Search

BackgroundThere is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission.MethodsWe analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set.FindingsThe LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors.InterpretationThis simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available athttps://mdscore.net/.Summary boxWhat is already known on this topic?Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice.What this study adds?A newly developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score.

Impaired water quality continues to be a serious problem in surface waters worldwide. Despite extensive regulatory water quality monitoring implemented by the Government of India over the past two decades, the spatial and temporal resolution of water quality observations, the range of monitored contaminants and data related to characterisation of point source effluents are still limited. In addition, discharge data for trans-boundary rivers is considered sensitive information and is not publicly available. Hence, quantifying, and mitigating pollutant loads and planning effective mitigation strategies are hindered by data paucity and there is an urgent need for the development of decision support tools (DST) that can account for these uncertainties.In this study, we tested the application of a probabilistic DST based on Bayesian Belief Networks, to evaluate pollution risk from nutrients (phosphate, nitrate, ammonia), sediments and heavy metals (Cd, Cr, Cu, Pb, Zn) in the Ramganga river basin (30,839 km2), the first major tributary of the Ganga in the state of Uttar Pradesh, India, and is understood to be a significant source of pollution into the Ganga River, contributed from a range of industries, domestic sources and intensive farming practices. Bayesian belief networks are graphical causal models that enable to integrate observational data (both spatial and temporal) with data from literature and expert knowledge within a probabilistic framework, whilst accounting for uncertainty.The objectives of this study were to 1) develop a parsimonious conceptual model of the system that allows harnessing diverse but limited data, 2) evaluate the important components of the system to inform further data collection and management strategies, and 3) simulate plausible management scenarios. We simulated the impacts of point source management interventions on pollution risk, including provision of sufficient municipal sewage treatment plant (STP) capacity, enhanced STP treatment levels and sufficient industrial wastewater effluent treatment capacity. We found a clear effect of enhanced STP interventions on improved regulatory standard compliance for nitrate (from 92% to 95%) and phosphate (from 33% to 41%). However, the effect of interventions on heavy metal pollution risk was not clear, due to considerable uncertainties related to the lack of reliable discharge data and the characterisation of industrial effluent quality. The parsimonious DST helped to collate the available understanding related to water quality impacts from multiple pollutants in the Ramganga river basin, while sensitivity analysis highlighted critical areas for further data collection.

Background: There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods: We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings: The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation: This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/. Funding Statement: EPSRC Impact Acceleration account fund EP/R511705/1; AS is funded by a Wellcome Trust fellowship 205188/Z/16/Z; University of Brighton COVID-19 Research Urgency Fund. Declaration of Interests: All researchers declare no competing interests. Ethics Approval Statement: De-identified and pseudo-anonymised patient data were obtained from data sets which were approved by the ethics committee as part of the existing Cardiac MRI Database NHS REC IRAS Ref: 222349 and University of Brighton REC (8011).

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Introduction: Despite significant therapeutic advancements, Atherosclerotic Cardiovascular Disease (ASCVD) patients require frequent hospitalization. Machine learning (ML) algorithms present an opportunity to develop improved and more generalizable prediction models for 30-day hospital readmission due to ASCVD. Objectives: The current study aims to leverage electronic health record (EHR) data to predict 30-day hospital readmission in high-risk ASCVD patients. Methods: This study utilized the MIMIC III database (a publicly available large, single-centre critical care database of 46,520 patients) comprising of deidentified ~60,000 ICU admissions. Adult patients with the first admission due to ASCVD event and whose length of stay (LOS) in the ICU was >48 hours were included. To develop a prediction model, features representing groups of diagnosis data along with demographics and length of stay in the hospital were used. The performance of ML models was evaluated using the area under the receiver operating characteristic curves (AUCs). Results: Our cohort consisted of 22,666 admissions (mean age 70.1 years) due to an ASCVD event. Out of the 9022 readmissions within 180 days, 60.5% were observed within 30 days of discharge. Among the 17 prospective predictors age, LOS and first hospitalization due to coronary atherosclerosis, heart failure, and haemorrhage issues were the most important factors to predict readmission within 30 days (Fig A). Random Forest was the best performing model with an AUC of 0.66 (Fig B). Conclusions: This model can be helpful in predicting the readmission of high-risk patients after the first ASCVD event which might address the huge unmet need of aiding healthcare resource planning, better patient care, and prevention of rehospitalization and death by intensifying treatment interventions in those patients and taking appropriate decisions to discharge patients based on their age, LOS and ASCVD events resulting in first hopitalization.

