scholarly journals Linear Models for the Impact of Order Flow on Prices II. The Mixture Transition Distribution Model

2016 ◽  
Author(s):  
Damian Eduardo Taranto ◽  
Giacomo Bormetti ◽  
Jean-Philippe Bouchaud ◽  
Fabrizio Lillo ◽  
Bence Toth
Keyword(s):  
Linear Models ◽  
Distribution Model ◽  
Order Flow ◽  
Transition Distribution ◽  
The Impact

scholarly journals Linear models for the impact of order flow on prices. II. The Mixture Transition Distribution model

2018 ◽  
Vol 18 (6) ◽  
pp. 917-931 ◽  
Author(s):  
Damian Eduardo Taranto ◽  
Giacomo Bormetti ◽  
Jean-Philippe Bouchaud ◽  
Fabrizio Lillo ◽  
Bence Tóth
Keyword(s):  
Linear Models ◽  
Distribution Model ◽  
Order Flow ◽  
Transition Distribution ◽  
The Impact

Linear models for the impact of order flow on prices. I. History dependent impact models

2018 ◽  
Vol 18 (6) ◽  
pp. 903-915 ◽  
Author(s):  
Damian Eduardo Taranto ◽  
Giacomo Bormetti ◽  
Jean-Philippe Bouchaud ◽  
Fabrizio Lillo ◽  
Bence Tóth
Keyword(s):  
Linear Models ◽  
Order Flow ◽  
The Impact ◽  
Impact Models

scholarly journals Linear Models for the Impact of Order Flow on Prices I. Propagators: Transient vs. History Dependent Impact

2016 ◽  
Author(s):  
Damian Eduardo Taranto ◽  
Giacomo Bormetti ◽  
Jean-Philippe Bouchaud ◽  
Fabrizio Lillo ◽  
Bence Toth
Keyword(s):  
Linear Models ◽  
Order Flow ◽  
The Impact

The impact of order flow on event study returns: New evidence from zero-leverage firms

2021 ◽  
Vol 80 ◽  
pp. 627-634
Author(s):  
Sijia Zhang ◽  
Andros Gregoriou
Keyword(s):  
Event Study ◽  
Order Flow ◽  
The Impact ◽  
New Evidence

scholarly journals Socio-economic consequences of mental distress: quantifying the impact of self-reported mental distress on the days of incapacity to work and medical costs in a two-year period: a longitudinal study in Germany

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gerhard Müller ◽  
Manuela Bombana ◽  
Monika Heinzel-Gutenbrenner ◽  
Nikolaus Kleindienst ◽  
Martin Bohus ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Mental Distress ◽  
Linear Models ◽  
Longitudinal Research ◽  
Depression Scale ◽  
Routine Data ◽  
Medical Costs ◽  
First Year ◽  
Second Year ◽  
The Impact

Abstract Background Mental disorders are related to high individual suffering and significant socio-economic burdens. However, it remains unclear to what extent self-reported mental distress is related to individuals’ days of incapacity to work and their medical costs. This study aims to investigate the impact of self-reported mental distress for specific and non-specific days of incapacity to work and specific and non-specific medical costs over a two-year span. Method Within a longitudinal research design, 2287 study participants’ mental distress was assessed using the Hospital Anxiety and Depression Scale (HADS). HADS scores were included as predictors in generalized linear models with a Tweedie distribution with log link function to predict participants’ days of incapacity to work and medical costs retrieved from their health insurance routine data during the following two-year period. Results Current mental distress was found to be significantly related to the number of specific days absent from work and medical costs. Compared to participants classified as no cases by the HADS (2.6 days), severe case participants showed 27.3-times as many specific days of incapacity to work in the first year (72 days) and 10.3-times as many days in the second year (44 days), and resulted in 11.4-times more medical costs in the first year (2272 EUR) and 6.2-times more in the second year (1319 EUR). The relationship of mental distress to non-specific days of incapacity to work and non-specific medical costs was also significant, but mainly driven from specific absent days and specific medical costs. Our results also indicate that the prevalence of presenteeism is considerably high: 42% of individuals continued to go to work despite severe mental distress. Conclusions Our results show that self-reported mental distress, assessed by the HADS, is highly related to the days of incapacity to work and medical costs in the two-year period. Reducing mental distress by improving preventive structures for at-risk populations and increasing access to evidence-based treatments for individuals with mental disorders might, therefore, pay for itself and could help to reduce public costs.

scholarly journals Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5213
Author(s):  
Chin-Shiuh Shieh ◽  
Wan-Wei Lin ◽  
Thanh-Tuan Nguyen ◽  
Chi-Hong Chen ◽  
Mong-Fong Horng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Denial Of Service ◽  
Gaussian Mixture ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Ddos Attacks ◽  
Open Set ◽  
The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

