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Cognitive reserve estimated with a life experience questionnaire outperforms education in predicting performance on MoCA: Italian normative data

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CURRENT PSYCHOLOGY 2023年第23期42卷 19503-19517页

作者： Montemurro, Sonia Daini, Roberta Tagliabue, Chiara Guzzetti, Sabrina Gualco, Giulia Mondini, Sara Arcara, Giorgio IRCCS San Camillo Hosp Lido Venice Italy Univ Milano Bicocca Dept Psychol Milan Italy NeuroMI Milan Ctr Neurosci Milan Italy Univ Trento Ctr Mind Brain Sci CIMeC Rovereto Italy Humanitas San Pio X Neurol Serv Memory Clin Milan Italy RSA St Andrea Monza Italy Univ Padua Dept Philosophy Sociol Educ & Appl Psychol FISPPA Padua Italy Univ Padua Human Inspired Technol Res Ctr HIT Padua Italy

Normative data of neuropsychological tests typically consider the effect of demographic variables like age and education on performance. However, a broad literature has shown that, after the school age, other cognitively stimulating experiences (e.g., occupational attainment and a variety of leisure-time activities) may increase and build up cognitive reserve (CR), which is positively associated with better performance in many neuropsychological tests. With these premises, we investigated the predictive capability of education and a life-experience proxy of CR on a widely used cognitive screening, i.e., the Montreal Cognitive Assessment (MoCA). Results show that models including the more comprehensive life-experience CR proxy performed better than models including Education. Based on the results of our analyses we provide normative data and cut-offs on 440 Italian individuals aged 50-90 years, by taking into account, for the first time for the Italian population, a CR index, together with demographic variables and Education, in the calculation of regression-based norms. Accounting for life-experience CR proxies can improve the accuracy of normative data and allow a finer estimation of cognitive performance, which lead to a more tailored approach to patient assessment.

关键词： Normative data MoCA Cognitive reserve Education generalized additive models Neuropsychological assessment Cognitive screening

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Nonlinear hedge fund index clones?

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AUSTRALIAN JOURNAL OF MANAGEMENT 2023年第1期48卷 147-170页

作者： Walden, Mikhail Lajbcygier, Paul SuperEd Melbourne Vic Australia Monash Univ Monash Business Sch Clayton Rd Clayton Vic 3800 Australia

Cloning hedge fund indexes circumvents the many challenges associated with direct hedge fund investment. Theoretically, hedge fund indexes could have nonlinear exposures to the economic risk factors that drive their returns and may require nonlinear clones. By using flexible statistical models, we enable the choice between linear and nonlinear clones. We demonstrate that for certain hedge fund styles, nonlinear index clones are crucial for high fidelity replication. Nonlinear clones both facilitate economic insights to cloning and enhance the best linear clones. JEL classification: G10, G23, C15

关键词： generalized additive models hedge funds investments nonlinearities

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Interpretable Survival Analysis for Heart Failure Risk Prediction 3

Interpretable Survival Analysis for Heart Failure Risk Predi...

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3rd Machine Learning for Health Symposium

作者： Van Ness, Mike Bosschieter, Tomas Din, Natasha Ambrosy, Andrew Sandhu, Alexander Udell, Madeleine Stanford Univ Stanford CA 94305 USA VA Palo Alto Hlth Care Syst Palo Alto CA USA Kaiser Permanente Northern Calif Div Res Pleasanton CA USA

Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when these assumptions fail. Newer survival models based on machine learning avoid these assumptions and offer improved accuracy, yet sometimes at the expense of model interpretability, which is vital for clinical use. We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions. To evaluate our pipeline, we predict risk of heart failure using a largescale EHR database. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.

关键词： explainability healthcare heart failure survival analysis generalized additive models

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Smoothed Bernstein Online Aggregation for Short-Term Load Forecasting in IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2022年 9卷 202-212页

作者： Ziel, Florian Univ Duisburg Essen House Energy Markets & Finance D-45141 Essen Germany

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMS) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach.

