The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention

Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A StudyUsing a Combination of Spatial Statistics and GIS Technology

Evidence shows that multiple factors, such as socio-economic status and access to health care facilities, affect tuberculosis (TB) incidence. However, there is limited literature available with respect to the correlation between socio-economic/health facility factors and tuberculosis incidence. This study aimed to explore the relationship between TB incidence and socio-economic/health service predictors in the study settings. A retrospective spatial regression analysis was carried out based on new sputum smear-positive pulmonary TB cases in Beijing districts. Global Moran’s I analysis was adopted to detect the spatial dependency followed by spatial regression models (spatial lag model, and spatial error model) along with the ordinary least square model were applied to examine the correlation between TB incidence and predictors. A high incidence of TB was seen in densely populated districts in Beijing, e.g., Haidian, Mentougou, and Xicheng. After comparing the R2, log-likelihood, and Akaike information criterion (AIC) values among three models, the spatial error model (R2 = 0.413; Log Likelihood = −591; AIC = 1199.76) identified the best model fit for the spatial regression model. The study showed that the number of beds in health institutes (p \u3c 0.001) and per capita gross domestic product (GDP) (p = 0.025) had a positive effect on TB incidence, whereas population density (p \u3c 0.001) and migrated population (p \u3c 0.001) had an adverse impact on TB incidence in the study settings. High TB incidence districts were detected in urban and densely populated districts in Beijing. Our findings suggested that socio-economic predictors influence TB incidence. These findings may help to guide TB control programs and promote targeted intervention

Volatility Spillover in India, USA and Japan Investigation of Recession Effects

In the past decades, there has been an unprecedented increase in cross border transactions between countries in terms of goods and financial flows. This integration has been fuelled by search of lower risk investments, risk diversification, search for cost effective and more efficient factors of production and dreams of global dominance in the world wide market place. An important result of these capital flows was its impact on linkages of global asset returns and spillover of volatility from one capital market to another. This study aims to understand the spillover effect between the US, the Japan capital markets and Indian equity index (Sensex). We analyze whether the volatility spillover is contemporaneous (directly in the very same day), or dynamic/lagged (with one day lag). A GARCH (1,1) model of modelling volatility has been undertaken for this purpose. This paper concludes that contemporary volatility of the Japan capital markets influenced Sensex in the pre-recession period but in the post recession there was no significant contemporaneous spillover from USA and Japan capital markets to Sensex. However, US became a significant factor while considering dynamic spillover in the post recession era. Also, there was no bidirectional volatility spillover from India to US. But, the study showed evidence of dynamic volatility spillover from Indian market to Japanese Capital market

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system

Profile Likelihood Biclustering

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at https://github.com/patperry/biclustp

A Statistical Method for Estimating Luminosity Functions using Truncated Data

The observational limitations of astronomical surveys lead to significant statistical inference challenges. One such challenge is the estimation of luminosity functions given redshift $z$ and absolute magnitude $M$ measurements from an irregularly truncated sample of objects. This is a bivariate density estimation problem; we develop here a statistically rigorous method which (1) does not assume a strict parametric form for the bivariate density; (2) does not assume independence between redshift and absolute magnitude (and hence allows evolution of the luminosity function with redshift); (3) does not require dividing the data into arbitrary bins; and (4) naturally incorporates a varying selection function. We accomplish this by decomposing the bivariate density into nonparametric and parametric portions. There is a simple way of estimating the integrated mean squared error of the estimator; smoothing parameters are selected to minimize this quantity. Results are presented from the analysis of a sample of quasars.Comment: 30 pages, 9 figures, Accepted for publication in Ap

Latent class analysis for segmenting preferences of investment bonds

Market segmentation is a key component of conjoint analysis which addresses consumer preference heterogeneity. Members in a segment are assumed to be homogenous in their views and preferences when worthing an item but distinctly heterogenous to members of other segments. Latent class methodology is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and a-priori segmentation. The main benefit of Latent class models is that market segment membership and regression parameters of each derived segment are estimated simultaneously. The Latent class model presented in this paper uses mixtures of multivariate conditional normal distributions to analyze rating data, where the likelihood is maximized using the EM algorithm. The application focuses on customer preferences for investment bonds described by four attributes; currency, coupon rate, redemption term and price. A number of demographic variables are used to generate segments that are accessible and actionable.peer-reviewe

Adaptive numerical designs for the calibration of computer codes

Making good predictions of a physical system using a computer code requires the inputs to be carefully specified. Some of these inputs called control variables have to reproduce physical conditions whereas other inputs, called parameters, are specific to the computer code and most often uncertain. The goal of statistical calibration consists in estimating these parameters with the help of a statistical model which links the code outputs with the field measurements. In a Bayesian setting, the posterior distribution of these parameters is normally sampled using MCMC methods. However, they are impractical when the code runs are high time-consuming. A way to circumvent this issue consists of replacing the computer code with a Gaussian process emulator, then sampling a cheap-to-evaluate posterior distribution based on it. Doing so, calibration is subject to an error which strongly depends on the numerical design of experiments used to fit the emulator. We aim at reducing this error by building a proper sequential design by means of the Expected Improvement criterion. Numerical illustrations in several dimensions assess the efficiency of such sequential strategies