model: an application to lung and brain cancer mortality

Joinpoint regression model identifies significant changes in the trends of the incidence, mortality, and survival of a specific disease in a given population. The purpose of the present study is to develop an age-stratified Bayesian joinpoint regression model to describe mortality trend assuming that the observed counts are probabilistically characterized by the Poisson distribution. The proposed model is based on Bayesian model selection criteria with the smallest number of joinpoints that are sufficient to explain the Annual Percentage Change. The prior probability distributions are chosen in such a way that they are automatically derived from the model index contained in the model space. The proposed model and methodology estimates the age-adjusted mortality rates in different epidemiological studies to compare the trends by accounting the confounding effects of age. In developing the subject methods, we use the cancer mortality counts of adult lung and bronchus cancer, and brain and other Central Nervous System cancer patients obtained from the Surveillance Epidemiology and End Results data base of the National Cancer Institute.     

A graphical evaluation of logistic ridge estimator in mixture experiments

In comparison to other experimental studies, multicollinearity appears frequently in mixture experiments, a special study area of response surface methodology, due to the constraints on the components composing the mixture. In the analysis of mixture experiments by using a special generalized linear model, logistic regression model, multicollinearity causes precision problems in the maximum-likelihood logistic regression estimate. Therefore, effects due to multicollinearity can be reduced to a certain extent by using alternative approaches. One of these approaches is to use biased estimators for the estimation of the coefficients. In this paper, we suggest the use of logistic ridge regression (RR) estimator in the cases where there is multicollinearity during the analysis of mixture experiments using logistic regression. Also, for the selection of the biasing parameter, we use fraction of design space plots for evaluating the effect of the logistic RR estimator with respect to the scaled mean squared error of prediction. The suggested graphical approaches are illustrated on the tumor incidence data set.