Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2

[1]  W. Messner,et al.  Being happy. The role of personal value priorities in subjective well-being across European countries , 2023, International Journal of Cross Cultural Management.

[2]  W. Messner,et al.  Improving the cross-cultural functioning of deep artificial neural networks through machine enculturation , 2022, Int. J. Inf. Manag. Data Insights.

[3]  W. Messner,et al.  Cultural Differences in an Artificial Representation of the Human Emotional Brain System: A Deep Learning Study , 2022, Journal of International Marketing.

[4]  Joao Marques-Silva,et al.  Delivering Trustworthy AI through Formal XAI , 2022, AAAI.

[5]  Christian Janiesch,et al.  Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability , 2022, Int. J. Inf. Manag..

[6]  Zeng-Wei Hong,et al.  A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application , 2022, Mathematics.

[7]  Marcus Eng Hock Ong,et al.  Shapley variable importance clouds for interpretable machine learning , 2021, ArXiv.

[8]  Matthijs J. Warrens,et al.  The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation , 2021, PeerJ Comput. Sci..

[9]  R. Thurasamy,et al.  The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis , 2021, PloS one.

[10]  Christopher J. Anders,et al.  Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications , 2021, Proceedings of the IEEE.

[11]  P. Biecek,et al.  dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python , 2020, J. Mach. Learn. Res..

[12]  Anne Gerdes,et al.  Dialogical Guidelines Aided by Knowledge Acquisition: Enhancing the Design of Explainable Interfaces and Algorithmic Accuracy , 2020 .

[13]  Martin Spindler,et al.  An explainable attention network for fraud detection in claims management , 2020, Journal of Econometrics.

[14]  Wolfgang Messner,et al.  Empirically assessing noisy necessary conditions with activation functions , 2020, Computational Management Science.

[15]  I. Moustaki,et al.  Assessing Partial Association Between Ordinal Variables: Quantification, Visualization, and Hypothesis Testing , 2020, Journal of the American Statistical Association.

[16]  Jingyi Jessica Li,et al.  Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines , 2020, Patterns.

[17]  Yavuz Selim Güçlü,et al.  Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA , 2020 .

[18]  Antonio Vetro,et al.  On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI - Three Challenges for Future Research , 2020, Inf..

[19]  Marinka Zitnik,et al.  Interpretability of machine learning‐based prediction models in healthcare , 2020, WIREs Data Mining Knowl. Discov..

[20]  Minglei Ren,et al.  Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series , 2020, Frontiers in Earth Science.

[21]  Angelo Gemignani,et al.  Machine Learning in Psychometrics and Psychological Research , 2020, Frontiers in Psychology.

[22]  Keon Myung Lee,et al.  Autonomic machine learning platform , 2019, Int. J. Inf. Manag..

[23]  Md. Hussain,et al.  pyMannKendall: a python package for non parametric Mann Kendall family of trend tests , 2019, J. Open Source Softw..

[24]  Franco Turini,et al.  Meaningful Explanations of Black Box AI Decision Systems , 2019, AAAI.

[25]  Jesse Thomason,et al.  Interpreting Black Box Models via Hypothesis Testing , 2019, FODS.

[26]  Sarah Filippi,et al.  Interpreting Deep Neural Networks Through Variable Importance , 2019, ArXiv.

[27]  Wei Yu,et al.  Tuning Deep Learning Performance for Android Malware Detection , 2018, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[28]  Filip Karlo Dosilovic,et al.  Explainable artificial intelligence: A survey , 2018, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[29]  Aaron J. Fisher,et al.  All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously , 2018, J. Mach. Learn. Res..

[30]  Tim Miller,et al.  Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences , 2017, ArXiv.

[31]  Derek Doran,et al.  What Does Explainable AI Really Mean? A New Conceptualization of Perspectives , 2017, CEx@AI*IA.

[32]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[33]  D. Apley,et al.  Visualizing the effects of predictor variables in black box supervised learning models , 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[34]  W. Messner The contribution of subjective measures to the quantification of social progress: Evidence from Europe and Israel , 2016 .

