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ACTUARIAL APPLICATIONS OF WORD EMBEDDING models

ASTIN BULLETIN 2020年第1期50卷 1-24页

作者： Lee, Gee Y. Manski, Scott Maiti, Tapabrata Michigan State Univ Dept Stat & Probabil Dept Math C337 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA Michigan State Univ Dept Stat & Probabil C511 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA Michigan State Univ Dept Stat & Probabil C424 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA

In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

关键词： Risk mitigation insurance claims adjustment word embedding text mining word2vec GloVe word similarity generalized additive models actuarial modeling classification logistic regression loss modeling

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Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

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ECOLOGICAL INDICATORS 2021年 129卷

作者： Mondal, Rubina Bhat, Anuradha Indian Inst Sci Educ & Res Kolkata Dept Biol Sci Mohanpur 741246 W Bengal India

Over the past several decades, ecologists have been striving to develop models that accurately describe specieshabitat relationships across ecological communities. Statistical models that explain ecological dynamics need to consider the nuances of the complex interactions between communities and ecological factors. Here, we used multiple linear mixed models (LMM), generalized additive models (GAM), multivariate adaptive regression splines (MARS), and artificial neural networks (ANN) to model species richness and diversity of freshwater fishes in eastern and central India. The models were based on fish abundance and associated ecological data over three years across the study regions. We developed global models using all predictors after removing highly correlated variables (Pearson's r > 0.7). Results revealed conductivity, water temperature, and water velocity as the most important predictive factors of both species richness and diversity. We, then, built two subsets of selected factors to build predictive models for diversity and richness- one variable set containing common significant factors as revealed from the four different modeling methods used and the second, using an automatic feature selection technique. Amongst the modeling methods used in our study, ANN was found to create the best fit models for explaining nonlinearities between response variables and predictors. The importance of variable selection is highlighted, given that subset 1 (common consensual factors) creates more homogeneity in predictions compared to using subset 2 (automated feature selection). Contrary to similar studies in recent years, which show machine learning (ML) methods to typically outperform conventional methods, our results revealed that ANN performed at par with other methods in terms of predictive power. Our findings underline the need for a judicious choice of modeling techniques based on the availability of the data and the ecological communities being studied.

关键词： Freshwater fish Artificial neural network Linear mixed models Multivariate adaptive regression splines generalized additive models

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Guest Editors' Introduction to the Special Issue on "Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture"

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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020年第4期25卷 453-456页

作者： Piepho, Hans-Peter Tempelman, Robert J. Williams, Emlyn R. Univ Hohenheim Inst Crop Sci D-70593 Stuttgart Germany Michigan State Univ Dept Anim Sci E Lansing MI 48824 USA Australian Natl Univ Stat Consulting Unit Canberra ACT 2601 Australia

The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture covers a select set of topics currently of primary importance in the field. Efficient use of resources in agricultural research, as well as valid statistical inference, requires good designs, and this special issue boasts seven papers providing both review and cutting-edge methodology for the purpose. A broad range of methods for analysis of data arising in different branches agricultural research is covered in another five exciting papers. This special issue highlights the importance of and opportunities for applied statistics in agriculture.

关键词： Design of experiments generalized linear mixed models Structural equation models Michaelis-Menten kinetics generalized additive models Spatial modelling

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Responses of different facets of aquatic plant diversity along environmental gradients in Mediterranean streams: Results from rivers of Greece

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 296卷 113307页

作者： Stefanidis, Konstantinos Oikonomou, Anthi Papastergiadou, Eva Hellen Ctr Marine Res Inst Marine Biol Resources & Inland Waters 46-7 Km AthensSounio Ave Attiki 19013 Greece Univ Patras Univ Campus Rio Dept Biol GR-26500 Patras Greece

Aquatic and riparian plants play a crucial role in the functioning of riverine ecosystems. Hence, analyzing multiple facets of plant diversity could be extremely useful for assessing the ecological integrity of lotic ecosystems. The main objective of this study was to investigate the response of multiple facets of aquatic plant diversity, such as species richness, taxonomic distinctness and compositional dissimilarity, to environmental factors (i.e. nutrient pollution and hydromorphological alteration) in 72 stream reaches of mainland Greece. We employed generalized additive models to identify the variables with the highest influence and examine the response of species richness and taxonomic distinctness to environmental gradients. The relationship between compositional dissimilarity and the environment was examined with generalized Dissimilarity Modelling. Our results supported our hypothesis that human disturbances play a considerable role in shaping macrophyte assemblages. In particular, phosphates and hydromorphological modification were significant predictors of species richness, whereas taxonomic distinctness was unaffected by indicators of anthropogenic stress but it was influenced mostly by elevation, water temperature and pH. Concerning the compositional dissimilarity, geographic distance, elevation, temperature and total inorganic nitrogen were the most important environmental parameters. Our findings suggest that human stressors, such as hydromorphological modification and nutrient enrichment, affect the plant species richness at stream reach scale, but when considering community composition or taxonomic distinctness, environmental factors associated with the natural variability (e.g. elevation, temperature and geographic distance) are of higher importance. Overall, our results emphasize the advantage of examining multiple aspects of diversity when designing conservation schemes and management plans for riparian areas.

关键词： Riparian corridors Aquatic biodiversity Hydromorphological modification generalized additive models Aquatic macrophytes Mediterranean streams

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Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China

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ENVIRONMENTAL POLLUTION 2021年 289卷 117899页

作者： Yin, Hao Liu, Cheng Hu, Qihou Liu, Ting Wang, Shuntian Gao, Meng Xu, Shiqi Zhang, Chengxin Su, Wenjing Chinese Acad Sci HFIPS Anhui Inst Opt & Fine Mech Key Lab Environm Opt & Technol Hefei 230031 Peoples R China Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230026 Peoples R China Chinese Acad Sci Inst Urban Environm Ctr Excellence Reg Atmospher Environm Xiamen 361021 Peoples R China Univ Sci & Technol China Key Lab Precis Sci Instrumentat Anhui Higher Educ Hefei 230026 Peoples R China Univ Sci & Technol China Anhui Prov Key Lab Polar Environm & Global Change Hefei 230026 Peoples R China Hong Kong Baptist Univ Dept Geog Kowloon Tong Hong Kong Peoples R China Univ Sci & Technol China Sch Earth & Space Sci Hefei 230026 Peoples R China

To prevent the spread of the COVID 19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM2.5, PM10, NO2, SO2, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015 2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NCL, at least 43 %. However, O-3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O-3, O-3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NOx reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O-3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken.

关键词： generalized additive models Meteorological factors influence Atmospheric pollution Titration effect VOC reduction

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Hybrid numerical models for wind speed forecasting

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JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS 2021年 220卷

作者： Brabec, Marek Craciun, Alexandra Dumitrescu, Alexandru Czech Acad Sci Dept Stat Modeling Inst Comp Sci Pod Vodarenskou Vezi 2 Prague 18207 8 Czech Republic Univ Bucharest Fac Phys Bucharest 077125 Romania Meteo Romania Natl Meteorol Adm Dept Numer Modeling Bucharest 013686 Romania Meteo Romania Natl Meteorol Adm Dept Climatol Bucharest 013686 Romania

Wind speed is involved in multiple scales physical phenomena and depends on specific features, that are not always easy to simulate numerically. Alternative solution that combines the physical advantages provided by numerical weather prediction (NWP) simulations and statistical models is investigated for wind speed forecast. Several aspects that influence the wind speed forecast error at synoptic stations in Romania were identified, such as discrepancy between model and true topography, urbanicity or distance to the Black Sea. Calibration models in the framework of generalized additive models (GAM) are developed for the proposed endeavour. A set of models applied to limited area model ALARO were introduced and evaluated. Results showed improved statistical scores compared to raw ALARO output and simple regression model: a decrease of up to 23% for the RMSE score, or 94% for the bias was observed for the model which performed best in terms of annual bias and RMSE. Different impact of terms involved in the calibration model is found. Most important effects in the model are associated with wind speed observations from the 24 past hours and simulated wind speed effect in relation to altitude.

关键词： Wind speed generalized additive models Numerical weather prediction Hybrid modeling

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The Root-Soil Water Relationship Is Spatially Anisotropic in Shrub-Encroached Grassland in North China: Evidence from GPR Investigation

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REMOTE SENSING 2021年第6期13卷

作者： Cui, Xihong Zhang, Zheng Guo, Li Liu, Xinbo Quan, Zhenxian Cao, Xin Chen, Xuehong Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Beijing Normal Univ Inst Remote Sensing Sci & Engn Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Beijing 100875 Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

