Abstract: We study the spatial distribution of clusters associated to the aftershocks of the megathrust Maule earthquake MW 8.8 of 27 February 2010. We used a recent clustering method which hinges on a nonparametric estimation of the underlying probability density function to detect subsets of points forming clusters associated with high density areas. In addition, we estimate the probability density function using a nonparametric kernel method for each of these clusters. This allows us to identify a set of regions where there is an association between frequency of events and coseismic slip. Our results suggest that high coseismic slip is spatially related to high aftershock frequency.
Abstract: PSA measurements are used to assess the risk for prostate cancer. PSA range and PSA kinetics such as PSA velocity have been correlated with in creased cancer detection and assist the clinician in deciding when prostate biopsy should be performed. Our aim is to evaluate the use of a novel, maxi mum likelihood estimation - prostate specific antigen (MLE-PSA) model for predicting the probability of prostate cancer using serial PSA measurements combined with PSA velocity in order to assess whether this reduces the need for prostate biopsy. A total of 1976 Caucasian patients were included. All these patients had at least 6 PSA serial measurements; all underwent trans-rectal biopsy with minimum 12 cores within the past 10 years. A multivariate logistic re gression model was developed using maximum likelihood estimation (MLE) based on the following parameters (age, at least 6 PSA serial measurements, baseline median natural logarithm of the PSA (ln(PSA)) and PSA velocity (ln(PSAV)), baseline process capability standard deviation of ln(PSA) and ln(PSAV), significant special causes of variation in ln(PSA) and ln(PSAV) detected using control chart logic, and the volatility of the ln(PSAV). We then compared prostate cancer probability using MLE-PSA to the results of prostate needle biopsy. The MLE-PSA model with a 50% cut-off probability has a sensitivity of 87%, specificity of 85%, positive predictive value (PPV) of 89%, and negative predictive value (NPV) of 82%. By contrast, a single PSA value with a 4ng/ml threshold has a sensitivity of 59%, specificity of 33%, PPV of 56%, and NPV of 36% using the same population of patients used to generate the MLE-PSA model. Based on serial PSA measurements, the use of the MLE-PSA model significantly (p-value < 0.0001) improves prostate cancer detection and reduces the need for prostate biopsy.
Abstract: Price limits are applied to control risks in various futures mar kets. In this research, we proposed an adapted autoregressive model for the observed futures return by introducing dummy variables that represent limit moves. We also proposed a stochastic volatility model with dummy variables. These two models are used to investigate the existence of price de layed discovery effect and volatility spillover effect from price limits. We give an empirical study of the impact of price limits on copper and natural rubble futures in Shanghai Futures Exchange (SHFE) by using MCMC method. It is found that price limits are efficient in controlling copper futures price, but the rubber futures price is distorted significantly. This implies that the effects of price limits are significant for products with large fluctuation and frequent limits hit.
Abstract: Cancer is a complex disease where various types of molecular aber rations drive the development and progression of malignancies. Among the diverse molecular aberrations, inherited and somatic mutations on DNA se quences are considered as major drivers for oncogenesis. The complexity of somatic alterations is revealed from large-scale investigations of cancer genomes and robust methods for interring the function of genes. In this review, we will describe sequence mutations of several cancer-related genes and discuss their functional implications in cancer. In addition, we will in troduce the on-line resources for accessing and analyzing sequence mutations in cancer. We will also provide an overview of the statistical and computa tional approaches and future prospects to conduct comprehensive analyses of the somatic alterations in cancer genomes.