[35]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[36]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[37]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[38]  Kristen A. Lindquist,et al.  A functional architecture of the human brain: emerging insights from the science of emotion , 2012, Trends in Cognitive Sciences.

[39]  S. Joseph,et al.  Applied Positive Psychology: A New Perspective for Professional Practice. , 2012 .

[40]  Donald Hedeker,et al.  A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED , 2012, Front. Psychology.

[41]  E. Kelloway,et al.  Transformational leadership and employee psychological well-being: The mediating role of employee trust in leadership , 2012 .

[42]  R.J.J. Wielers,et al.  What makes workers happy? , 2011 .

[43]  Douglas G Altman,et al.  How to obtain the P value from a confidence interval , 2011, BMJ : British Medical Journal.

[44]  Mukta Paliwal,et al.  Assessing the contribution of variables in feed forward neural network , 2011, Appl. Soft Comput..

[45]  Robert Biswas-Diener,et al.  Prosocial Spending and Well-Being: Cross-Cultural Evidence for a Psychological Universal , 2010, Journal of personality and social psychology.

[46]  J. Helliwell,et al.  Trust and Well-Being , 2010 .

[47]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[48]  C. Ferguson An effect size primer: A guide for clinicians and researchers. , 2009 .

[49]  Michael McAleer,et al.  An alternative approach to estimating demand: neural network regression with conditional volatility for high frequency air passenger arrivals. , 2008 .

[50]  Thomas G. Cummings,et al.  Quest for an Engaged Academy , 2007 .

[51]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[52]  Stavros Papadokonstantakis,et al.  Comparison of recent methods for inference of variable influence in neural networks , 2006, Neural Networks.

[53]  Richard E. Lucas,et al.  Personality, culture, and subjective well-being: emotional and cognitive evaluations of life. , 2003, Annual review of psychology.

[54]  Tony Delamothe,et al.  Happiness , 2002, BMJ : British Medical Journal.

[55]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[56]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[57]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[58]  Richard E. Lucas,et al.  Subjective Weil-Being: Three Decades of Progress , 2004 .

[59]  Roger Bakeman,et al.  Determining the power of multiple regression analyses both with and without repeated measures , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[60]  Nigel Crook,et al.  Using input parameter influences to support the decisions of feedforward neural networks , 1999, Neurocomputing.

[61]  Lars I. Nord,et al.  A novel method for examination of the variable contribution to computational neural network models , 1998 .

[62]  Khaled H. Hamed,et al.  A modified Mann-Kendall trend test for autocorrelated data , 1998 .

[63]  Neil A. Thacker,et al.  Algorithmic modelling for performance evaluation , 1997, Machine Vision and Applications.

[64]  Judith M. Collins,et al.  AN APPLICATION OF THE THEORY OF NEURAL COMPUTATION TO THE PREDICTION OF WORKPLACE BEHAVIOR: AN ILLUSTRATION AND ASSESSMENT OF NETWORK ANALYSIS , 1993 .

[65]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[66]  R. Hirsch,et al.  Techniques of trend analysis for monthly water quality data , 1982 .

[67]  H. B. Mann Nonparametric Tests Against Trend , 1945 .

[68]  Yogesh Kumar Dwivedi,et al.  Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis , 2022, Journal of Business Research.

[69]  Nwqep Notes Statistical Analysis for Monotonic Trends , 2011 .

[70]  A. The Cultural Psychology of the Emotions , 2010 .

[71]  D. Kahneman,et al.  Income's association with judgments of life versus feelings. , 2010 .

[72]  J. Helliwell,et al.  NBER WORKING PAPER SERIES INTERNATIONAL EVIDENCE ON THE SOCIAL CONTEXT OF WELL-BEING , 2008 .

[73]  Jonathan Haidt,et al.  The cultural psychology of the emotions: Ancient and renewed. , 2008 .

[74]  E. Diener,et al.  Subjective well-being. The science of happiness and a proposal for a national index. , 2000, The American psychologist.

[75]  Richard D. De Veaux,et al.  Multicollinearity: A tale of two nonparametric regressions , 1994 .

[76]  A. N. PETTrrr A Non-parametric Approach to the Change-point Problem , 1979 .