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Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point—the number of leading singular vectors to retain—based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer. [ABSTRACT FROM AUTHOR]

Local spatiotemporal nonstationarity occurs in various natural and socioeconomic processes. Many studies have attempted to introduce time as a new dimension into a geographically weighted regression (GWR) model, but the actual results are sometimes not satisfying or even worse than the original GWR model. The core issue here is a mechanism for weighting the effects of both temporal variation and spatial variation. In many geographical and temporal weighted regression (GTWR) models, the concept of time distance has been inappropriately treated as a time interval. Consequently, the combined effect of temporal and spatial variation is often inaccurate in the resulting spatiotemporal kernel function. This limitation restricts the configuration and performance of spatiotemporal weights in many existing GTWR models. To address this issue, we propose a new spatiotemporal weighted regression (STWR) model and the calibration method for it. A highlight of STWR is a new temporal kernel function, wherein the method for temporal weighting is based on the degree of impact from each observed point to a regression point. The degree of impact, in turn, is based on the rate of value variation of the nearby observed point during the time interval. The updated spatiotemporal kernel function is based on a weighted combination of the temporal kernel with a commonly used spatial kernel (Gaussian or bi-square) by specifying a linear function of spatial bandwidth versus time. Three simulated datasets of spatiotemporal processes were used to test the performance of GWR, GTWR, and STWR. Results show that STWR significantly improves the quality of fit and accuracy. Similar results were obtained by using real-world data for precipitation hydrogen isotopes (δ2 H) in the northeastern United States. The leave-one-out cross-validation (LOOCV) test demonstrates that, compared with GWR, the total prediction error of STWR is reduced by using recent observed points. Prediction surfaces of models in this case study show that STWR is more localized than GWR. Our research validates the ability of STWR to take full advantage of all the value variation of past observed points. We hope STWR can bring fresh ideas and new capabilities for analyzing and interpreting local spatiotemporal nonstationarity in many disciplines. [ABSTRACT FROM AUTHOR]

Between 2003 and 2015, there were 5343 outbreak tornadoes and 9389 isolated tornadoes reported in the continental United States. Here, the near-storm environmental parameter-space distributions of these two categories are compared via kernel density estimation, and the seasonal, diurnal, and geographical features of near-storm environments of these two sets of events are compared via self-organizing maps (SOMs). Outbreak tornadoes in a given geographical region tend to be characterized by greater 0–1-km storm-relative helicity and 0–6-km vector shear magnitude than isolated tornadoes in the same geographical region and also have considerably higher tornado warning-based probability of detection (POD) than isolated tornadoes. A SOM of isolated tornadoes highlights that isolated tornadoes with higher POD also tend to feature higher values of the significant tornado parameter (STP), regardless of the specific shape of the area of STP. For a SOM of outbreak tornadoes, when two outbreak environments with similarly high magnitudes but different patterns of STP are compared, the difference is primarily geographical, with one environment dominated by Great Plains and Midwest outbreaks and another dominated by outbreaks in the southeastern United States. Two specific tornado outbreaks are featured, and the events are placed into their climatological context with more nuance than typical single proximity sounding-based approaches would allow. [ABSTRACT FROM AUTHOR]

Aim The goal of this study was to evaluate the sufficiency of the network of protected lands in the U.S. northern Rocky Mountains in providing protection for habitat connectivity for 105 hypothetical organisms. A large proportion of the landscape falls into one of several categories of protected lands. However, protected lands in the region are primarily higher elevation forest and mountain habitats. Little is known about how the network of protected lands may maintain connectivity for a broad spectrum of species expressing different habitat requirements and dispersal abilities. Location The study was conducted across the states of Montana and northern Idaho, USA, comprising an area of 30.2 million hectares. Methods We used resistant kernel modelling to map the extent of the study area predicted to be connected by dispersal for each of 35 species groups with different ecological associations. We evaluated the effect of vagility on protected area sufficiency by varying dispersal ability across three levels for each species group. We evaluated the degree of vulnerability of each of the 105 hypothetical species (35 species groups × 3 dispersal abilities) in terms of the extent of the total study area predicted to be connected by dispersal. We defined nine categories of risk as the combination of species vulnerability because of the extent of connected habitat and the degree to which that habitat was protected. Results We found high variation in the vulnerability of species because of the extent of connected habitat, and the extent to which connected habitat overlapped protected lands. Species associated with high elevations and species associated with lower elevations were predicted to have limited extent of connected habitat. Species associated with high elevations were predicted to have the vast majority of their connected habitat protected by federal Forest Service and National Park Service lands. In contrast, species associated with lower elevations were poorly protected by the existing network of protected lands. Main conclusions Low elevation and non-forest habitats are at highest risk of human-induced habitat loss and fragmentation in the study area. Conservation efforts in the region may be most effective if they focus on expanding the network of lower elevation protected lands in such a way that maximizes connectivity across the landscape. [ABSTRACT FROM AUTHOR]

