- Email: [email protected]
Second special issue on statistical algorithms and software
Computational Statistics and Data Analysis 54 (2010) 609–610
Contents lists available at ScienceDirect
Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda
Editorial
Second special issue on statistical algorithms and software
Computational Statistics and Data Analysis has long published articles on algorithms and software. Recently it published its first Special Issue on Statistical Algorithms and Software (Gatu et al., 2007a). The 15 papers in the issue included, among others: Gatu et al. (2007b) on all possible regression submodels; Park et al. (2007) on sampling streaming data; Tvrdik, Krivy and Misik (2007) on adaptive population search; and several software packages (Aluja-Banet et al., 2007; Massmann, 2007; Hohle and Feldmann, 2007). The present issue is the journal’s second such special issue, in which we have again collected fifteen papers. Three papers focus on specific software packages. Bulla et al. (2010) present an R package for analyzing hidden semi-Markov models. Harrington and Salibian-Barrera (2010) use the package BIRCH to find approximate solutions to combinatorial problems on large datasets. Wua et al. (2010) introduce the package GAP, which is designed to facilitate matrix visualization and cluster analysis. Seven papers present algorithms for solving statistical problems. Davidov and Iliopoulos (2010) comment on an iterative algorithm for nonparametric estimation in biased sampling models. Hu and Kam-Wah (2010) offer a Bayesian approach to distributed evolutionary Monte Carlo. Iacobucci et al. (2010) use double Rao-Blackwellisation to enhance variance stabilisation in Population Monte Carlo. McNicholas et al. (2010) offer serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Poitevineau and Lecoutre (2010) use the K-prime and K-square distributions for Bayesian predictive procedures. Reddy and Rajaratnam (2010) use component-wise parameter smoothing to learn mixture models. Saadaoui (2010) reviews EM acceleration procedures and offers a new method. Five methodological papers have algorithmic or software components. Escanciano and Jacho-Chavez (2010) approximate the critical values of the Cramer-von Mises statistic. Gallegos and Ritter (2010) use combinatorial optimization for clustering with cardinality constraints. Hanea et al. (2010) use Bayesian Belief Networks to mine and visualize ordinal data. Yang et al. (2010) discuss generalized quasi-regression. Yucel and Demirtas (2010) use simulation to assess the impact of non-normal random effects on inference by multiple imputation. Computational Statistics and Data Analysis will continue to publish special issues devoted to statistical algorithms and software, providing researchers a specialized outlet for disseminating their advances in these areas. References Aluja-Banet, Tomas, Daunis-i-Estadella, Josep, Pellicer, David, 2007. GRAFT, a complete system for data fusion. Computational Statistics and Data Analysis 52 (2), 635–649. Bulla, Jan, Ingo, Bulla, Oleg, Nenadic, 2010. hsmm — an R package for analyzing hidden semi-markov models. Computational Statistics and Data Analysis 54 (3), 611–619. Davidov, Ori, Iliopoulos, George, 2010. A note on an iterative algorithm for nonparametric estimation in biased sampling models. Computational Statistics and Data Analysis 54 (3), 620–624. Escanciano, Juan Carlos, Jacho-Chavez, David T., 2010. Approximating the critical values of cramer-von mises tests in general parametric conditional specifications. Computational Statistics and Data Analysis 54 (3), 625–636. Gallegos, Maria T., Gunter, Ritter, 2010. Using combinatorial optimization in model-based trimmed clustering with cardinality constraints. Computational Statistics and Data Analysis 54 (3), 637–654. Gatu, Cristian, Gentle, James, Hinde, John, Huh, Moon, 2007a. Special issue on statistical algorithms and software. Computational Statistics and Data Analysis 52 (2), 655–667. Gatu, Cristian, Yanev, Petko I., Kontoghiorghes, Erricos J., 2007b. A graph approach to generate all possible regression submodels. Computational Statistics and Data Analysis 52 (2), 799–815. Harrington, Justin, Salibian-Barrera, Matias, 2010. Finding approximate solutions to combinatorial problems with very large data sets using BIRCH. Computational Statistics and Data Analysis 54 (3), 655–667. Hanea, A.M., Kurowicka, D., Cooke, R.M., Ababei, D.A., 2010. Mining and visualising ordinal data with non-parametric continuous BBNs. Computational Statistics and Data Analysis 54 (3), 668–687. Hohle, Michael, Feldmann, Ulrike, 2007. RLadyBug: R package for stochastic epidemic models. Computational Statistics and Data Analysis 52 (2), 680–686. 0167-9473/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.csda.2009.11.009
