Vyacheslav Lyubchich

Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard Sampling

Environmental scientists face multiple challenges when analyzing unevenly recorded time series with small sample sizes. For example, trends in water temperature may be confounded with time and date of sampling when the latter represent convenience samples and thus introduce bias into regression estimates. We address these concerns using multilevel random slope models and nonparametric bootstrap inference for assessing the statistical significance of the annual trend in river temperature when measurement times and dates are haphazard.

Bootstrap quantification of estimation uncertainties in network degree distributions

We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the “blocking” argument, developed for bootstrapping of time series and re-tiling of spatial data, to random networks. We first sample a set of multiple ego networks of varying orders that form a patch, or a network block analogue, and then resample the data within patches. To select an optimal patch size, we develop a new computationally efficient and data-driven cross-validation algorithm. The proposed fast patchwork bootstrap (FPB) methodology further extends the ideas for a case of network mean degree, to inference on a degree distribution. In addition, the FPB is substantially less computationally expensive, requires less information on a graph, and is free from nuisance parameters. In our simulation study, we show that the new bootstrap method outperforms competing approaches by providing sharper and better-calibrated confidence intervals for functions of a network degree distribution than other available approaches, including the cases of networks in an ultra sparse regime. We illustrate the FPB in application to collaboration networks in statistics and computer science and to Wikipedia networks.

A distribution-free m -out-of- n bootstrap approach to testing symmetry about an unknown median

Testing for symmetry about an unknown median is a ubiquitous problem in mathematical statistics, particularly, for nonparametric rank-based methods, and in a broad range of applied studies, from economics and business to biology, ecology, and medicine. However, the challenge still remains on how to derive a symmetry test with a good power performance and at the same time delivering a reliable Type I Error estimate. To overcome this problem, a new data-driven m -out-of- n bootstrap method is introduced for testing symmetry about an unknown median. The asymptotic properties of the developed m -out-of- n bootstrap tests are investigated along with their empirical finite-sample performance. The new tests are illustrated by applications to legal studies and wildlife monitoring.

Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll a concentrations of 4.5 to 7.4 mg m-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities.

Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilistic Short-Term Forecasting of Wind Speed

Although clean and sustainable wind energy has long been recognized as one of the most attractive electric power sources, generation of wind power is still much easier than its integration into liberalized electricity markets. One of the key obstacles on the way of wider implementation of wind energy is its highly volatile and intermittent nature. This has boosted an interest in developing a fully probabilistic forecast of wind speed, aiming to assess a variety of related uncertainties. Nonetheless, most of the available methodology for constructing a future predictive density for wind speed are based on parametric distributional assumptions on the observed wind data, and such conditions are often too restrictive and infeasible in practice. In this paper we propose a new nonparametric data-driven approach to probabilistic wind speed forecasting, adaptively combining sieve bootstrap and regime switching models. Our new bootstrapped regime switching (BRS) model delivers highly competitive, sharp and calibrated ensembles of wind speed forecasts, governed by various states of wind direction, and imposes minimal requirements on the observed wind data. The proposed methodology is illustrated by developing probabilistic wind speed forecasts for a site in the Washington State, USA.

A local factor nonparametric test for trend synchronism in multiple time series

The problem of identifying joint trend dynamics in multiple time series, i.e., testing whether two or more observed processes follow the same common trend, is essential in a wide spectrum of applications, from economics and finance to climate and environmental studies. However, most of the available tests for comparing multiple mean functions either deal with independent errors or are applicable only to a case of two time series, which constitutes a substantial limitation in many modern, typically high-dimensional, studies. In this paper we propose a new nonparametric test for synchronism of trends exhibited by multiple linear time series where the number of time series N can be large but fixed. The core idea of our new approach is based on employing the local regression test statistic, which allows to detect possibly non-monotonic nonlinear trends. The finite sample performance of the new synchronism test statistic is enhanced by a nonparametric hybrid bootstrap approach. The proposed methodology is illustrated by simulations and a case study on insurance claims due to extreme weather.

Evaluating the Impact of Climate Change on Dynamics of House Insurance Claims

The adverse effects of climate change bring increasingly more alterations to all aspects of human life and welfare, and one of the sectors that is particularly affected by changing climate is the insurance sector. Indeed, the year 2013 brought a record number of claims and substantial losses due to weather-related damages, and in the USA and Canada alone, the extreme weather events cost the insurance industry more than 3 billion dollars. The objective of this paper is to provide statistical data-driven insight on the (non)linear relationship between weather-related house insurance claims and atmospheric variables and to predict future claim dynamics accounting for changes in extreme precipitation. In this paper we propose to employ a flexible Generalized Autoregressive Moving Average (GARMA) model for count time series of claims, develop a new method to compare tails of the observed and projected extreme precipitation, and evaluate the impact of climate change on a number of house insurance claims in the GARMA framework. We illustrate our approach by studying insurance dynamics in four Canadian cities.

Time series models of decadal trends in the harmful algal species Karlodinium veneficum in Chesapeake Bay

The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance.

Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking

Modern customer analytics offers retailers a variety of unprecedented opportunities to enhance customer intelligence solutions by tracking individual clients and their peers and studying clientele behavioral patterns. While telecommunication providers have been actively utilizing peer network data to improve their customer analytics for a number of years, there yet exists a very limited knowledge on the peer effects in retail banking. We introduce modern deep learning concepts to quantify the impact of social network variables on bank customer attrition. Furthermore, we propose a novel deep ensemble classifier that systematically integrates predictive capabilities of individual classifiers in a meta-level model, by efficiently stacking multiple predictions using convolutional neural networks. We evaluate our methodology in application to customer retention in a retail financial institution in Canada.