جست‌وجوی استاد و پژوهشگر

Nan Zouمشاهده پروفایل

مدرس

• Time series analysis
• Extreme value theory
• Resampling-type methods
• +۳ مورد دیگر

Nan Zou is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a Ph.D. in Mathematics with a Specialization in Statistics from the University of California San Diego (2017) and a B.B.A. in Management Science from Renmin University of China (2011). Her research focuses on statistical methodology and theory in time series analysis, extreme value theory, resampling techniques, and dynamical systems. She has collaborated internationally and received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her recent work addresses bias correction in extreme value analysis, bootstrap methods for unit root testing, and multivariate time series extremes. Key contributions include reweighted madogram estimators and seasonal unit root tests under periodic variation. Current research emphasizes statistical inference for massive datasets and dynamical systems. Nan Zou actively supervises PhD and MRes students at Macquarie University, encouraging applications from eligible candidates. She has published in journals like the Annals of Statistics and Journal of Nonparametric Statistics , with a focus on methodological advancements in statistical theory.

Jake Soloffمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Empirical Bayes
• Nonparametric Estimation
• +۶ مورد دیگر

Jake Soloff is an Assistant Professor of Statistics at the University of Michigan. He earned his Ph.D. in Statistics from UC Berkeley in 2022 under advisors Adityanand Guntuboyina and Michael Jordan, followed by a postdoctoral fellowship at the University of Chicago with Rina Foygel Barber and Rebecca Willett. Research Focus: Statistical machine learning, algorithmic stability, empirical Bayes methods, and theoretical analysis of calibration and false discovery rate control. Key Contributions: Developed the Inflated Argmax for stable classification, proposed Cutoff Calibration Error metrics, analyzed distribution-free properties of isotonic regression, and created resampling techniques for stability guarantees. Publication Trends : Recent work emphasizes stability in machine learning pipelines, empirical Bayes optimization for high-dimensional problems, and novel approaches to hypothesis testing with stochastic monotonicity constraints. Software Development : Maintains the NPEB Python package for nonparametric empirical Bayes estimation.

• Causal Machine Learning
• Labour Economics
• Health Economics
• +۳ مورد دیگر

Michael Lechner is a Professor of Economics at the University of St. Gallen (HSG) with a focus on Applied Microeconometrics. His work integrates causal machine learning with labor, health, and sports economics. He holds affiliations with institutions such as the Centre for Economic Policy Research (CEPR) and the Institute for the Study of Labor (IZA). Lechner earned his academic credentials from the University of Mannheim (1990-1998). His research explores topics like policy evaluation, treatment heterogeneity, and the socioeconomic impacts of sports and health interventions. He has published extensively on causal machine learning frameworks and their applications in public policy. His editorial roles include co-editing Empirical Economics (2014-2017) and serving on editorial boards of journals like German Economic Review and Econometric Reviews . He is a member of the German National Academy of Sciences Leopoldina, recognizing his contributions to economic research. Lechner’s recent work emphasizes causal machine learning to analyze labor market policies, sports participation effects, and pandemic-driven market shifts. His research bridges econometric rigor with modern computational techniques to address real-world policy challenges.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

James G. MacKinnonمشاهده پروفایل

استاد

• Bootstrap Methods
• Cluster-Robust Inference
• Econometric Theory
• +۳ مورد دیگر

James G. MacKinnon is the Sir Edward Peacock Professor of Econometrics at Queen’s University, Department of Economics. He holds a B.A. (York University), M.A., and Ph.D. (Princeton University). His research focuses on bootstrap methods, cluster-robust inference, and econometric theory. He has authored influential textbooks like *Estimation and Inference in Econometrics* and *Econometric Theory and Methods*. MacKinnon has served as Head of the Department of Economics (2003–2013) and President of the Canadian Economics Association (2001–2002). His honors include Fellowships from the Econometric Society, Royal Society of Canada, and International Association for Applied Econometrics. His work spans over 150 publications, emphasizing robust statistical methods and empirical practice. Notable contributions include numerical distribution functions for cointegration tests and cluster-robust inference guidelines. He has advised numerous graduate students and collaborates on software tools for econometric analysis, such as Stata modules for bootstrap inference.

