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Year of publication
Subject
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Gaussian process 219 Gauß-Prozess 109 Stochastischer Prozess 67 Stochastic process 65 Theorie 60 Theory 59 Estimation theory 47 Schätztheorie 47 Bayesian inference 33 Bayes-Statistik 32 Regression analysis 20 Regressionsanalyse 20 Forecasting model 17 Prognoseverfahren 17 Simulation 17 Bootstrap approach 16 Bootstrap-Verfahren 16 Kriging 16 Nichtparametrisches Verfahren 15 Nonparametric statistics 15 Statistische Verteilung 15 Time series analysis 15 Zeitreihenanalyse 15 Gaussian process regression 14 Maximum likelihood estimation 14 Modellierung 14 Scientific modelling 14 Statistical distribution 14 Gaussian Process 13 Maximum-Likelihood-Schätzung 13 Artificial intelligence 12 Künstliche Intelligenz 12 Zinsstruktur 12 Yield curve 11 Mathematical programming 10 Mathematische Optimierung 10 Distribution function 8 Spatial statistics 8 Statistischer Test 8 VAR model 8
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Online availability
All
Free 121 Undetermined 94 CC license 10
Type of publication
All
Article 125 Book / Working Paper 115
Type of publication (narrower categories)
All
Working Paper 73 Article in journal 67 Aufsatz in Zeitschrift 67 Graue Literatur 67 Non-commercial literature 67 Arbeitspapier 65 Aufsatz im Buch 7 Book section 7 Hochschulschrift 5 Article 4 Thesis 4 Collection of articles written by one author 1 Sammlung 1 research-article 1
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Language
All
English 183 Undetermined 55 French 1 Russian 1
Author
All
Kleijnen, Jack P. C. 14 Chernozhukov, Victor 12 Linton, Oliver 10 Whang, Yoon-Jae 7 Belloni, Alexandre 6 Fiorentini, Gabriele 6 Mehdad, Ehsan 6 Sentana, Enrique 6 Chetverikov, Denis 5 Cho, Jin Seo 5 Kato, Kengo 5 Fernández-Val, Iván 4 Hébert, Benjamin 4 Kleijnen, Jack P.C. 4 Lanne, Markku 4 Mehdad, E. 4 Woodford, Michael 4 Yen, Yu-Min 4 Beers, Wim C. M. van 3 Dearmon, Jacob 3 Giudici, Paolo 3 Guvenen, Fatih 3 Hall, Peter 3 Han, Heejoon 3 Härdle, Wolfgang 3 Kleinow, Torsten 3 Kristensen, Dennis 3 Lee, Sokbae 3 Liesenfeld, Roman 3 Madera, Rocio 3 Ozkan, Serdar 3 Phillips, Peter C. B. 3 Schmidt, Peter 3 Smith, Tony E. 3 Subramanian, Sundarraman 3 Tjostheim, Dag 3 Vogler, Jan 3 Whang, Yoon-jae 3 Xu, Xiaojie 3 Yen, Yu-min 3
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Institution
All
Tilburg University, Center for Economic Research 4 Cowles Foundation for Research in Economics, Yale University 3 Centre for Microdata Methods and Practice (CEMMAP) 2 Christian-Albrechts-Universität zu Kiel 2 International Monetary Fund (IMF) 2 National Bureau of Economic Research 2 Berkeley Electronic Press 1 Department of Economics and Business, Universitat Pompeu Fabra 1 Départment des sciences administratives, Université du Québec en Outaouais (UQO) 1 Institute of Economic Research, Korea University 1 London School of Economics (LSE) 1 School of Economics and Management, University of Aarhus 1 School of Economics, University of Adelaide 1 Solvay Brussels School of Economics and Management, Université Libre de Bruxelles 1 Sonderforschungsbereich 373, Quantifikation und Simulation ökonomischer Prozesse, Wirtschaftswissenschaftliche Fakultät 1 Suntory and Toyota International Centres for Economics and Related Disciplines, LSE 1 Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München 1
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Published in...