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression—techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies. [ABSTRACT FROM AUTHOR]

An increasing challenge in analysis of microarray data is how to interpret and gain biological insight of profiles of thousands of genes. This article provides a review of statistical methods for analysis of microarray data by incorporating prior biological knowledge using gene sets and biological pathways, which consist of groups of biologically similar genes. We first discuss issues of individual gene analysis. We compare several methods for analysis of gene sets including over-representation anlaysis, gene set enrichment analysis, principal component analysis, global test and kernel machine. We discuss the assumptions of these methods and their pros and cons. We illustrate these methods by application to a type II diabetes data set. [ABSTRACT FROM AUTHOR]

Abstract: Vehicle soak time, the duration of time a vehicle’s engine is at rest prior to being started, and its distribution function are important transportation activity data inputs for mobile emissions inventory estimation due to their impacts on vehicle start and evaporative emissions. This paper provides vehicle emission researchers with an overview of statistical analysis methods relevant to analyzing vehicle soak time data. Many of these methods are already in use in emissions research and have appeared in the literature. These methods are reviewed and further details regarding the implementation and interpretation of these methods are provided. Statistical methods relevant to the analysis of soak time data that have yet to appear in the emissions literature, including kernel density estimation and generalized linear models, are also introduced. Advantages and disadvantages of the methods are compared and theoretical justification is provided. Issues of correlated observations and censored data are discussed. General guidelines for the analysis of soak time data, such as stratification by start type and geographical region, are established. Finally, a subset of the statistical methods discussed is used to analyze the US Environmental Protection Agency’s 3-city data. [Copyright &y& Elsevier]

Abstract: In this paper, we assume that the log return of the underlying asset follows a semi-Markov process, then from the knowledge of the kernel we derive an explicit expression for the value of the option and for the bare risk in the case of the European call (put) option and, by means of a recursive system, we derive the value and the bare risk in the case of the American option. The prices and risks we obtained depend explicitly on the waiting-time distributions of the asset and they are duration dependent. The link with models based on Markov Chains and Continuous Time Random Walks is debated. [Copyright &y& Elsevier]

Quantitative indices of residential segregation have been with us for half a century, but suffer significant limitations. While useful for comparison among regions, summary indices fail to reveal spatial aspects of segregation. Such measures generally consider only the population mix within zones, not between them. Zone boundaries are treated as impenetrable barriers to interaction between population subgroups, so that measurement of segregation is constrained by the zoning system, which bears no necessary relation to interaction among population subgroups. A segregation measurement approach less constrained by the chosen zoning system, which enables visualization of segregation levels at the local scale and accounts for the spatial dimension of segregation, is required. We propose a kernel density estimation approach to model spatial aspects of segregation. This provides an explicitly geographical framework for modeling and visualizing local spatial segregation. The density estimation approach lends itself to development of an index of spatial segregation with the advantage of functional compatibility with the most widely used index of segregation (the dissimilarity index D). We provide a short review of the literature on measuring segregation, briefly describe the kernel density estimation method, and illustrate how the method can be used for measuring segregation. Examples using a simulated landscape and two empirical cases in Washington, DC and Philadelphia, PA are presented. [ABSTRACT FROM AUTHOR]

Examines the use of log-likelihood kernel approach in the analysis of cohort mortality data in the United States. Formulation of epidemiological techniques in classifying deaths; Determination of the timing and intensity of exposure to hazard; Use of poisson regression analysis in determining the death rate.