Tomás Del Barrio Castroمشاهده پروفایل

استاد

• Time Series Analysis
• Seasonality
• Applied Econometrics
• +۲ مورد دیگر

Dr. Tomás Del Barrio Castro is a Full Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB) , where he has been affiliated since 2007. He holds a PhD in Economics (University of Barcelona, 1998) and a Degree in Economics (Autonomous University of Madrid, 1992). PhD in Economics, University of Barcelona (1998) Degree in Economic and Business Sciences, Autonomous University of Madrid (1992) His research focuses on Time Series Analysis , Seasonality , and Applied Econometrics , with methodological contributions to seasonal unit root testing and periodic integration. He has published in top journals including Econometric Theory , The Econometric Journal , and Journal of Time Series Analysis . A significant portion of his work examines temporal aggregation effects, persistent cycles, and mixed-frequency data challenges. Scientific Recognition : Econometric Theory Multa Scripsit Award (2014) As Principal Investigator since 2007, he leads competitive research projects in econometrics. His teaching spans undergraduate and graduate courses in Statistics and Econometrics, including Macroeconometrics and Time Series Analysis at UIB and University of Barcelona. Recent appointments include: 2025-26: Macroeconometrics (Double Degree in Economics & Tourism) 2025-26: Time Series Analysis (Master's in Big Data Analysis)

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

Zeinab Mashreghiمشاهده پروفایل

دانشیار

• Sampling Theory
• Nonresponse Handling
• Bootstrap Methods
• +۳ مورد دیگر

Zeinab Mashreghi is an Associate Professor in the Department of Mathematics and Statistics at the University of Winnipeg, holding this position since July 2020. Previously, she served as an Assistant Professor at the same institution from January 2016 to June 2020. She is also an Adjunct Professor in the Department of Community Health Sciences at the University of Manitoba since October 2021. Her educational background includes a Ph.D. in Statistics from Université de Montréal, an M.Sc. in Pure Mathematics from Université Laval, and a B.Sc. in Applied Mathematics from the University of Kashan, Iran. Dr. Mashreghi’s research focuses on sampling methodology , particularly addressing nonresponse , resampling methods , imputation , and variance estimation . She also develops R packages and contributes to infectious disease modelling . Currently, she holds an NSERC Grant to support student researchers in these areas. Her work bridges statistical theory and practical applications, such as analyzing gonorrhea clusters in Manitoba using spatio-temporal methods. Teaching : She instructs courses including Mathematical Statistics II, Survey Sampling I & II, and Applied Regression Analysis. Her publications** span bootstrap techniques for imputed survey data, optimization algorithms, and disease surveillance. No scientific awards explicitly mentioned .

Bradley Efronمشاهده پروفایل

استاد

• Statistical Methodology
• Bootstrap Methods
• Computational Statistics
• +۳ مورد دیگر

Bradley Efron is Max H. Stein Professor and Professor of Statistics and of Health Research and Policy at Stanford University. A pioneering statistician, he developed the bootstrap method that revolutionized computational statistics, substituting computer power for mathematical formulas to extend statistical methodology for complex problems. Research contributions: Bootstrap resampling techniques Large-scale inference methods Empirical Bayes approaches Clinical trial statistical design Bayesian-frequentist synthesis Honors include membership in National Academy of Sciences, presidency of American Statistical Association, MacArthur Prize Fellowship, and Wilks Medal. Actively consults on statistical applications for healthcare evaluations, clinical studies, and scientific research methodology.