关键词： Load forecasting forecasting competition prediction demand analytics forecast combination online learning aggregation method ensemble holiday effects COVID-19 shutdown lockdown post-COVID effects generalized additive models GAM lasso weather effects

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Expected education deficiencies from Covid-related school-lockdowns in spring 2020-empirical evidence on family-education resources using nonlinear regression

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ZEITSCHRIFT FUR ERZIEHUNGSWISSENSCHAFT 2023年第2期26卷 403-441页

作者： Lorenz, Jennifer Ike, Sina Dammann, Lea Maria Becker, Dominik Saefken, Benjamin Silbersdorff, Alexander Georg August Univ Gottingen Zentrum Stat Humboldtallee 3 D-37073 Gottingen Germany Georg August Univ Gottingen Stat Humboldtallee 3 D-37073 Gottingen Germany Georg August Univ Gottingen Lehrstuhl Maschinelles Lernen Goldschmidtstr 1 D-37077 Gottingen Germany Tech Univ Clausthal Prof Data Sci & Angew Stat Adolph Roemer Str 2a D-38678 Clausthal Zellerfeld Germany Georg August Univ Gottingen CIDAS Campus Inst Data Sci Goldschmidtstr 1 D-37077 Gottingen Germany

In light of the Covid-19-related school lockdowns in Germany in 2020 schools, families and the students were faced with the major challenge to continue instruction at home. This paper examines the expectations of the parents that their children will experience school-related problems as a result to the lockdown-induced homeschooling within the next six months. For our explorative analysis, we choose a nonlinear regression approach. In the course of this, we introduce nonlinear models and highlight their added value compared to methods commonly used in empirical educational research. For the analysis we combine data from the National Educational Panel Study (NEPS) with additional data sources like the COVID-19-Dashboard of the Robert-Koch-Institut (RKI). Our results show that parental expectations of future school problems were particularly prevalent among those parents whose children had low reading competencies and low diligence as an aspect of school effort. In addition, we find a relationship between a lower occupational status (ISEI) and higher parental expectations of school-related problems. Furthermore, parents' short-term and long-term concerns about Covid-19 show a positive association, making school problems more likely in the eyes of the parents. The purpose of this paper, in addition to applying and explaining nonlinear models for the first time in empirical educational research, is to analyze expectations regarding problems of homeschooling in the first lockdown from a parents' perspective and to explore variables that influence these parental expectations.

关键词： Covid pandemic Home Schooling Social inequality generalized additive models Semiparametric Regression

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Towards the comprehensive water quality control in Lake Taihu: Correlating chlorphyll a and water quality parameters with generalized additive model

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年第0期705卷 135993-000页

作者： Liu, Lili Dong, Yongcheng Kong, Ming Zhou, Jian Zhao, Hanbin Wang, Yupeng Zhang, Meng Wang, Zhiping East China Univ Sci & Technol Sch Resource & Environm Engn State Environm Protect Key Lab Environm Risk Asse Shanghai 200237 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai 200092 Peoples R China Minist Environm Protect Nanjing Inst Environm Sci Nanjing 210042 Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Nanjing 210008 Peoples R China Shanghai Municipal Engn Design Inst Grp Co Ltd Shanghai 200092 Peoples R China Shanghai Jiao Tong Univ Sch Environm Sci & Technol Shanghai 200240 Peoples R China

In this study, the generalized additive model (GAM) was used to analyze seasonal monitoring data from Lake Taihu, collected from 2010 to 2014, with the aim to explore the correlation between chlorophyll a (Chia) and other water quality parameters. The selected optimal multivariable GAM could effectively explain the concentration variation of Chla occurring during each season, and the interpretation degree followed the order: summer > autumn > spring > winter. The fitting results indicated that the concentration variation of Chia could reflect that of biochemical oxygen demand and chemical oxygen demand in all seasons. In addition, the total phosphorus showed strong ability to explain the concentration change of Chla in spring and summer, as the growth of algae would be affected when the concentration of phosphorus shifted high or low. Nitrogen showed strong ability to explain the variations in Chia concentration in autumn. The condusions of the optimal multivariable GAM could provide decision basis for the eutrophication control. In other words, the prevention of eutrophication outbreaks could be carried out via the targeted control of key water pollutants. According to these results, the concentration of Chia was higher in northern and western lake during summer and autumn, the management should focus on nutrient input of adjacent rivers. (C) 2019 Elsevier B.V. All rights reserved.

关键词： generalized additive models Chlorophyll a Water quality parameters Eutrophication control Lake Taihu

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Bayesian explanatory additive IRT models

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2022年第1期75卷 59-87页

作者： Mair, Patrick Gruber, Kathrin Harvard Univ Cambridge MA 02138 USA Erasmus Univ Rotterdam Netherlands

In this article we extend the framework of explanatory mixed IRT models to a more general class called explanatory additive IRT models. We do this by augmenting the linear predictors in terms of smooth functions. This development offers many new modeling options such as the inclusion of nonlinear covariate effects, the specification of various temporal and spatial dependency patterns, and parameter partitioning across covariates. We use integrated nested Laplace approximation (INLA) for accurate and computationally efficient estimation of the parameters. Uninformative, weakly informative, and informative prior settings for the hyperparameters are discussed. Running time experiments and Monte Carlo parameter recovery simulations are performed in order to study the accuracy and computational efficiency of INLA when applied to the proposed explanatory additive IRT model class. Using a real-life dataset, a variety of application scenarios is explored, and the results are compared with classical maximum likelihood estimation when possible. R code is included in the supplemental materials to allow readers to fully reproduce the examples computed in the paper.