All
CEMMAP working papers / Centre for Microdata Methods and Practice 13 Discussion paper / Center for Economic Research, Tilburg University 12 Journal of business & economic statistics : JBES ; a publication of the American Statistical Association 6 Journal of econometrics 5 Stochastic Processes and their Applications 5 Annals of the Institute of Statistical Mathematics 4 Discussion Paper / Tilburg University, Center for Economic Research 4 Journal of Multivariate Analysis 4 Statistical Inference for Stochastic Processes 4 Statistics & Probability Letters 4 cemmap working paper 4 CEMFI working paper 3 CREATES research paper 3 Computational Statistics & Data Analysis 3 Cowles Foundation Discussion Papers 3 European journal of operational research : EJOR 3 International journal of forecasting 3 Operations research 3 Statistical Papers / Springer 3 Working paper / National Bureau of Economic Research, Inc. 3 Bayesian model comparison 2 CeMMAP working papers 2 Cowles Foundation discussion paper 2 Discussion paper / Centre for Economic Policy Research 2 IMF Working Papers 2 INFORMS journal on computing : JOC ; charting new directions in operations research and computer science ; a journal of the Institute for Operations Research and the Management Sciences 2 IRTG 1792 Discussion Paper 2 Insurance / Mathematics & economics 2 International Journal of Energy Economics and Policy : IJEEP 2 International journal of production research 2 Journal of Global Optimization 2 NBER working paper series 2 Physica A: Statistical Mechanics and its Applications 2 Risks : open access journal 2 Spatial econometrics: qualitative and limited dependent variables 2 TEST: An Official Journal of the Spanish Society of Statistics and Operations Research 2 Tinbergen Institute research series 2 Working paper / Indian Institute of Management, Ahmedabad 2 AStA Advances in Statistical Analysis 1 American economic review 1
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Source
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ECONIS (ZBW) 160 RePEc 64 EconStor 12 BASE 2 USB Cologne (EcoSocSci) 1 Other ZBW resources 1
Showing 1 - 50 of 240
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Predicting wholesale edible oil prices through Gaussian process regressions tuned with Bayesian optimization and cross-validation
Jin, Bingzi; Xu, Xiaojie - In: Asian journal of economics and banking : AJEB 9 (2025) 1, pp. 64-82
Purpose - Developing price forecasts for various agricultural commodities has long been a significant undertaking for a variety of agricultural market players. The weekly wholesale price of edible oil in the Chinese market over a ten-year period, from January 1, 2010 to January 3, 2020, is the...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015339298
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GMM estimation with Brownian kernels applied to income inequality measurement
Cho, Jin Seo; Phillips, Peter C. B. - 2024
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015077115
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Consumption dynamics and welfare under non-Gaussian earnings risk
Guvenen, Fatih; Ozkan, Serdar; Madera, Rocio - 2024
Recent empirical studies document that the distribution of earnings changes displays substantial deviations from lognormality: in particular, earnings changes are negatively skewed with extremely high kurtosis (long and thick tails), and these non-Gaussian features vary substantially both over...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014543845
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Consumption dynamics and welfare under non-Gaussian earnings risk
Guvenen, Fatih; Madera, Rocio; Ozkan, Serdar - 2024
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014521342
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Posterior manifolds over prior parameter regions : beyond pointwise sensitivity assessments for posterior statistics from MCMC inference
Jacobi, Liana; Kwok, Chun Fung; Ramírez Hassan, Andrés; … - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 28 (2024) 2, pp. 403-434
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014631952
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Efficient global optimization and the zero-gradient condition, in expensive simulation
Angün, Mevlüde Ebru; Kleijnen, Jack P. C. - 2024
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Optimization of system dynamics models : a novel methodology
Angün, Mevlüde Ebru; Kleijnen, Jack P. C.; Smits, Martin - 2023
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014439377
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Modeling the interest rates term structure using Machine Learning : a Gaussian process regression approach
Delucchi, Alessio; Giribone, Pier Giuseppe - In: Risk management magazine 18 (2023) 3, pp. 16-35
The correct modeling of the interest rates term structure should definitely be considered an aspect of primary importance since the forward rates and the discount factors used in any financial and risk analysis are calculated from such structure. The turbulence of the markets in recent years,...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014491969
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A Gaussian process regression machine learning model for forecasting retail property prices with Bayesian optimizations and cross-validation
Xu, Xiaojie; Zhang, Yun - In: Decision analytics journal 8 (2023), pp. 1-12
The real estate market in China has been growing rapidly during the past decade, with different property price patterns across various regions. Among different types of properties, prices of retail properties have not been sufficiently analyzed. This study focuses on forecasting problems of...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014516551
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Understanding models and model bias with Gaussian processes
Cook, Thomas R.; Palmer, Nathan M. - 2023
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014317533
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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of Risk and Financial Management 15 (2022) 11, pp. 1-17
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014332691
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Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates
Barde, Sylvain - 2022
Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function, requiring the use of simulation-based inference,...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014480466
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Examining the vintage effect in hedonic pricing using spatially varying coefficients models: A case study of single-family houses in the Canton of Zurich
Dambon, Jakob A.; Fahrländer, Stefan Sebastian; … - In: Swiss Journal of Economics and Statistics 158 (2022) 1, pp. 1-14
This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred...