Lasanthi Watagodaمشاهده پروفایل

استادیار

• Statistical Learning
• Linear Regression
• Variable Selection
• +۳ مورد دیگر

Dr. Lasanthi Watagoda is an Assistant Professor in the Department of Mathematical Sciences at Appalachian State University. She joined the university in 2017 as a Visiting Assistant Professor and transitioned to her current role. Her research focuses on statistical learning, particularly in linear regression, variable selection, prediction intervals, and bootstrap methods, with additional interests in actuarial science. She holds a Ph.D. in Mathematics from Southern Illinois University Carbondale. Dr. Watagoda teaches a variety of courses, including Statistical Data Analysis, Linear Regression Models, and Probability. Education: Ph.D. in Mathematics, Southern Illinois University Carbondale Research Interests: Dr. Watagoda’s work emphasizes developing methods for inference after variable selection, with applications in statistical modeling and prediction. Her research integrates bootstrap techniques to enhance the accuracy of prediction intervals and explores shrinkage estimators for improved model performance. She also applies machine learning to wearable sensor data for sprint assessment and examines theoretical aspects of regression models, such as consistency and asymptotic properties. Teaching: She has taught numerous courses, including STT 3851 (Statistical Data Analysis II), STT 4830 (Linear Regression Models), and introductory statistics courses. Her teaching emphasizes reproducible research workflows and the use of tools like R and RStudio. Publications: Her recent work includes studies on distribution-free bootstrap prediction intervals, shrinkage estimators, and the theoretical underpinnings of variable selection in regression. These contributions highlight her focus on advancing statistical methodology and its practical applications. Labs/Teams: No specific lab or team affiliations are mentioned in the provided texts.

Adam Loyمشاهده پروفایل

دانشیار

• Statistical Education
• Data Visualization
• Computational Statistics
• +۳ مورد دیگر

Adam Loy is an Associate Professor of Statistics in the Department of Mathematics and Statistics at Carleton College. He teaches statistics courses at all levels and focuses on integrating modern computational methods and visualization into education. His research emphasizes visual inference, statistical software development (e.g., R packages like lmeresampler and qqplotr), and improving statistical education through innovative pedagogical tools. Education: B.A. in Mathematics/Statistics from Luther College (2007) M.S. in Statistics from Iowa State University (2009) Ph.D. in Statistics from Iowa State University (2013) Research Interests: Professor Loy’s work combines statistical methodology with computational and educational applications. He develops R packages for statistical diagnostics and visualization, explores non-traditional grading systems in statistics education, and applies statistical models to complex social and environmental datasets. Recent Teaching: Fall 2024: STAT 270 (Statistical Learning), STAT 294 (Directed Research) Winter 2025: MATH 240 (Probability), STAT 250, STAT 294 Spring 2025: STAT 294 Future courses include STAT 220 (Data Science) and STAT 230 (Applied Regression Analysis). Publications: Recent work focuses on classroom innovations (non-traditional grading), computational methods (bootstrap approaches for clustered data), and diagnostic tools for mixed-effects models. His articles highlight interdisciplinary applications in urban studies and environmental science. Labs/Teams: Active in R package development communities, contributing to open-source tools for statistical education and analysis.

Jürgen Frankeمشاهده پروفایل

استاد

• Nonlinear Time Series Analysis
• Stochastic Processes
• Statistical Methodology
• +۵ مورد دیگر

Prof. Dr. Jürgen Franke is a renowned academic in statistics and applied mathematics. He held the position of Professor C4 (Chair) for Applied Mathematical Statistics at Technische Universität Kaiserslautern from 1988 to 2017. Since 2017, he has been a Consultant Researcher at the Fraunhofer Institute for Industrial Mathematics (ITWM) in Kaiserslautern. His research focuses on nonlinear time series analysis, stochastic processes, and their applications in finance, risk management, and biomedical data analysis. Education: - Diplom in Mathematics, Goethe-Universität Frankfurt a.M., 1974. - PhD (Dr. phil.nat.) in Mathematics, Goethe-Universität Frankfurt a.M., 1980. - Habilitation in Mathematics, Goethe-Universität Frankfurt a.M., 1985. Research Interests: Franke's research emphasizes the development of statistical methodologies for nonlinear time series, spatial data, and stochastic processes. Key areas include local smoothing techniques (kernel and wavelet-based estimates), functional data analysis, resampling methods (bootstrap), and nonparametric approaches in machine learning. His applied work addresses challenges in finance, risk management, and biomedical data analysis where traditional methods may be insufficient. Advising and Grants: While specific grants are not detailed, Franke has collaborated extensively with researchers in applied mathematics and statistics, contributing to numerous projects. His work often intersects with interdisciplinary applications, reflecting a commitment to bridging theoretical and practical domains. Labs/Teams: Affiliated with the Department of Mathematics at TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), where he focuses on applied statistical research and collaboration with industry.