610
Editorial / Computational Statistics and Data Analysis 54 (2010) 609–610
Hu, Bo, Tsui, Kam-Wah, 2010. Distributed evolutionary Monte Carlo for bayesian computing. Computational Statistics and Data Analysis 54 (3), 688–697. Iacobucci, Allesandra, Marin, Jean-Michel, Robert, Christian, 2010. On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation. Computational Statistics and Data Analysis 54 (3), 698–710. Massmann, Michael, 2007. Cobra: A Package for co-breaking analysis. Computational Statistics and Data Analysis 52 (2), 663–679. McNicholas, P.D., Murphy, P.D., McDaid, A.F., Frost, D., 2010. Serial and parallel implementations of model-based clustering via parsimonious gaussian mixture models. Computational Statistics and Data Analysis 54 (3), 711–723. Park, Byung-Hoon, Ostrouchov, George, Samatova, Nagiza F., 2007. Sampling streaming data with replacement. Computational Statistics and Data Analysis 52 (2), 750–762. Poitevineau, Jacques, Lecoutre, Bruno, 2010. Implementing Bayesian predictive procedures: The K-prime and K-square distributions. Computational Statistics and Data Analysis 54 (3), 724–731. Reddy, Chandan K., Rajaratnam, Bala, 2010. Learning mixture models via component-wise parameter smoothing. Computational Statistics and Data Analysis 54 (3), 732–749. Saadaoui, Foued, 2010. Acceleration of the EM algorithm via extrapolation methods: Review comparison and new methods. Computational Statistics and Data Analysis 54 (3), 750–766. Wua, Han-Ming, Tien, Yin-Jing, Chen, Chun-houh, 2010. GAP: A graphical environment for matrix visualization and cluster analysis. Computational Statistics and Data Analysis 54 (3), 767–778. Yang, Guijin, Wang, Zhigang, Deng, Wei, 2010. Unbiased generalized quasi-regression. Computational Statistics and Data Analysis 54 (3), 779–789. Yucel, Recai M., Demirtas, Hakan, 2010. Impact of non-normal random effects on inference by multiple imputation: A simulation assessment. Computational Statistics and Data Analysis 54 (3), 790–801.
Cristian Gatu Faculty of Computer Science, ‘‘Alexandru Ioan Cuza’’ University of Iasi, 16, Gen. Berthelot St., 700483 - Iasi, Romania Department of Public and Business Administration, University of Cyprus, P.O. Box 20537, CY-1678 Nicosia, Cyprus E-mail address: [email protected]. B.D. McCullough Department of Decision Sciences, LeBow College of Business, Drexel University, Philadlephia, PA 19104-2875, United States E-mail address: [email protected].
Contents lists available at ScienceDirect
Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda
Editorial
Second special issue on statistical algorithms and software
Computational Statistics and Data Analysis has long published articles on algorithms and software. Recently it published its first Special Issue on Statistical Algorithms and Software (Gatu et al., 2007a). The 15 papers in the issue included, among others: Gatu et al. (2007b) on all possible regression submodels; Park et al. (2007) on sampling streaming data; Tvrdik, Krivy and Misik (2007) on adaptive population search; and several software packages (Aluja-Banet et al., 2007; Massmann, 2007; Hohle and Feldmann, 2007). The present issue is the journal’s second such special issue, in which we have again collected fifteen papers. Three papers focus on specific software packages. Bulla et al. (2010) present an R package for analyzing hidden semi-Markov models. Harrington and Salibian-Barrera (2010) use the package BIRCH to find approximate solutions to combinatorial problems on large datasets. Wua et al. (2010) introduce the package GAP, which is designed to facilitate matrix visualization and cluster analysis. Seven papers present algorithms for solving statistical problems. Davidov and Iliopoulos (2010) comment on an iterative algorithm for nonparametric estimation in biased sampling models. Hu and Kam-Wah (2010) offer a Bayesian approach to distributed evolutionary Monte Carlo. Iacobucci et al. (2010) use double Rao-Blackwellisation to enhance variance stabilisation in Population Monte Carlo. McNicholas et al. (2010) offer serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Poitevineau and Lecoutre (2010) use the K-prime and K-square distributions for Bayesian predictive procedures. Reddy and Rajaratnam (2010) use component-wise parameter smoothing to learn mixture models. Saadaoui (2010) reviews EM acceleration procedures and offers a new method. Five methodological papers have algorithmic or software components. Escanciano and Jacho-Chavez (2010) approximate the critical values of the Cramer-von Mises statistic. Gallegos and Ritter (2010) use combinatorial optimization for clustering with cardinality constraints. Hanea et al. (2010) use Bayesian Belief Networks to mine and visualize ordinal data. Yang et al. (2010) discuss