There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS-CoV-2 positive patients, respectively. External validation of the final model was conducted on 741 Accident and Emergency (A&E) admissions with suspected SARS-CoV-2 infection from a separate NHS Trust. The LUCAS mortality score included five strongest predictors (Lymphocyte count, Urea, C-reactive protein, Age, Sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the area under the receiving operating characteristics curve (AUC-ROC) in development cohort 0.765 (95% confidence interval (CI): 0.738–0.790), in internal validation cohort 0.744 (CI: 0.673–0.808), and in external validation cohort 0.752 (CI: 0.713–0.787). The discriminatory power of LUCAS increased slightly when including the CXR image data. LUCAS can be used to obtain valid predictions of mortality in patients within 60 days of SARS-CoV-2 RT-PCR results into low, moderate, high, or very high risk of fatality.

In this paper, we compare three functional regression models from a growth curve perspective to predict the relationship between two economic variables, specifically we compare a functional concurrent model, a functional historical model and a functional autoregressive model (FAR). The dependent and the independent variables are cumulated over the annual time window for the growth curve analyses. These models are used to predict exports (real) for the South African economy in terms of electricity demand. The data analysed consist of 33 years of exports (in ZAR million) at annual quarterly frequency, and electricity demand (in GwH) at monthly totals. Exploratory analysis included phase‐plane plots for the two series. For the prediction exercise, the baseline concurrent model was evaluated against the other two models, and their performance compared using the root‐mean‐square error (RSME) measure, the relative performance in terms of the ratio of the RMSEs, and a Kolmogorov–Smirnov based hypothesis test to compare the distributions of the RMSEs of the models. Our results show that from the growth curve perspective, for the prediction of exports in terms of electricity for the SA economy, the FAR model of lag(1) outperforms both the concurrent model and the historical model by far.

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and Resnet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively., Graphical abstract

Puerperium is the time following delivery during which pregnancy-induced maternal anatomical and physiological changes return to the nonpregnant state. Puerperium period of 6 weeks can be divided into: (a) immediate – within 24 hours (b) early – up to 7 days (c) remote – up to 6 weeks. The puerperal effects are seen in all organs and particularly in reproductive organs. Infection and haemorrhage are the common postpartum complications. Post partum care is very important. Advice on exclusive breast feeding and contraception is also mandatory after every childbirth.

Rivers are vital parts of the hydrosphere, providing ecosystem services and water for drinking and agriculture. However, rapid industrialisation and urbanisation globally leads to increasing pollution in many rivers. On their own, many in-river monitoring efforts in lower middle income countries do not yet provide enough information to adequately understand the general state or trends in freshwater ecosystems, presenting difficulties in efforts to mitigate water quality degradation. However, new sources of data such as satellites, drones and sondes provide better spatial and temporal coverage of the river network. This talk presents a statistical downscaling approach for the fusion of data from these different sources into a single product with improved accuracy and coverage compared to that of an individual source, through a Bayesian hierarchical modelling approach. The model development is motivated by the Ramganga river in northern India, a source of irrigation for crops and drinking water that supports millions of people, but suffers from heavy metal and nutrient pollution from population pressures, intensive agriculture and industries along its length, leading to water quality degradation and biodiversity loss. The work takes place as part of the Ramganga Water Data Fusion Project, funded by the UK Global Challenges Research Fund with the aim of informing work such as risk-based modelling and developing future monitoring design to improve mitigation efforts.

