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文献信息
Computational Statistics (CompStat) is an international journal that promotes the publication of applications and methodological research in Computational Statistics and Data Science. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa with special attention to contributions analyzing complex data sets. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, big data analysis, functional data analysis, image analysis, spatial data analysis, graphics, simulation, algorithms, knowledge-based systems, non/semi-parametrics, and Bayesian computing. CompStat publishes package reports and software articles.
Variable SelectionMaximum LikelihoodMaximum Likelihood EstimationMarkov Chain Monte CarloEM AlgorithmNonparametric RegressionMonte Carlo SimulationModel SelectionConfidence IntervalsBayesian InferenceQuantile RegressionCensored DataRegression ModelTest StatisticData ExampleTime SeriesLongitudinal DataBandwidth SelectionSample SizeData Set
vol.40 (2025)
vol.39 (2024)
vol.38 (2023)
vol.37 (2022)
vol.36 (2021)
vol.35 (2020)
vol.34 (2019)
vol.33 (2018)
vol.32 (2017)
vol.31 (2016)
vol.30 (2015)
vol.29 (2014)
vol.28 (2013)
vol.27 (2012)
vol.26 (2011)
vol.25 (2010)
vol.24 (2009)
vol.23 (2008)
vol.22 (2007)
vol.21 (2006)
vol.20 (2005)
vol.19 (2004)
vol.18 (2003)
vol.17 (2002)
vol.16 (2001)
vol.15 (2000)
vol.14 (1999)
vol.13 (1998)
vol.12 (1997)
vol.11 (1996)
vol.10 (1995)
vol.9 (1994)