关键词： Bayesian IRT explanatory additive IRT models generalized additive models integrated nested laplace approximation

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Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2022年第4期185卷 1903-1930页

作者： Gao, Chenyin Thompson, Katherine Jenny Kim, Jae Kwang Yang, Shu North Carolina State Univ Dept Stat Raleigh NC USA US Census Bur 4600 Silver Hill Rd Washington DC 20233 USA Iowa State Univ Dept Stat Ames IA USA

Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbour imputation method is used to handle such incomplete multinomial data. In this article, we investigate the nearest neighbour ratio imputation (NNRI) estimator, in which auxiliary variables are used to identify the closest donor and the vector of proportions from the donor is applied to the total of the recipient to implement ratio imputation. To estimate the asymptotic variance, we first treat the NNRI as a special case of predictive matching imputation and build on earlier work to linearize the imputed estimate. To account for the non-negligible sampling fractions, parametric and generalized additive models are employed to incorporate the smoothness of the imputation estimator, which results in a valid variance estimator. We apply the proposed method to estimate expenditures detail items based on empirical data from the 2018 collection of the Service Annual Survey, conducted by the United States Census Bureau. Our simulation results demonstrate the validity of our proposed estimators and also confirm that the derived variance estimators have good performance even when the sampling fraction is non-negligible.

关键词： generalized additive models hot deck imputation non-response

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Identifying engaging bird species and traits with community science observations

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第16期119卷 e2110156119-e2110156119页

作者： Stoudt, Sara Goldstein, Benjamin R. de Valpine, Perry Bucknell Univ Dept Math Lewisburg PA 17837 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA

Identifying rates at which birders engage with different species can inform the impact and efficacy of conservation outreach and the scientific use of community-collected biodiversity data. Species that are thought to be "charismatic" are often prioritized in conservation, and previous researchers have used sociological experiments and digital records to estimate charisma indirectly. In this study, we take advantage of community science efforts as another record of human engagement with animals that can reveal observer biases directly, which are in part driven by observer preference. We apply a multistage analysis to ask whether opportunistic birders contributing to iNaturalist engage more with larger, more colorful, and rarer birds relative to a baseline approximated from eBird contributors. We find that body mass, color contrast, and range size all predict overrepresentation in the opportunistic dataset. We also find evidence that, across 472 modeled species, 52 species are significantly overreported and 158 are significantly underreported, indicating a wide variety of species-specific effects. Understanding which birds are highly engaging can aid conservationists in creating impactful outreach materials and engaging new naturalists. The quantified differences between two prominent community science efforts may also be of use for researchers leveraging the data from one or both of them to answer scientific questions of interest.

关键词： animal charisma birds eBird iNaturalist generalized additive models

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Nutrient Improvements in Chesapeake Bay: Direct Effect of Load Reductions and Implications for Coastal Management

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022年第1期56卷 260-270页

作者： Murphy, Rebecca R. Keisman, Jennifer Harcum, Jon Karrh, Renee R. Lane, Mike Perry, Elgin S. Zhang, Qian US Geol Survey Catonsville MD 21228 USA Tetra Tech Inc Fairfax VA 22030 USA Maryland Dept Nat Resources Annapolis MD 21401 USA Old Dominion Univ Dept Biol Sci Norfolk VA 23529 USA Univ Maryland Ctr Environm Sci Annapolis MD 21401 USA

In Chesapeake Bay in the United States, decades of management efforts have resulted in modest reductions of nutrient loads from the watershed, but the corresponding improvements in estuarine water quality have not consistently followed. generalized additive models were used to directly link river flows and nutrient loads from the watershed to nutrient trends in the estuary on a station-by-station basis, which allowed for identification of exactly when and where responses are happening. Results show that Chesapeake Bay's total nitrogen and total phosphorus conditions are mostly improving after accounting for variation in freshwater flow. Almost all of these improving nutrient concentrations in the estuary can be explained by reductions in watershed loads entering through 16 rivers and 145 nearby point sources, with the nearby point source reductions being slightly more effective at explaining estuarine nutrient trends. Overall, these two major types of loads from multiple locations across the watershed are together necessary and responsible for the improving estuarine nutrient conditions, a finding that is highly relevant to managing valuable estuarine resources worldwide.

关键词： water quality estuary eutrophication generalized additive models trends

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