Background Accurate diagnostic tools to identify patients at risk of cancer therapy-related cardiac dysfunction (CTRCD) are critical. For patients undergoing cardiotoxic cancer therapy, ejection fraction assessment using radionuclide ventriculography (RNVG) is commonly used for serial assessment of left ventricular (LV) function. Methods In this retrospective study, approximate entropy (ApEn), synchrony, entropy, and standard deviation from the phase histogram (phase SD) were investigated as potential early markers of LV dysfunction to predict CTRCD. These phase parameters were calculated from the baseline RNVG phase image for 177 breast cancer patients before commencing cardiotoxic therapy. Results Of the 177 patients, 11 had a decline in left ventricular ejection fraction (LVEF) of over 10% to an LVEF below 50% after treatment had commenced. This patient group had a significantly higher ApEn at baseline to those who maintained a normal LVEF throughout treatment. Of the parameters investigated, ApEn was superior for predicting the risk of CTRCD. Combining ApEn with the baseline LVEF further improved the discrimination between the groups. Conclusions The results suggest that RNVG phase analysis using approximate entropy may aid in the detection of sub-clinical LV contraction abnormalities, not detectable by baseline LVEF measurement, predicting a subsequent decline in LVEF.

No abstract available.

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one overall sample size n). We also consider a modification of PBIC which is more favourable to complex models.

This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data. Additionally, in the sparse observation framework, we propose a novel approach of spatial principal analysis by conditional expectation to explicitly estimate spatial correlations and reconstruct individual curves. Assuming spatial stationarity, empirical spatial correlations are calculated as the ratio of eigenvalues of the smoothed covariance surface Cov $$(X_i(s),X_i(t))$$ and cross-covariance surface Cov $$(X_i(s), X_j(t))$$ at locations indexed by i and j. Then a anisotropy Matern spatial correlation model is fitted to empirical correlations. Finally, principal component scores are estimated to reconstruct the sparsely observed curves. This framework can naturally accommodate arbitrary covariance structures, but there is an enormous reduction in computation if one can assume the separability of temporal and spatial components. We demonstrate the consistency of our estimates and propose hypothesis tests to examine the separability as well as the isotropy effect of spatial correlation. Using simulation studies, we show that these methods have some clear advantages over existing methods of curve reconstruction and estimation of model parameters.

Highlights • Early SARS-CoV-2 prediction from monocytes-leukocytes-eosinophils variable: AUC 84%. • Prediction in regular ward using machine learning from complete blood count: AUC 94%. • Predominance of monocytes in SARS-CoV-2 positive patients similar to SARS and MERS., Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.

Heterotopic gestation is a very rare entity in natural conception, occurring in only 1 in 30,000 spontaneous pregnancies. A high index of suspicion is necessary for the timely diagnosis and appropriate intervention. We report the case of heterotopic pregnancy in a 32-year-old female presented with hemoperitoneum from ruptured tubal pregnancy with live intrauterine gestation at 6 weeks' period of gestation, diagnosed on ultrasound examination.

Peatlands are spatially heterogeneous ecosystems that develop due to a complex set of autogenic physical and biogeochemical processes and allogenic factors such as the climate and topography. They are significant stocks of global soil carbon, and therefore predicting the depth of peatlands is an important part of establishing an accurate assessment of their magnitude. Yet there have been few attempts to account for both internal and external processes when predicting the depth of peatlands. Using blanket peatlands in Great Britain as a case study, we compare a linear and geostatistical (spatial) model and several sets of covariates applicable for peatlands around the world that have developed over hilly or undulating terrain. We hypothesized that the spatial model would act as a proxy for the autogenic processes in peatlands that can mediate the accumulation of peat on plateaus or shallow slopes. Our findings show that the spatial model performs better than the linear model in all cases-root mean square errors (RMSE) are lower, and 95% prediction intervals are narrower. In support of our hypothesis, the spatial model also better predicts the deeper areas of peat, and we show that its predictive performance in areas of deep peat is dependent on depth observations being spatially autocorrelated. Where they are not, the spatial model performs only slightly better than the linear model. As a result, we recommend that practitioners carrying out depth surveys fully account for the variation of topographic features in prediction locations, and that sampling approach adopted enables observations to be spatially autocorrelated.

