Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

On the zeros of the Abelian integrals for a class of Liénard systems

A planar polynomial differential system has a finite number of limit cycles. However, finding the upper bound of the number of limit cycles is an open problem for the general nonlinear dynamical systems. In this paper, we investigated a class of Liénard systems of the form x'=y, y'=f(x)+y g(x) with deg f=5 and deg g=4. We proved that the related elliptic integrals of the Liénard systems have at most three zeros including multiple zeros, which implies that the number of limit cycles bifurcated from the periodic orbits of the unperturbed system is less than or equal to 3.

The influence of ad model ethnicity and self-referencing on attitudes evidence from New Zealand

This paper examines consumers self-referencing as a mechanism for explaining ethnicity effects in advertising. Data was collected from a 2 (model ethnicity: Asian, white) x 2 (product stereotypicality: stereotypical, non-stereotypical) experiment. Measured independent variables included participant ethnicity and self-referencing. Results shows that (1) Asian exhibit greater self-referencing of Asian models than whites do; (2) self-referencing mediates ethnicity effects on attitude ( ie, attitude towards the model, attitude toward the add, brand attitude, and purchase intentions); (3) high self-referencing Asian have more favourable attitude towards the add and purchase intentions than low self referencing Asians; and (4) Asian models advertising atypical products generate more self-referencing and more favourable attitudes toward the model, A, and purchase intentions for both Asians and whites.

A conceptual framework for a systematic mapping of layout design principles by using Yin and Yang theory

There are various principles for layout design such as balance, rhythm, unity and harmony, but each principle has often been introduced as a separate concept rather than within an integrated and systematic structure, so that designers and design students have to keep practices for the acquisition of skills. The paper seeks to develop a conceptual framework for a systematic mapping of layout design principles by using Yin and Yang and the Five Elements. Yin and Yang theory explains all natural phenomena with its own conceptual model and facilitates finding harmony and balance between the visual elements in terms of systematic and organic relations. Most common and well-known layout design principles are defined with 10 different resources such as design books and articles, and have been remapped following with the structure of Yin and Yang and the Five Elements. A systematic framework explaining the relationships of design principles was created and 32 design students participated in its efficiency test. The outcome suggests there is a high possibility that the framework can be used in professional fields and design education.

Book Review: "Rethinking Learning in Early Childhood Education" written by Nicola Yelland, Libby Lee, Maureen O’Rourke & Cathie Harrison

There is a growing body of work that responds to the impact of the rapid uptake of information and communication technology (ICT) on education (Buckingham, 2003; Cheung, 2003; Cuban, 2003; Leung, 2003; Prensky, 2005; Green & Hannon, 2007; Brooks-Gunn & Donahue, 2008; Lyman et al, 2008). Mostly, this work has been positioned in the context of upper-primary or secondary classrooms. More recently, there has been a growing call for research about the impact of ICT on the early years or in early childhood contexts. This text initiates a response to that call. The authors concur that today’s children are a generation who create, learn, work, play and communicate very differently from their parents and teachers (Buckingham, 2003), and that classroom activity needs to reflect this difference.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Integrated genomic characterization of endometrial carcinoma

We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours.

Susan Carson in conversation with Donna Lee Brien on research higher degree examination administration

This paper is the edited transcript of a conversation between Susan Carson and Donna Lee Brien about an administrator’s perspective of the process of examining doctoral theses in the creative industries. Susan was central to the process in the Faculty of Creative Industries from 2008 to 2012, and has overseen the carriage of examination for creative arts theses in the creative industries disciplines of creative writing, performance studies, media and communication, journalism, film and television, visual arts, and interaction and visual design.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.