241 Skeletal Muscle Mitochondrial Capacities Are Impacted by Breed and Temperament in Young Angus and Brahman Steers

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 127-127
Author(s):  
Chloey P Guy ◽  
Lauren T Wesolowski ◽  
Audrey L Earnhardt ◽  
Dustin Law ◽  
Don A Neuendorff ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Fixed Effects ◽  
Linear Models ◽  
Citrate Synthase ◽  
Volume Density ◽  
Skeletal Muscle Mitochondria ◽  
Mitochondrial Volume ◽  
Mitochondrial Volume Density ◽  
Brahman Steers ◽  
The Impact

Abstract Temperament impacts skeletal muscle mitochondria in Brahman heifers, but this has not been investigated in steers or between cattle breeds. We hypothesized mitochondrial measures would be greater in Angus than Brahman, temperamental than calm steers, and the trapezius (TRAP) than the longissimus thoracis (LT) muscle. Samples from calm (n = 13 per breed), intermediate (n = 12 per breed), and temperamental (n=13 per breed) Angus and Brahman steers (mean±SD 10.0±0.8 mo) were evaluated for mitochondrial enzyme activities via colorimetry. Calm and temperamental LT samples were evaluated for oxidative phosphorylation (P) and electron transfer (E) capacities by high-resolution respirometry. Data were analyzed using linear models with fixed effects of breed, muscle, temperament, and all interactions. Brahman tended to have greater mitochondrial volume density (citrate synthase activity; CS) than Angus (P = 0.08), while intrinsic (relative to CS) mitochondrial function (cytochrome c oxidase activity) was greater in Angus than Brahman (P = 0.001) and greater in TRAP than LT (P = 0.008). Angus exhibited greater integrative (per mg tissue) and intrinsic P with complex I (PCI), P with complexes I+II (PCI+II), maximum noncoupled E, and E with complex II (ECII; P ≤ 0.04) and tended to have greater intrinsic leak (P = 0.1) than Brahman. Contribution of PCI to total E was greater in Angus than Brahman (P = 0.01), while contribution of ECII to total E was greater in Brahman than Angus (P = 0.05). A trend for the interaction of breed and temperament (P = 0.07) indicated calm Angus had the greatest intrinsic ECII (P ≤ 0.03) while intrinsic ECII was similar between temperamental Angus and calm and temperamental Brahman. Integrative PCI+II and ECII, and the contribution of PCI and PCI+II to overall E tended to be greater in temperamental than calm steers (P ≤ 0.09), while intrinsic ECII tended to be greater in calm than temperamental steers (P = 0.07). The impact of these mitochondrial differences on meat quality measures remains to be determined.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals Impact of the pan-Canadian Oncology Drug Review on provincial concordance with respect to cancer drug funding decisions and time to funding

2017 ◽  
Vol 24 (5) ◽  
pp. 295 ◽  
Author(s):  
A. Srikanthan ◽  
H. Mai ◽  
N. Penner ◽  
E. Amir ◽  
A. Laupacis ◽  
...  
Keyword(s):  
Linear Models ◽  
Drug Product ◽  
Median Number ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Oncology Drug ◽  
Before And After ◽  
Canadian Provinces ◽  
The Impact ◽  
Drug Review

Background The pan-Canadian Oncology Drug Review (pcodr) was implemented in 2011 to address uneven drug coverage and lack of transparency with respect to the various provincial cancer drug review processes in Canada. We evaluated the impact of the pcodr on provincial decision concordance and time from Notice of Compliance (noc) to drug funding.Methods In a retrospective review, Health Canada’s Drug Product Database was used to identify new indications for cancer drugs between January 2003 and May 2014, and provincial formulary listings for drug-funding dates and decisions between 1 January 2003 and 31 December 2014 were retrieved. Multiple linear models and quantile regressions were used to evaluate changes in time to decision-making before and after the implementation of the pcodr. Agreement of decisions between provinces was evaluated using kappa statistics.Results Data were available from 9 provinces (all Canadian provinces except Quebec), identifying 88 indications that represented 51 unique cancer drugs. Two provinces lacked available data for all 88 indications at the time of data collection. Interprovincial concordance in drug funding decisions significantly increased after the pcodr’s implementation (Brennan-Prediger coefficient: 0.54 pre-pcodr vs. 0.78 post-pcodr; p = 0.002). Nationwide, the median number of days from Health Canada’s noc date to the date of funding significantly declined (to 393 days from 522 days, p < 0.001). Exploratory analyses excluding provinces with incomplete data did not change the results.Conclusions After the implementation of the pcodr, greater concordance in cancer drug funding decisions between provinces and decreased time to funding decisions were observed.

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