To analyze the root-soil water relationship at the stand level, we integrated ground-penetrating radar (GPR), which characterized the distribution of lateral coarse roots (>2 mm in diameter) of shrubs (Caragana microphylla Lam.), with soil core sampling, which mapped soil water content (SWC) distribution. GPR surveys and soil sampling were carried out in two plots (Plot 1 in 2017 and Plot 2 in 2018) with the same size (30 x 30 m(2)) in the sandy soil of the semi-arid shrubland in northern China. First, the survey area was divided into five depth intervals, i.e., 0-20, 20-40, 40-60, 60-80, and 80-100 cm. Each depth interval was then divided into three zones in the horizontal direction, including root-rich canopy-covered area, root-rich canopy-free area, and root-poor area, to indicate different surface distances to the canopy. The generalized additive models (GAMs) were used to analyze the correlation between root distribution density and SWC after the spatial autocorrelation of each variable was eliminated. Results showed that the root-soil water relationship varies between the vertical and horizontal directions. Vertically, more roots are distributed in soil with high SWC and fewer roots in soil with low SWC. Namely, root distribution density is positively correlated with SWC in the vertical direction. Horizontally, the root-soil water relationship is, however, more complex. In the canopy-free area of Plot 1, the root-soil water relationship was significant (p < 0.05) and negatively correlated in the middle two depth intervals (20-40 cm and 40-60 cm). In the same two depth intervals in the canopy-free area of Plot 2, the root-soil water relationship was also significant (p < 0.01) but non-monotonic correlated, that is, with the root distribution density increasing, the mean SWC decreased first and then increased. Moreover, we discussed possible mechanisms, e.g., root water uptake, 3D root distribution, preferential flow along roots, and different growing stages, wh

关键词： root ecology root distribution soil water content spatial pattern non-invasive generalized additive models

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Inference Procedures on the generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric models for Analysis of Covariance (ANCOVA) of Count Data

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Open Journal of Statistics 2021年第3期11卷 420-436页

作者： Maha Al-Eid Mohamed M. Shoukri Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric generalized additive models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric generalized additive models.

关键词： Count Regression Over Dispersion generalized Linear models Analysis of Covariance generalized additive models

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A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings

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APPLIED ENERGY 2021年 301卷

作者： Grillone, Benedetto Mor, Gerard Danov, Stoyan Cipriano, Jordi Sumper, Andreas Ctr Int Metodes Numer Engn Bldg Energy & Environm Grp CIMNE UPC Campus Terrassa Edifici GALAC Rambla S Barcelona 08222 Spain Ctr Int Metodes Numer Engn Bldg Energy & Environm Grp CIMNE Lleida Pere Cabrera 16 2 Off G Lleida Spain Univ Lleida Dept Environm & Soil Sci INSPIRES Rovira Roure 191 Lleida 25198 Spain Univ Politecn Cataluna Ctr Innovac Tecnol Convertidors Estat & Accioname Dept Engn Elect ETS Engn Ind Barcelona Av Diagonal 647Pl 2 Barcelona 08028 Spain

Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building's weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available.

关键词： Building energy retrofit Measurement and verification Data driven approach generalized additive models Building energy performance Energy savings estimation

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Modeling the relationship between emerging and persistent organic contaminants in water, sediment and oysters from a temperate lagoon

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MARINE POLLUTION BULLETIN 2021年 164卷 111994页

作者： Stoichev, Teodor Marques, Antonio Almeida, Cristina Marisa R. Univ Porto Interdisciplinary Ctr Marine & Environm Res CIIMA Av Gen Norton Matos S-N P-4450208 Matosinhos Portugal IP IPMA Portuguese Inst Sea & Atmosphere Div Aquaculture Seafood Upgrading & Bioprospect Rua Alfredo Magalhaes Ramalho 6 P-1495006 Lisbon Portugal

The concentrations of emerging and persistent organic contaminants (EPOC) in oysters (C-O) from Aveiro Lagoon are represented as a function of their concentrations in water (C-W) and sediment (CS) using linear and generalized additive models (LM, GAM). Additionally, four sampling seasons, octanol/water partition coefficients (K) and type of EPOC (pyrethroids, flame-retardants, musks, UV filters, polycyclic aromatic hydrocarbons, others) are included in the models. The probabilities of detection of EPOC in water, sediment and oysters are analyzed by GAM. The behavior of contaminants in water is determined by K with a clear seasonal trend. Sediments are reservoirs for hydrophobic compounds with less seasonal variation. Seasonal changes are found for C-O, the last being determined additively both by C-W and hydrophobicity from one side and by C-S and type of contaminants from the other side. The seasonal change of EPOC concentration in water, sediment and oysters is specific for each contaminant type.

关键词： Emerging organic contaminants Linear models generalized additive models Water Sediment Oysters

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