The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Natasa Milic - One of the best experts on this subject based on the ideXlab platform.
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the importance of medical students attitudes regarding cognitive competence for teaching Applied Statistics multi site study and meta analysis
PLOS ONE, 2016Co-Authors: Natasa Milic, Srdjan Masic, Jelena Milinlazovic, Goran Trajkovic, Zoran Bukumiric, Marko Savic, Andja Cirkovic, Milan GajicAbstract:Background The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students’ attitudes towards Statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching Applied Statistics to medical students. Methods A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in bioStatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and Statistics achievement. Pooled estimates were calculated using random effects models. Results SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards Statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students’ achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32–0.41). Conclusion Students' subjective attitudes regarding Cognitive Competence at the beginning of the bioStatistics course, which were directly linked to mathematical knowledge, affected their attitudes at the end of the course that, in turn, influenced students' performance. This indicates the importance of positively changing not only students’ cognitive competency, but also their perceptions of gained competency during the bioStatistics course.
Jose Miguel Valiente - One of the best experts on this subject based on the ideXlab platform.
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defect detection in random colour textures using the mia t 2 defect maps
International Conference on Image Analysis and Recognition, 2006Co-Authors: Fernando Duran Lopez, Jose Manuel Prats, Alberto Ferrer, Jose Miguel ValienteAbstract:In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of Applied Statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.
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defect detection in random colour textures using the mia t2defect maps
Lecture Notes in Computer Science, 2006Co-Authors: Fernando Duran Lopez, Jose Manuel Prats, Alberto Ferrer, Jose Miguel ValienteAbstract:In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T 2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of Applied Statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T 2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.
Milan Gajic - One of the best experts on this subject based on the ideXlab platform.
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the importance of medical students attitudes regarding cognitive competence for teaching Applied Statistics multi site study and meta analysis
PLOS ONE, 2016Co-Authors: Natasa Milic, Srdjan Masic, Jelena Milinlazovic, Goran Trajkovic, Zoran Bukumiric, Marko Savic, Andja Cirkovic, Milan GajicAbstract:Background The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students’ attitudes towards Statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching Applied Statistics to medical students. Methods A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in bioStatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and Statistics achievement. Pooled estimates were calculated using random effects models. Results SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards Statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students’ achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32–0.41). Conclusion Students' subjective attitudes regarding Cognitive Competence at the beginning of the bioStatistics course, which were directly linked to mathematical knowledge, affected their attitudes at the end of the course that, in turn, influenced students' performance. This indicates the importance of positively changing not only students’ cognitive competency, but also their perceptions of gained competency during the bioStatistics course.
Fernando Duran Lopez - One of the best experts on this subject based on the ideXlab platform.
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defect detection in random colour textures using the mia t 2 defect maps
International Conference on Image Analysis and Recognition, 2006Co-Authors: Fernando Duran Lopez, Jose Manuel Prats, Alberto Ferrer, Jose Miguel ValienteAbstract:In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of Applied Statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.
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defect detection in random colour textures using the mia t2defect maps
Lecture Notes in Computer Science, 2006Co-Authors: Fernando Duran Lopez, Jose Manuel Prats, Alberto Ferrer, Jose Miguel ValienteAbstract:In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T 2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of Applied Statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T 2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.
Rocio Titiunik - One of the best experts on this subject based on the ideXlab platform.
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optimal data driven regression discontinuity plots
Journal of the American Statistical Association, 2015Co-Authors: Sebastian Calonico, Matias D Cattaneo, Rocio TitiunikAbstract:Exploratory data analysis plays a central role in Applied Statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated because it provides both easy presentation and transparent validation of the design. RD plots are nowadays widely used in applications, despite its formal properties being unknown: these plots are typically presented employing ad hoc choices of tuning parameters, which makes these procedures less automatic and more subjective. In this article, we formally study the most common RD plot based on an evenly spaced binning of the data, and propose several (optimal) data-driven choices for the number of bins depending on the goal of the researcher. These RD plots are constructed either to approximate the underlying unknown regression functions without imposing smoothness in the estimator, or to approximate the underlying variability of the raw data while smoothing out the otherwise uninformative scatterplot of the da...