Holger Dreesمشاهده پروفایل

استاد

• Actuarial Mathematics
• Financial Time Series Modeling
• Extreme Value Statistics
• +۳ مورد دیگر

Prof. Dr. Holger Drees is a Professor of Actuarial Mathematics at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a position in the Department of Mathematics, specializing in the ST – Mathematical Statistics and Stochastic Processes research group. His office is located at Bundesstraße 55, Room T15 in Hamburg. He earned his diploma in mathematics from the University of Dortmund (1990), his PhD from the University of Siegen (1993), and his habilitation from the University of Cologne (1998). His research focuses on extreme value theory, actuarial mathematics, financial time series modeling, and non/semiparametric statistics. He is a member of the Hamburger Zentrum für Versicherungswissenschaft (HZV) and serves as an Associate Editor for *Bernoulli* and *Extremes* journals. His recent research emphasizes statistical inference on extreme value dependence structures, cluster-based methods for time series extremes, and dimension reduction techniques for multivariate extremes. His work bridges theoretical advancements in extreme value analysis with practical applications in finance and insurance. Teaching activities include advanced courses on extreme value theory and actuarial mathematics. Professional contributions include editorial roles and collaborative projects on statistical methodologies for extremes. His research has been supported by grants such as the DFG Heisenberg grant (2000–2002). He maintains an active international research network, collaborating with institutions like the University of Cologne and the University of Heidelberg.

Guangjian Zhangمشاهده پروفایل

دانشیار

• Psychometrics
• Factor Analysis
• Structural Equation Modeling
• +۳ مورد دیگر

Guangjian Zhang is an Associate Professor at the University of Notre Dame, affiliated with the Psychometrics and Dynamic Analysis (PDA) Lab. His primary research focuses on developing and improving quantitative methods in the behavioral and social sciences, particularly in factor analysis, structural equation modeling, time series analysis, and longitudinal data analysis. He holds a Ph.D. in Psychology from Ohio State University (2006) and an M.S. in Statistics from the same institution (2004), along with an M.Ed. in Psychology from Beijing University (1999) and a B. Med. from Tianjin Medical University (1994). Education: Ph.D. in Psychology, Ohio State University, 2006 M.S. in Statistics, Ohio State University, 2004 M.Ed. in Psychology, Beijing University, 1999 B. Med., Tianjin Medical University, 1994 His research interests emphasize enhancing measurement tools like questionnaires, with a focus on methodological advancements in psychometrics. His work spans dynamic factor analysis, multivariate time series, and computational efficiency in statistical methods. Notable contributions include the development of the EFAutilities package and bootstrap procedures for factor analysis validation. He teaches courses on statistics and advanced methodology in behavioral and social sciences. Dr. Zhang has published extensively on topics such as factor rotation, bootstrap standard errors, and longitudinal modeling. His research addresses challenges in psychological dynamics, clinical outcomes, and statistical methodologies, reflecting a commitment to bridging theoretical and applied quantitative psychology.

Adriana Cornea-Madeiraمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Inflation Dynamics
• +۳ مورد دیگر

Adriana Cornea-Madeira is a Professor at the University of Lisbon's School of Economics and Management, affiliated with the Department of Economics. She holds a Doctorate in Economics from Aix-Marseille University (2008) and advanced degrees from Babes-Bolyai University. Her research focuses on econometric methodology, inflation dynamics, and applications of statistical techniques to public health and financial markets. She teaches core courses in Statistics and Econometrics for undergraduate programs in Finance, Economics, and Management, as well as advanced courses in Time Series and Statistical Methods at the master’s level. Her supervision record includes master’s theses on machine learning applications in credit scoring, inflation forecasting, and climate change impact analysis. Her work bridges theoretical econometrics and real-world policy issues, with recent studies analyzing pandemic response effectiveness and monetary policy impacts. She contributes to research initiatives at ISEG’s Data Lab, Futures Lab, and Policy Lab, addressing topics like large data set analysis and behavioral economic modeling.