Abstract: We group approaches to modeling correlated binary data accord ing to data recorded cross-sectionally as opposed to data recorded longi tudinally; according to models that are population-averaged as opposed to subject-specific; and according to data with time-dependent covariates as opposed to time-independent covariates. Standard logistic regression mod els are appropriate for cross-sectional data. However, for longitudinal data, methods such as generalized estimating equations (GEE) and generalized method of moments (GMM) are commonly used to fit population-averaged models, while random-effects models such as generalized linear mixed mod els (GLMM) are used to fit subject-specific models. Some of these methods account for time-dependence in covariates while others do not. This paper addressed these approaches with an illustration using a Medicare dataset as it relates to rehospitalization. In particular, we compared results from standard logistic models, GEE models, GMM models, and random-effects models by analyzing a binary outcome for four successive hospitalizations. We found that these procedures address differently the correlation among responses and the feedback from response to covariate. We found marginal GMM logistic regression models to be more appropriate when covariates are classified as time-dependent in comparison to GEE models. We also found conditional random-intercept models with time-dependent covariates decom posed into components to be more appropriate when time-dependent covari ates are present in comparison to ordinary random-effects models. We used the SAS procedures GLIMMIX, NLMIXED, IML, GENMOD, and LOGIS TIC to analyze the illustrative dataset, as well as unique programs written using the R language.
Abstract: This paper presents a permutation test for the incomplete pairs setting. This situation arises in both observational and experimental studies when some of the data are in the form of a paired sample and the rest of the data comprise two independent samples. The proposed method uses the data from the two types of samples to test the difference between the mean responses. Our test statistic combines the observed mean difference for the complete pairs with the difference between the two means of the independent samples. The randomizations are carried out as is typically done with standard permutation tests for paired and independent samples. We show by a simulation study that our statistic performs well in comparison to other methods.
Abstract: The five parameter Kumaraswamy generalized gamma model (Pas coa et al., 2011) includes some important distributions as special cases and it is very useful for modeling lifetime data. We propose an extended version of this distribution by assuming that a shape parameter can take negative values. The new distribution can accommodate increasing, decreasing, bath tub and unimodal shaped hazard functions. A second advantage is that it also includes as special models reciprocal distributions such as the recipro cal gamma and reciprocal Weibull distributions. A third advantage is that it can represent the error distribution for the log-Kumaraswamy general ized gamma regression model. We provide a mathematical treatment of the new distribution including explicit expressions for moments, generating function, mean deviations and order statistics. We obtain the moments of the log-transformed distribution. The new regression model can be used more effectively in the analysis of survival data since it includes as sub models several widely-known regression models. The method of maximum likelihood and a Bayesian procedure are used for estimating the model pa rameters for censored data. Overall, the new regression model is very useful to the analysis of real data.
Abstract: In this paper we introduce a Bayesian analysis of a spherical distri bution applied to rock joint orientation data in presence or not of a vector of covariates, where the response variable is given by the angle from the mean and the covariates are the components of the normal upwards vector. Standard simulation MCMC (Markov Chain Monte Carlo) methods have been used to obtain the posterior summaries of interest obtained from Win Bugs software. Illustration of the proposed methodology are given using a simulated data set and a real rock spherical data set from a hydroelectrical site.
Abstract: The application of linear mixed models or generalized linear mixed models to large databases in which the level 2 units (hospitals) have a wide variety of characteristics is a problem frequently encountered in studies of medical quality. Accurate estimation of model parameters and standard errors requires accounting for the grouping of outcomes within hospitals. Including the hospitals as random effect in the model is a common method of doing so. However in a large, diverse population, the required assump tions are not satisfied, which can lead to inconsistent and biased parameter estimates. One solution is to use cluster analysis with clustering variables distinct from the model covariates to group the hospitals into smaller, more homogeneous groups. The analysis can then be carried out within these groups. We illustrate this analysis using an example of a study of hemoglobin A1c control among diabetic patients in a national database of United States Department of Veterans’ Affairs (VA) hospitals.
Abstract: The Lee-Carter model and its extensions are the most popular methods in the field of forecasting mortality rate. But, in spite of introducing several different methods in forecasting mortality rate so far, there is no general method applicable to all situations. Singular Spectrum Analysis (SSA) is a relatively new, powerful and non parametric time series analysis that its capability in forecasting different time series has been proven in the various sciences. In this paper, we investigate the feasibility of using the SSA to construct mortality forecasts. We use the Hyndman-Ullah model, which is a new extension of Lee-Carter model, as a benchmark to evaluate the performance of the SSA for mortality forecasts in France data sets.