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Empirical Asset Pricing via Ensemble Gaussian Process Regression
Filipović, Damir; Pasricha, Puneet - 2022
We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014236083
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Examining the vintage effect in hedonic pricing using spatially varying coefficients models : a case study of single-family houses in the Canton of Zurich
Dambon, Jakob A.; Fahrländer, Stefan S.; Karlen, Saira; … - In: Swiss journal of economics and statistics 158 (2022) 1, pp. 1-14
This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012793516
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Neighborhood-Based Information Costs
Hébert, Benjamin; Woodford, Michael - 2022
We derive a new cost of information in rational inattention problems, the neighborhood-based cost functions, starting from the observation that many settings involve exogenous states with a topological structure. These cost functions are uniformly posterior-separable and capture notions of...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013314321
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Forecasting US inflation using Bayesian nonparametric models
Clark, Todd E.; Huber, Florian; Koop, Gary; Marcellino, … - 2022
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013277506
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Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates
Barde, Sylvain - 2022
Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function, requiring the use of simulation-based inference,...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013439970
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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013475217
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A probabilistic approach for denoising option prices
Gueye, Djibril; Lawuobahsumo, Kokulo - In: International journal of economics and financial issues … 13 (2023) 2, pp. 18-26
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Specification tests for non-Gaussian structural vector autoregressions
Amengual, Dante; Fiorentini, Gabriele; Sentana, Enrique - 2022
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013540674
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Consistent non-Gaussian pseudo maximum likelihood estimators of spatial autoregressive models
Jin, Fei; Wang, Yuqin - In: Econometric theory 40 (2024) 5, pp. 1120-1158
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Analyzing human search behavior when subjective returns are unobservable
Nakazato, Shinji; Yang, Bojian; Shimokawa, Tesuya - In: Computational economics 63 (2024) 5, pp. 1921-1947
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Sample and computationally efficient stochastic kriging in high dimensions
Ding, Liang; Zhang, Xiaowei - In: Operations research 72 (2024) 2, pp. 660-683
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014520867
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Consumption Dynamics and Welfare Under Non-Gaussian Earnings Risk
Guvenen, Fatih; Ozkan, Serdar; Madera, Rocio - National Bureau of Economic Research - 2024
Recent empirical studies document that the distribution of earnings changes displays substantial deviations from lognormality: in particular, earnings changes are negatively skewed with extremely high kurtosis (long and thick tails), and these non-Gaussian features vary substantially both over...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014528338
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Disasters everywhere : the costs of business cycles reconsidered
Jordà, Òscar; Schularick, Moritz; Taylor, Alan M. - In: IMF economic review 72 (2024) 1, pp. 116-151
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Bayesian herd detection for dynamic data
Keppo, Jussi; Satopää, Ville A. - In: International journal of forecasting 40 (2024) 1, pp. 285-301
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014450271
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Pre-owned housing price index forecasts using Gaussian process regression
Jin, Bingzi; Xu, Xiaojie - 2024
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015371626
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Synthetic dataset generation of driver telematics
So, Banghee; Boucher, Jean-Philippe; Valdez, Emiliano - In: Risks 9 (2021) 4, pp. 1-19
This article describes the techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations regarding driver's claims experience, together with...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10013200727
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Gaussian process regression for forecasting gasoline prices in Jordan
Ajlouni, Sameh Asim; Alodat, Moh'd Taleb - In: International Journal of Energy Economics and Policy : IJEEP 11 (2021) 3, pp. 502-509
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012622773
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Synthetic dataset generation of driver telematics
So, Banghee; Boucher, Jean-Philippe; Valdez, Emiliano - In: Risks : open access journal 9 (2021) 4, pp. 1-19
This article describes the techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations regarding driver's claims experience, together with...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012508585
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Inference in predictive regression models with persistent regressors
Hillmann, Benjamin - 2021
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Nearly optimal central limit theorem and bootstrap approximations in high dimensions
Chernozhukov, Victor; Četverikov, Denis N.; Koike, Yuta - 2021
