Estimating capability index in multivariate processes using bootstrap sequential sampling procedures

Capability indices in both univariate and multivariate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a measure of the ratio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared.

Does body satisfaction influence self-esteem in adolescents' daily lives? An experience sampling study

This study examined, within the context of the Contingencies of Self-Worth model, state-based associations between self-esteem and body satisfaction using the experience sampling method. One hundred and forty-four adolescent girls (mean age = 14.28 years) completed up to 6 assessments per day for one week using Palm Digital Assistants, in addition to baseline measures of trait body satisfaction and self-esteem. Results showed considerable variation in both state-based constructs within days, and evidence of effects of body satisfaction on self-esteem, but not vice versa. Although these state-based associations were small in size and weakened as the time lag between assessments increased for the sample as a whole, individual differences in the magnitude of these effects were observed and predicted by trait self-esteem and body satisfaction. Collectively, these findings offer support for key tenets of the Contingencies of Self-Worth model.

Monitoring the ungulate prey of the Komodo dragon Varanus komodoensis: distance sampling or faecal counts?

Monitoring the abundances of prey is important for informing the management of threatened and endangered predators. We evaluated the usefulness of faecal counts and distance sampling for monitoring the abundances of rusa deer Rusa timorensis, feral pig Sus scrofa and water buffalo Bubalus bubalis, the three key prey of the Komodo dragon Varanus komodoensis, at 11 sites on five islands in and around Komodo National Park, eastern Indonesia. We used species-specific global detection functions and cluster sizes (i.e. multiple covariates distance sampling) to estimate densities of rusa deer and feral pig, but there were too few observations to estimate densities of water buffalo. Rusa deer densities varied from from 2.5 to 165.5 deer/km2 with coefficients of variation (CVs) of 15-105%. Feral pig densities varied from 0.0 to 25.2 pigs/km 2 with CVs of 25-106%. There was a positive relationship between estimated faecal densities and estimated population densities for both rusa deer and feral pig: the form of the relationship was non-linear for rusa deer, but there was similar support for linear and non-linear relationships for feral pig. We found that faecal counts were more useful when ungulate densities were too low to estimate densities with distance sampling. Faecal count methods were also easier for field staff to conduct than distance sampling. Because spatial and temporal variation in ungulate density is likely to influence the population dynamics of the Komodo dragon, we recommend that annual monitoring of ungulates in and around Komodo National Park be undertaken using distance sampling and faecal counts. The relationships reported here will also be useful for managers establishing monitoring programmes for feral pig, rusa deer and water buffalo elsewhere in their native and exotic ranges.

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical Bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the Corr-wddCRF also shows considerable accuracy.