The Experts below are selected from a list of 79371 Experts worldwide ranked by ideXlab platform
Chardin Wese Simen - One of the best experts on this subject based on the ideXlab platform.
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financial data science the birth of a new financial research paradigm complementing econometrics
European Journal of Finance, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
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financial data science the birth of a new financial research paradigm complementing econometrics
Social Science Research Network, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:In this paper, we compare and contrast financial data science with econometrics and conclude that the former is inevitably interdisciplinary due to the numerous skill-sets needed within a competitive research team. The latter, in contrast, is firmly rooted in economics. Both areas are highly complementary, as they share an equivalent process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from the age of big data. First, the ever increasing computational power allows researchers to experiment with an extremely large number of generated test subjects and leads to the challenge of p-hacking. Second, the extremely large number of observations available in big data sets provide levels of statistical power at which common statistical significance levels are barely a challenge. We argue that the former challenge can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on the integrity of the research process and the researchers themselves, with pre-registration and actual out-of-sample periods being the best technical though in themselves potentially insufficient tools. The latter challenge can be addressed in two ways. First, researchers can simply use more stringent statistical significance levels such as 0.1%, 0.5% and 1% instead of 1%, 5% and 10%, respectively. Second, and more importantly, researchers can use additional criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial in the age of big data, as it appears not impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
Chris Brooks - One of the best experts on this subject based on the ideXlab platform.
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financial data science the birth of a new financial research paradigm complementing econometrics
European Journal of Finance, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
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financial data science the birth of a new financial research paradigm complementing econometrics
Social Science Research Network, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:In this paper, we compare and contrast financial data science with econometrics and conclude that the former is inevitably interdisciplinary due to the numerous skill-sets needed within a competitive research team. The latter, in contrast, is firmly rooted in economics. Both areas are highly complementary, as they share an equivalent process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from the age of big data. First, the ever increasing computational power allows researchers to experiment with an extremely large number of generated test subjects and leads to the challenge of p-hacking. Second, the extremely large number of observations available in big data sets provide levels of statistical power at which common statistical significance levels are barely a challenge. We argue that the former challenge can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on the integrity of the research process and the researchers themselves, with pre-registration and actual out-of-sample periods being the best technical though in themselves potentially insufficient tools. The latter challenge can be addressed in two ways. First, researchers can simply use more stringent statistical significance levels such as 0.1%, 0.5% and 1% instead of 1%, 5% and 10%, respectively. Second, and more importantly, researchers can use additional criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial in the age of big data, as it appears not impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
Anthony W Butch - One of the best experts on this subject based on the ideXlab platform.
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biological variation of immunological blood biomarkers in healthy Individuals and quality goals for biomarker tests
BMC Immunology, 2019Co-Authors: Najib Aziz, Roger Detels, Joshua J Quint, David W Gjertson, Timothy Ryner, Anthony W ButchAbstract:Cytokines, chemokines, adipocytokines, soluble cell receptors, and immune activation markers play an important role in immune responsiveness and can provide prognostic value since they reflect underlying conditions and disease states. This study was undertaken to investigate the components of biological variation for various laboratory tests of blood immunological biomarkers. Estimates of intra-Individual Coefficient of variation (CVI) and inter-Individual Coefficient of variation (CVG) were examined for blood immunological biomarkers. Biomarkers with CVI 40% were adiponectin, IL-1ra, leptin, MIP-1β, sCD163, and sIL-2Rα. The biological variations of biomarkers have important monitoring value for longitudinal investigation and are essential for quality specification of tests that are performed in the laboratory. The CVI was relatively small while CVG was comparatively large and mean values of each biomarker vary between subjects. The Individuality of biomarkers significantly influences reference interval values. A majority of the biomarkers in this study had strong Individuality and the result of each biomarker should be cautiously interpreted if using established reference interval values. Comparison of a patient’s test result with previous ones may be more useful than the usage of conventional reference values.
Andrew Vivian - One of the best experts on this subject based on the ideXlab platform.
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financial data science the birth of a new financial research paradigm complementing econometrics
European Journal of Finance, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
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financial data science the birth of a new financial research paradigm complementing econometrics
Social Science Research Network, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:In this paper, we compare and contrast financial data science with econometrics and conclude that the former is inevitably interdisciplinary due to the numerous skill-sets needed within a competitive research team. The latter, in contrast, is firmly rooted in economics. Both areas are highly complementary, as they share an equivalent process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from the age of big data. First, the ever increasing computational power allows researchers to experiment with an extremely large number of generated test subjects and leads to the challenge of p-hacking. Second, the extremely large number of observations available in big data sets provide levels of statistical power at which common statistical significance levels are barely a challenge. We argue that the former challenge can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on the integrity of the research process and the researchers themselves, with pre-registration and actual out-of-sample periods being the best technical though in themselves potentially insufficient tools. The latter challenge can be addressed in two ways. First, researchers can simply use more stringent statistical significance levels such as 0.1%, 0.5% and 1% instead of 1%, 5% and 10%, respectively. Second, and more importantly, researchers can use additional criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial in the age of big data, as it appears not impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
David G Mcmillan - One of the best experts on this subject based on the ideXlab platform.
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financial data science the birth of a new financial research paradigm complementing econometrics
European Journal of Finance, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.
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financial data science the birth of a new financial research paradigm complementing econometrics
Social Science Research Network, 2019Co-Authors: Chris Brooks, Andreas G F Hoepner, David G Mcmillan, Andrew Vivian, Chardin Wese SimenAbstract:In this paper, we compare and contrast financial data science with econometrics and conclude that the former is inevitably interdisciplinary due to the numerous skill-sets needed within a competitive research team. The latter, in contrast, is firmly rooted in economics. Both areas are highly complementary, as they share an equivalent process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from the age of big data. First, the ever increasing computational power allows researchers to experiment with an extremely large number of generated test subjects and leads to the challenge of p-hacking. Second, the extremely large number of observations available in big data sets provide levels of statistical power at which common statistical significance levels are barely a challenge. We argue that the former challenge can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on the integrity of the research process and the researchers themselves, with pre-registration and actual out-of-sample periods being the best technical though in themselves potentially insufficient tools. The latter challenge can be addressed in two ways. First, researchers can simply use more stringent statistical significance levels such as 0.1%, 0.5% and 1% instead of 1%, 5% and 10%, respectively. Second, and more importantly, researchers can use additional criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant Coefficients. Especially statistical relevance seems crucial in the age of big data, as it appears not impossible for an Individual Coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.