Inference for Weibull distribution under adaptive Type-I progressive hybrid censored competing risks data

In this paper, a competing risks model is considered under adaptive type-I progressive hybrid censoring scheme (AT-I PHCS). The lifetimes of the latent failure times have Weibull distributions with the same shape parameter. We investigate the maximum likelihood estimation of the parameters. Bayes estimates of the parameters are obtained based on squared error and LINEX loss functions under the assumption of independent gamma priors. We propose to apply Markov Chain Monte Carlo (MCMC) techniques to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. To evaluate the performance of the estimators, a simulation study is carried out.     

The influence of land use type and municipal context on urban tree species diversity

Recent research has focused on the ways urban forest patterns vary in relation to level of urbanization and socioeconomic characteristics, with most studies limited to one urban land use type or multiple non-differentiate land uses. Additionally, the majority of studies examining urban forest patterns focus on canopy cover extent, with less attention given to patterns of species diversity. This study explores how tree species diversity varies across different urban land uses and municipal boundaries to better understand the role of land use types in shaping urban forest patterns. The goal is addressed through an exploration of plot-level tree data in the urban municipalities of Peel Region located in the Greater Toronto Area (Ontario, Canada). Species composition and standard diversity metrics are calculated for eight land use types and four municipalities. Our results show that differences in diversity metrics and species composition are greater between urban land uses than municipalities. Moreover, Peel’s urban forest has relatively high alpha diversity but many species are present on only one land use type. The results suggest that different causal processes are associated with each land use type, and that urban forest managers should adopt land use-specific strategies to meet species composition goals within the urban forest.     

Experienced Well-Being and Labor Market Status: The Role of Pleasure and Meaning

Social Indicators Research - This paper examines the experienced well-being of employed and unemployed workers. We use the survey-adapted Day Reconstruction Method of the Innovation Sample of the...     

Estimation and diagnostic analysis in skew-generalized-normal regression models

The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

Optimal design of repetitive group sampling plans for Weibull and gamma distributions with applications and comparison to the Birnbaum–Saunders distribution

In this paper, we propose a multiple deferred state repetitive group sampling plan which is a new sampling plan developed by incorporating the features of both multiple deferred state sampling plan and repetitive group sampling plan, for assuring Weibull or gamma distributed mean life of the products. The quality of the product is represented by the ratio of true mean life and specified mean life of the products. Two points on the operating characteristic curve approach is used to determine the optimal parameters of the proposed plan. The plan parameters are determined by formulating an optimization problem for various combinations of producer's risk and consumer's risk for both distributions. The sensitivity analysis of the proposed plan is discussed. The implementation of the proposed plan is explained using real-life data and simulated data. The proposed plan under Weibull distribution is compared with the existing sampling plans. The average sample number (ASN) of the proposed plan and failure probability of the product are obtained under Weibull, gamma and Birnbaum–Saunders distributions for a specified value of shape parameter and compared with each other. In addition, a comparative study is made between the ASN of the proposed plan under Weibull and gamma distributions.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

Social Evolution and Environmental Context: Explanative Pluralism and Potentiality

We identify four common, but we argue fallacious, reasons social scientists object to theories that rely on environmental conditions to explain variation in the evolution of societies. First, some sociologists exclude environmental forces as beyond the domain of sociology. Second, environmental theories are often pejoratively labeled “determinist,” and rejected on those grounds. Third, environmental theories are regularly dismissed as being functionalist. Fourth, environmental theories are frequently charged with being Eurocentric and serving to justify colonialism and social inequalities. We argue that although there are examples of theories that fail in these regards, there is no inherent reason that environmental theories are more prone to these limitations and problems than are more sociologically “pure” theories. We argue that sociology should strive to be more transdisciplinary and can benefit from consideration of environmental conditions.     

Anti-N-methyl-D-aspartate receptor antibody encephalitis: An important cause of encephalitis in young adults. A report of two cases

Encephalitis is a clinical syndrome which can include altered mental status, motor and sensory deficits, altered behavior including personality changes, speech and movement disorders and seizures. While the overall incidence of encephalitis is not known, it is common enough that most pediatric and adolescent medicine physicians will have seen at least one case. Peak times of risk include the newborn period and middle-to-late adolescence.¹ Dalmau J, Tüzün E, Wu HY, et al. Paraneoplastic anti-N-methyl-D-aspartate receptor encephalitis associated with ovarian teratoma. Ann Neurol. 2007 Jan;61(1):25–36. doi:10.1002/ana.21050. PMID: 17262855.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] It is important for clinicians to have a working knowledge of the broad range of encephalitis etiologies: viral, post-viral, toxic, auto-immune, and paraneoplastic. We discuss two cases of encephalitis in young adult women with ovarian teratoma and production of anti-N-methyl-D-aspartate receptor (NMDAR) antibodies.