This article develops methods of statistical monitoring of clinical trials with multiple co-primary endpoints, where success is defined as meeting both endpoints simultaneously. In practice, a group sequential design (GSD) method is used to stop trials early for promising efficacy, and conditional power (CP) is used for futility stopping rules. In this article, we show that stopping boundaries for the GSD with multiple co-primary endpoints should be the same as those for studies with single endpoints. Lan and Wittes proposed the B-value tool to calculate the CP of single endpoint trials and we extend this tool to calculate the CP for studies with multiple co-primary endpoints. We consider the cases of two-arm studies with co-primary normal and provide an example of implementation with simulated trial. A fixed-weight sample size reestimation approach based on CP is introduced.

Selecting between competing structural equation models is a common problem. Often selection is based on the chi-square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with structural equation models compared to other fit indices. This article examines several new and old information criteria (IC) that approximate Bayes factors. We compare these IC measures to common fit indices in a simulation that includes the true and false models. In moderate to large samples, the IC measures outperform the fit indices. In a second simulation we only consider the IC measures and do not include the true model. In moderate to large samples the IC measures favor approximate models that only differ from the true model by having extra parameters. Overall, SPBIC, a new IC measure, performs well relative to the other IC measures.

Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficiently extract clusters in high dimensions with arbitrary density shapes. Further, clustering is performed over several resolutions and the results are summarized as a hierarchical tree, thus providing a model based multi resolution cluster analysis. Finally we implement a novel parallel implementation of HMAC which performs the clustering job over several processors thereby dramatically increasing the speed of clustering procedure especially for large data sets. This package also provides a number of functions for visualizing clusters in high dimensions, which can also be used with other clustering softwares.

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets.

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a variant of BIC. The authors provide a comparison of several BF approximations, including two new approximations, the Scaled Unit Information Prior Bayesian Information Criterion (SPBIC) and Information matrix-based Bayesian Information Criterion (IBIC). The SPBIC uses a scaled unit information prior that is more general than the BIC’s unit information prior, and the IBIC utilizes more terms of approximation than the BIC. Through simulation, the authors show that several measures perform well in large samples, that performance declines in smaller samples, and that SPBIC and IBIC provide improvement to existing measures under some conditions, including small sample sizes. The authors illustrate the use of the fit measures with the crime data of Ehrlich and then conclude with recommendations for researchers.

This paper illustrates the pitfalls of the conventional heteroskedasticity and autocorrelation robust (HAR) Wald test and the advantages of new HAR tests developed by Kiefer and Vogelsang in 2005 and by Phillips, Sun and Jin in 2003 and 2006. The illustrations use the 1993 Fama–French three-factor model. The null that the intercepts are zero is tested for 5-year, 10-year and longer sub-periods. The conventional HAR test with asymptotic P-values rejects the null for most 5-year and 10-year sub-periods. By contrast, the null is not rejected by the new HAR tests. This conflict is explained by showing that inferences based on the conventional HAR test are misleading for the sample sizes used in this application. Copyright © 2007 John Wiley & Sons, Ltd.

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC., Updated with reviewer suggestions

Summary. Pearson's X2- and the log-likelihood ratio X2-statistics are fundamental tools in goodness-of-fit testing. Cressie and Read constructed a general family of divergences which includes both statistics as special cases. This family is indexed by a single parameter, and divergences at either end of the scale are more powerful against alternatives of one type while being rather poor against the opposite type. Here we present several new goodness-of-fit testing procedures which have reasonably high powers for both kinds of alternative. Graphical studies illustrate the advantages of the new methods.

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online.

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation.\ud \ud Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods.\ud \ud Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics.

Pattern discovery in sequences is an important unsolved problem in biology, with many applications, including detecting regulation of genes by transcription factors, and differentiating proteins of infecting organisms such as viruses from an animal’s own genome. In this article we describe some of the recent statistical approaches developed to address these problems, and some possible future directions for progress in this field.

We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly-periodic and nearly-aperiodic with a predefined period. We show that the factor rotation can be obtained by calculation of canonical correlations between appropriate spaces which make the methodology computationally efficient. Moreover, we demonstrate that our proposed rotations provide stable and interpretable results in the presence of highly complex covariance. This work is motivated by the goal of finding interpretable sources of variability in gridded time series of vegetation index measurements obtained from remote sensing, and we demonstrate our methodology through an application of factor rotation of this data., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS518 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)