generalized quasi-regression. Yucel and Demirtas (2010) use simulation to assess the impact of non-normal random effects on inference by multiple imputation. Computational Statistics and Data Analysis will continue to publish special issues devoted to statistical algorithms and software, providing researchers a specialized outlet for disseminating their advances in these areas. References Aluja-Banet, Tomas, Daunis-i-Estadella, Josep, Pellicer, David, 2007. GRAFT, a complete system for data fusion. Computational Statistics and Data Analysis 52 (2), 635–649. Bulla, Jan, Ingo, Bulla, Oleg, Nenadic, 2010. hsmm — an R package for analyzing hidden semi-markov models. Computational Statistics and Data Analysis 54 (3), 611–619. Davidov, Ori, Iliopoulos, George, 2010. A note on an iterative algorithm for nonparametric estimation in biased sampling models. Computational Statistics and Data Analysis 54 (3), 620–624. Escanciano, Juan Carlos, Jacho-Chavez, David T., 2010. Approximating the critical values of cramer-von mises tests in general parametric conditional specifications. Computational Statistics and Data Analysis 54 (3), 625–636. Gallegos, Maria T., Gunter, Ritter, 2010. Using combinatorial optimization in model-based trimmed clustering with cardinality constraints. Computational Statistics and Data Analysis 54 (3), 637–654. Gatu, Cristian, Gentle, James, Hinde, John, Huh, Moon, 2007a. Special issue on statistical algorithms and software. Computational Statistics and Data Analysis 52 (2), 655–667. Gatu, Cristian, Yanev, Petko I., Kontoghiorghes, Erricos J., 2007b. A graph approach to generate all possible regression submodels. Computational Statistics and Data Analysis 52 (2), 799–815. Harrington, Justin, Salibian-Barrera, Matias, 2010. Finding approximate solutions to combinatorial problems with very large data sets using BIRCH. Computational Statistics and Data Analysis 54 (3), 655–667. Hanea, A.M., Kurowicka, D., Cooke, R.M., Ababei, D.A., 2010. Mining and visualising ordinal data with non-parametric continuous BBNs. Computational Statistics and Data Analysis 54 (3), 668–687. Hohle, Michael, Feldmann, Ulrike, 2007. RLadyBug: R package for stochastic epidemic models. Computational Statistics and Data Analysis 52 (2), 680–686. 0167-9473/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.csda.2009.11.009
610
Editorial / Computational Statistics and Data Analysis 54 (2010) 609–610
Hu, Bo, Tsui, Kam-Wah, 2010. Distributed evolutionary Monte Carlo for bayesian computing. Computational Statistics and Data Analysis 54 (3), 688–697. Iacobucci, Allesandra, Marin, Jean-Michel, Robert, Christian, 2010. On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation. Computational Statistics and Data Analysis 54 (3), 698–710. Massmann, Michael, 2007. Cobra: A Package for co-breaking analysis. Computational Statistics and Data Analysis 52 (2), 663–679. McNicholas, P.D., Murphy, P.D., McDaid, A.F., Frost, D., 2010. Serial and parallel implementations of model-based clustering via parsimonious gaussian mixture models. Computational Statistics and Data Analysis 54 (3), 711–723. Park, Byung-Hoon, Ostrouchov, George, Samatova, Nagiza F., 2007. Sampling streaming data with replacement. Computational Statistics and Data Analysis 52 (2), 750–762. Poitevineau, Jacques, Lecoutre, Bruno, 2010. Implementing Bayesian predictive procedures: The K-prime and K-square distributions. Computational Statistics and Data Analysis 54 (3), 724–731. Reddy, Chandan K., Rajaratnam, Bala, 2010. Learning mixture models via component-wise parameter smoothing. Computational Statistics and Data Analysis 54 (3), 732–749. Saadaoui, Foued, 2010. Acceleration of the EM algorithm via extrapolation methods: Review comparison and new methods. Computational Statistics and Data Analysis 54 (3), 750–766. Wua, Han-Ming, Tien, Yin-Jing, Chen, Chun-houh, 2010. GAP: A graphical environment for matrix visualization and cluster analysis. Computational Statistics and Data Analysis 54 (3), 767–778. Yang, Guijin, Wang, Zhigang, Deng, Wei, 2010. Unbiased generalized quasi-regression. Computational Statistics and Data Analysis 54 (3), 779–789. Yucel, Recai M., Demirtas, Hakan, 2010. Impact of non-normal random effects on inference by multiple imputation: A simulation assessment. Computational Statistics and Data Analysis 54 (3), 790–801.
Cristian Gatu Faculty of Computer Science, ‘‘Alexandru Ioan Cuza’’ University of Iasi, 16, Gen. Berthelot St., 700483 - Iasi, Romania Department of Public and Business Administration, University of Cyprus, P.O. Box 20537, CY-1678 Nicosia, Cyprus E-mail address: [email protected]. B.D. McCullough Department of Decision Sciences, LeBow College of Business, Drexel University, Philadlephia, PA 19104-2875, United States E-mail address: [email protected].