Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric

A non-parametric-based computationally efficient approach for credit scoring

Ashofteh, A., & Bravo, J. M. (2019). A non-parametric-based computationally efficient approach for credit scoring. In Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao 2019: 19ª Conferencia da Associacao Portuguesa de Sistemas de Informacao, CAPSI 2019 - 19th Conference of the Portuguese Association for Information Systems, CAPSI 2019; Lisboa; Portugal; 11 October 2019 through 12 October 2019 (pp. 19). (Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao).This research aimed at the case of credit scoring in risk management and presented the novel method for credit scoring to be used for default prediction. This study uses Kruskal-Wallis non-parametric statistic to form a computationally efficient credit-scoring model based on artificial neural network to study the impact on modelling performance. The findings show that new credit scoring methodology represents reasonable coefficient of determination and low false negative rate. It is computationally less expensive with high accuracy (AUC=0.99). Because of the recent respective of continued credit/behavior scoring, our study suggests to use this credit score for non-traditional data sources such as mobile phone data to study and reveal changes of client’s behavior during the time. This is the first study that develops a non-parametric credit scoring, which is able to reselect effective features for continued credit evaluation and weighted out by their level of contribution with a good diagnostic ability.publishersversionpublishe

A Non-Parametric-Based Computationally Efficient Approach for Credit Scoring

This research aimed at the case of credit scoring in risk management and presented the novel method for credit scoring to be used for default prediction. This study uses Kruskal-Wallis non-parametric statistic to form a computationally efficient credit-scoring model based on artificial neural network to study the impact on modelling performance. The findings show that new credit scoring methodology represents reasonable coefficient of determination and low false negative rate. It is computationally less expensive with high accuracy (AUC=0.99). Because of the recent respective of continued credit/behavior scoring, our study suggests to use this credit score for non-traditional data sources such as mobile phone data to study and reveal changes of client’s behavior during the time. This is the first study that develops a non-parametric credit scoring, which is able to reselect effective features for continued credit evaluation and weighted out by their level of contribution with a good diagnostic ability

Перетворення структури складної технічної системи із частково недоступними елементами до зорового образу

The issues of structure state recognition of the hidden part of complex network objects under limited information from their hardly usable elements, including intellectual transformation of information from usable elements into some visual image of the entire object, followed by its recognition and restoration of damaged structures were considered.The proposed method for the state recognition of network objects formed the basis for constructing the intelligent decision support system during operation and re-engineering of renewable wireless computer networks with the elements, unusable for direct monitoring that increase the structural reliability of these networks.To achieve the goal, the following tasks were solved: the overall structure of the method for the structure transformation to the visual image was proposed; the theoretical basis of the method, which is the scientific novelty of the work was formulated.Testing of the proposed method within the common system of maintaining performance and re-engineering of damaged wireless computer networks was performed.Рассмотрено распознавание состояния структуры скрытой части сложных сетевых объектов в условиях ограниченной информации от их труднодоступных элементов. Метод распознавания состояния сетевых объектов лег в основу построения интеллектуальной системы поддержки принятия решений при эксплуатации и реинжиниринге восстанавливаемых беспроводных компьютерных сетей с недоступными для непосредственного мониторинга элементами.Розглянуто розпізнавання стану структури прихованої частини складних мережевих об'єктів в умовах обмеженої інформації від їх важкодоступних елементів. Метод розпізнавання стану мережних об'єктів ліг в основу побудови інтелектуальної системи підтримки прийняття рішень при експлуатації та реінжинірингу відновлюваних бездротових комп'ютерних мереж з недоступними для моніторингу елементами

Перетворення структури складної технічної системи із частково недоступними елементами до зорового образу

The issues of structure state recognition of the hidden part of complex network objects under limited information from their hardly usable elements, including intellectual transformation of information from usable elements into some visual image of the entire object, followed by its recognition and restoration of damaged structures were considered.The proposed method for the state recognition of network objects formed the basis for constructing the intelligent decision support system during operation and re-engineering of renewable wireless computer networks with the elements, unusable for direct monitoring that increase the structural reliability of these networks.To achieve the goal, the following tasks were solved: the overall structure of the method for the structure transformation to the visual image was proposed; the theoretical basis of the method, which is the scientific novelty of the work was formulated.Testing of the proposed method within the common system of maintaining performance and re-engineering of damaged wireless computer networks was performed.Рассмотрено распознавание состояния структуры скрытой части сложных сетевых объектов в условиях ограниченной информации от их труднодоступных элементов. Метод распознавания состояния сетевых объектов лег в основу построения интеллектуальной системы поддержки принятия решений при эксплуатации и реинжиниринге восстанавливаемых беспроводных компьютерных сетей с недоступными для непосредственного мониторинга элементами.Розглянуто розпізнавання стану структури прихованої частини складних мережевих об'єктів в умовах обмеженої інформації від їх важкодоступних елементів. Метод розпізнавання стану мережних об'єктів ліг в основу побудови інтелектуальної системи підтримки прийняття рішень при експлуатації та реінжинірингу відновлюваних бездротових комп'ютерних мереж з недоступними для моніторингу елементами