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of n independent high-dimensional centered random vectors X1, . . . , Xn over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate. In the case...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012482915
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Inference in predictive regression models with persistent regressors
Hillmann, Benjamin - 2021
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Physics-informed Gaussian process regression for states estimation and forecasting in power grids
Tartakovsky, Alexandre M.; Ma, Tong; Barajas-Solano, … - In: International journal of forecasting 39 (2023) 2, pp. 967-980
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014465184
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Forecasting US inflation using bayesian nonparametric models
Clark, Todd E.; Huber, Florian; Koop, Gary; Marcellino, … - 2023
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10014326677
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Electricity demand forecasting and risk management using Gaussian process model with error propagation
Wen, Kuangyu; Wu, Wenbin; Wu, Ximing - In: Journal of forecasting 42 (2023) 4, pp. 957-969
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Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model
Fan, Yanqin; Han, Fang; Park, Hyeonseok - In: Journal of econometrics 237 (2023) 1, pp. 1-28
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Confidence bands for a distribution function with merged data from multiple sources
Saegusa, Takumi - In: Statistics in Transition New Series 21 (2020) 4, pp. 144-158
We consider nonparametric estimation of a distribution function when data are collected from multiple overlapping data sources. Main statistical challenges include (1) heterogeneity of data sets, (2) unidentified duplicated records across data sets, and (3) dependence due to sampling without...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012600249
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Bayesian Selection of Systemic Risk Networks
Ahelegbey, Daniel Felix - 2020
The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012856814
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Neighborhood-Based Information Costs
Hébert, Benjamin - 2020
We propose a new measure of the cost of information structures in rational inattention problems, the "neighborhood-based" cost functions, given that many applications involve states with a topological structure. These cost functions summarize the results of a sequential information sampling...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012479267
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Neighborhood-based information costs
Hébert, Benjamin; Woodford, Michael - 2020
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012194915
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Confidence bands for a distribution function with merged data from multiple sources
Saegusa, Takumi - In: Statistics in transition : an international journal of … 21 (2020) 4, pp. 144-158
We consider nonparametric estimation of a distribution function when data are collected from multiple overlapping data sources. Main statistical challenges include (1) heterogeneity of data sets, (2) unidentified duplicated records across data sets, and (3) dependence due to sampling without...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012317732
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Who are the hand-to-mouth
Aguiar, Mark; Bils, Mark; Boar, Corina - 2020
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012179247
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Machine learning for multiple yield curve markets : fast calibration in the Gaussian affine framework
Gümbel, Sandrine; Schmidt, Thorsten - In: Risks : open access journal 8 (2020) 2/50, pp. 1-18
Calibration is a highly challenging task, in particular in multiple yield curve markets. This paper is a first attempt to study the chances and challenges of the application of machine learning techniques for this. We employ Gaussian process regression, a machine learning methodology having many...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012292851
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Financial Applications of Gaussian Processes and Bayesian Optimization
Gonzalvez, Joan - 2019
In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as...
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012891532
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Non-Modellable Risk Factor (NMRF) Measurement Using Gaussian Process Regression (GPR)
Slime, Badreddine - 2019
One innovation defined in the new market risk rules by the Fundamental Review of the Trading Book (FRTB) is the Non-Modellable Risk Factor (NMRF) framework. This new concept introduces a methodology to differentiate between modellable and non-modellable risk factors in the Internal Models...
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Comprehensive testing of linearity against the smooth transition autoregressive model
Seong, Dakyung; Cho, Jin Seo; Teräsvirta, Timo - 2019 - This version: August 2019
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Statistical tests for cross-validation of Kriging models
Kleijnen, Jack P. C.; Beers, Wim C. M. van - 2019
Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10012010856
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Uniform inference in high-dimensional gaussian graphical models
Klaassen, Sven; Kück, Jannis; Spindler, Martin; … - 2019 - Version November 2018
Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures. We provide results for uniform inference on high-dimensional graphical models with the number of target parameters d being possible much...
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