Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets

This paper poses a few fundamental questions regarding the attributes of the volume profile of a Limit Order Books stochastic structure by taking into consideration aspects of intraday and interday statistical features, the impact of different exchange features and the impact of market participants in different asset sectors. This paper aims to address the following questions: 1. Is there statistical evidence that heavy-tailed sub-exponential volume profiles occur at different levels of the Limit Order Book on the bid and ask and if so does this happen on intra or interday time scales ? 2.In futures exchanges, are heavy tail features exchange (CBOT, CME, EUREX, SGX and COMEX) or asset class (government bonds, equities and precious metals) dependent and do they happen on ultra-high (<1sec) or mid-range (1sec -10min) high frequency data? 3.Does the presence of stochastic heavy-tailed volume profile features evolve in a manner that would inform or be indicative of market participant behaviors, such as high frequency algorithmic trading, quote stuffing and price discovery intra-daily? 4. Is there statistical evidence for a need to consider dynamic behavior of the parameters of models for Limit Order Book volume profiles on an intra-daily time scale ? Progress on aspects of each question is obtained via statistically rigorous results to verify the empirical findings for an unprecedentedly large set of futures market LOB data. The data comprises several exchanges, several futures asset classes and all trading days of 2010, using market depth (Type II) order book data to 5 levels on the bid and ask

The glarma Package for Observation-Driven Time Series Regression of Counts

We review the theory and application of generalized linear autoregressive moving average observation-driven models for time series of counts with explanatory variables and describe the estimation of these models using the R package glarma. Forecasting, diagnostic and graphical methods are also illustrated by several examples

Spatial temporal modeling of hospitalizations for fall-related hip fractures in older people

Summary: The study determined the spatial temporal characteristics of fall-related hip fractures in the elderly using routinely collected injury hospitalization and sociodemographic data. There was significant spatial temporal variation in hospitalized hip fracture rates in New South Wales, Australia. Introduction: The study determined the spatial temporal characteristics of fall-related hip fractures in the elderly using routinely collected injury hospitalization data. Methods: All New South Wales (NSW), Australia residents aged 65+ years who were hospitalized for a fall-related hip fracture between 1 July 1998 and 30 June 2004 were included. Bayesian Poisson regression was used to model rates in local government areas (LGAs), allowing for the incorporation of spatial, temporal, and covariate effects. Results: Hip fracture rates were significantly decreasing in one LGA, and there were no significant increases in any LGAs. The proportion of the population in residential aged care facilities was significantly associated with the rate of hospitalized hip fractures with a relative risk (RR) of 1.003 (95% credible interval 1.002, 1.004). Socioeconomic status was also related to hospitalized hip fractures with those in the third and fourth quintiles being at decreased risk of hip fracture compared to those in the least disadvantaged (fifth) quintile [RR = 0.837 (0.717, 0.972) and RR = 0.855 (0.743, 0.989) respectively]. Conclusions: There was significant spatial temporal variation in hospitalized hip fracture rates in NSW, Australia. The use of Bayesian methods was crucial to allow for spatial correlation, covariate effects, and LGA boundary changes. © 2009 International Osteoporosis Foundation and National Osteoporosis Foundation

Assessment of neonatal respiratory rate variability

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8–18.9%) to 28.1% (IQR 23.5–36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were − 0.5 (− 2.7, 1.66), − 3.16 (− 12.12, 5.8), and − 3.99 (− 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing

Evaluation of Sibel’s Advanced Neonatal Epidermal (ANNE) wireless continuous physiological monitor in Nairobi, Kenya

Background: Neonatal multiparameter continuous physiological monitoring (MCPM) technologies assist with early detection of preventable and treatable causes of neonatal mortality. Evaluating accuracy of novel MCPM technologies is critical for their appropriate use and adoption. Methods: We prospectively compared the accuracy of Sibel’s Advanced Neonatal Epidermal (ANNE) technology with Masimo’s Rad-97 pulse CO-oximeter with capnography and Spengler’s Tempo Easy reference technologies during four evaluation rounds. We compared accuracy of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and skin temperature using Bland-Altman plots and root-mean-square deviation analyses (RMSD). Sibel’s ANNE algorithms were optimized between each round. We created Clarke error grids with zones of 20% to aid with clinical interpretation of HR and RR results. Results: Between November 2019 and August 2020 we collected 320 hours of data from 84 neonates. In the final round, Sibel’s ANNE technology demonstrated a normalized bias of 0% for HR and 3.1% for RR, and a non-normalized bias of -0.3% for SpO2 and 0.2°C for temperature. The normalized spread between 95% upper and lower limits-of-agreement (LOA) was 4.7% for HR and 29.3% for RR. RMSD for SpO2 was 1.9% and 1.5°C for temperature. Agreement between Sibel’s ANNE technology and the reference technologies met the a priori-defined thresholds for 95% spread of LOA and RMSD. Clarke error grids showed that all HR and RR observations were within a 20% difference. Conclusion: Our findings suggest acceptable agreement between Sibel’s ANNE and reference technologies. Clinical effectiveness, feasibility, usability, acceptability, and cost-effectiveness investigations are necessary for large-scale implementation

Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya

Background: Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings. Methods: In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation. Results: Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference. Conclusion: Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates

Clinical feasibility of a contactless multiparameter continuous monitoring technology for neonates in a large public maternity hospital in Nairobi, Kenya

Multiparameter continuous physiological monitoring (MCPM) technologies are critical in the clinical management of high-risk neonates; yet, these technologies are frequently unavailable in many African healthcare facilities. We conducted a prospective clinical feasibility study of EarlySense’s novel under-mattress MCPM technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of EarlySense’s technology to Masimo’s Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR) and respiratory rate (RR) measurements using up-time, clinical event detection performance, and accuracy. Between September 15 and December 15, 2020, we collected and analyzed 470 hours of EarlySense data from 109 enrolled neonates. EarlySense’s technology’s up-time per neonate was 2.9 (range 0.8, 5.3) hours for HR and 2.1 (range 0.9, 4.0) hours for RR. The difference compared to the reference was a median of 0.6 (range 0.1, 3.1) hours for HR and 0.8 (range 0.1, 2.9) hours for RR. EarlySense’s technology identified high HR and RR events with high sensitivity (HR 81%; RR 83%) and specificity (HR 99%; RR 83%), but was less sensitive for low HR and RR (HR 0%; RR 14%) although maintained specificity (HR 100%; RR 95%). There was a greater number of false negative and false positive RR events than false negative and false positive HR events. The normalized spread of limits of agreement was 9.6% for HR and 28.6% for RR, which met the a priori-identified limit of 30%. EarlySense’s MCPM technology was clinically feasible as demonstrated by high percentage of up-time, strong clinical event detection performance, and agreement of HR and RR measurements compared to the reference technology. Studies in critically ill neonates, assessing barriers and facilitators to adoption, and costing analyses will be key to the technology’s development and potential uptake and scale-up

Syndromic Surveillance for Influenzalike Illness in Ambulatory Care Setting

Detection algorithm using proxy data for a bioterrorism agent release and historical data for influenza was effective

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases