Statistical Proxy Research Articles

Statistical proxy based mean‐reverting portfolios with sparsity and volatility constraints

AbstractMean‐reverting portfolios with volatility and sparsity constraints are of prime interest to practitioners in finance since they are both profitable and well‐diversified, while also managing risk and minimizing transaction costs. Three main measures that serve as statistical proxies to capture the mean‐reversion property are predictability, portmanteau criterion, and crossing statistics. If in addition, reasonable volatility and sparsity for the portfolio are desired, a convex quadratic or quartic objective function, subject to nonconvex quadratic and cardinality constraints needs to be minimized. In this paper, we introduce and investigate a comprehensive modeling framework that incorporates all the previous proxies proposed in the literature and develop an effective unifying algorithm that is enabled to obtain a Karush–Kuhn–Tucker (KKT) point under mild regularity conditions. Specifically, we present a tailored penalty decomposition method that approximately solves a sequence of penalized subproblems by a block coordinate descent algorithm. To the best of our knowledge, our proposed algorithm is the first method for directly solving volatile, sparse, and mean‐reverting portfolio problems based on the portmanteau criterion and crossing statistics proxies. Further, we establish that the convergence analysis can be extended to a nonconvex objective function case if the starting penalty parameter is larger than a finite bound and the objective function has a bounded level set. Numerical experiments on the S&P 500 data set demonstrate the efficiency of the proposed algorithm in comparison to a semidefinite relaxation‐based approach and suggest that the crossing statistics proxy yields more desirable portfolios.

Constraining the carbon cycle in JULES-ES-1.0

Abstract. Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply uncertainty quantification (UQ) techniques to constrain the input parameter space and corresponding historical simulations of JULES-ES-1.0 (Joint UK Land Environment Simulator Earth System), the land surface component of the UK Earth System Model, UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out parts of the parameter space where the simulator has not yet been run. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We successfully rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. The result is a set of historical simulations and a constrained input space that are statistically consistent with observations. Furthermore, we use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0 and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases.

Beyond Factors: IGSCA-SEM’s Application in The Context of Cannabis Tourism

Structural Equation Modeling (SEM) is a statistical technique that is used to model the relationships among hypothetical constructs investigated by researchers. SEM can be broadly classified into two main approaches: factor-based (or covariance-based) SEM and component-based (or variance-based) SEM. Factor-based SEM is particularly well-suited for analyzing constructs that resemble factors, while component-based SEM is designed for composites or components. Historically, in the field of tourism research, there has been a tendency to treat factor models as a statistical proxy for all types of hypothetical constructs. However, when the hypothetical construct is incorrectly modeled as a factor instead of a composite, which is its appropriate representation, it can result in bias in parameter estimates. The information presented in this study highlights that this practice has persisted even in top-tier tourism journals, including articles published in the ABAC journal. Contemporary practices that align with the current research landscape in tourism are synthesized. These practices acknowledge that hypothetical constructs can either be factors or components. To illustrate this, a hypothetical example related to cannabis tourism is used, modelling it using mixed constructs based on IGSCA-SEM. Researchers are consequently encouraged to employ SEM, particularly when aiming to publish in the ABAC journal, to enhance their methodological rigor by adopting the recommended practices outlined.

Uncertainty quantification of reference-based cellular deconvolution algorithms

ABSTRACT The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

Using a process-based dendroclimatic proxy system model in a data assimilation framework: a test case in the Southern Hemisphere over the past centuries

Abstract. Currently available data-assimilation-based reconstructions of past climate variations have only used statistical proxy system models to make the link between climate model outputs and indirect observations from tree rings. However, the linearity and stationarity assumptions of the statistical approach may have limitations. In this study, we incorporate the process-based dendroclimatic model MAIDEN into a data assimilation procedure using the reconstruction of near-surface air temperature, precipitation and winds in the midlatitudes of the Southern Hemisphere over the past 400 years as a test case. We compare our results with a data assimilation approach including a linear regression as a proxy system model for tree-ring width proxies. Overall, when compared to instrumental data, the reconstructions using MAIDEN as a proxy system model offer a skill equivalent to the experiment using the regression model. However, knowing the advantages that a process-based model can bring and the improvements that can still be made with MAIDEN, those results are promising.

An evaluation of geo-located Twitter data for measuring human migration

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Dimensions and Correlates of Development in Construction

In light of the limited impact research on construction in developing countries has had on theory and practice, this article discusses the meaning of development as it pertains to construction. The discussion distinguishes between studies addressing construction in developing countries, entailing focus on context and case-based approaches to research, and studies that focus on the process of construction industry development. Progress in research addressing the process of development has been hampered by a lack of data to support the examination of changes in construction industries over time and to compare industries in countries at different stages of economic development. To encourage more process-oriented research, a definition of development in construction is elaborated to include six important dimensions – i.e., important factors affecting construction industry development. Taking into account the lack of relevant statistics, correlates for these dimensions (i.e., statistical proxies) are reviewed. Finally, a manner of graphically presenting these dimensions and correlates is introduced. This graphic format enables visual comparison of conditions affecting construction in different countries. It also provides a frame for relating separate case studies to support comparisons and contributions to theory.

Mahalanobis distance, a novel statistical proxy of homeostasis loss is longitudinally associated with risk of type 2 diabetes

BackgroundThe potential role of individual plasma biomarkers in the pathogenesis of type 2 diabetes (T2D) has been broadly studied, but the impact of biomarkers interaction remains underexplored. Recently, the Mahalanobis distance (MD) of plasma biomarkers has been proposed as a proxy of physiological dysregulation. Here we aimed to investigate whether the MD calculated from circulating biomarkers is prospectively associated with development of T2D.MethodsWe calculated the MD of the Principal Components (PCs) integrating the information of 32 circulating biomarkers (comprising inflammation, glycemic, lipid, microbiome and one-carbon metabolism) measured in 6247 participants of the PREVEND study without T2D at baseline. Cox proportional-hazards regression analyses were performed to study the association of MD with T2D development.FindingsAfter a median follow-up of 7·3 years, 312 subjects developed T2D. The overall MD (mean (SD)) was higher in subjects who developed T2D compared to those who did not: 35·65 (26·67) and 30.75 (27·57), respectively (P = 0·002). The highest hazard ratio (HR) was obtained using the MD calculated from the first 31 PCs (per 1 log-unit increment) (1·72 (95% CI 1·42,2·07), P < 0·001). Such associations remained after the adjustment for age, sex, plasma glucose, parental history of T2D, lipids, blood pressure medication, and BMI (HRadj 1·37 (95% CI 1·11,1·70), P = 0·004).InterpretationOur results are in line with the premise that MD represents an estimate of homeostasis loss. This study suggests that MD is able to provide information about physiological dysregulation also in the pathogenesis of T2D.FundingThe Dutch Kidney Foundation (Grant E.033).

Vertical assessment of the mineral dust optical and microphysical properties as retrieved from the synergy between polarized micro-pulse lidar and sun/sky photometer observations using GRASP code

The potential of the synergetic use of both polarized Micro-Pulse Lidar (P-MPL) and Sun/sky photometer observations by using the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) code for retrieving dust properties has been analysed in this work. Both the columnar and height-resolved variables have been evaluated in comparison, respectively, with AERONET data and vertical P-MPL-derived profiles of the dust optical and microphysical properties over a mid-latitude site. The linear regression analysis together with the mean fractional bias values and relative differences between datasets have been used as the statistical proxies for assessing the degree of agreement by examining 24 selected dust cases. Regarding columnar variables, the microphysical properties (both the particle volume concentration and effective radius) presented a high confidence level between GRASP and AERONET retrievals by taking into account all the proxies. In addition, the single scattering albedo and the real refractive index presented a good confidence level at all the wavelengths, as well, unlike the imaginary refractive index, which was confident at 440 nm only. By analysing the height-resolved properties of the total particle backscatter coefficient and the total volume concentration, the degree of agreement shows a high confidence between GRASP retrievals and P-MPL-derived variables, which were retrieved by using an alternative, well-validated, polarization-based methodology. Indeed, these results can be essential for application with no-polarized systems (e.g., ceilometers, lidars …). Overall, the synergy between P-MPL and Sun/sky photometer measurements regarding the usual co-location of both MPLNET and AERONET networks in combination with GRASP code represent an improved approach to derive the height-resolved optical and microphysical properties for mineral dust particles with a high degree of confidence.

On possible proxies of AGN light-curves cadence selection in future time domain surveys

ABSTRACT Motivated by upcoming photometric and spectroscopic surveys [Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic Explorer], we design the statistical proxies to measure the cadence effects on active galactic nuclei (AGNs) variability-observables [time-lags, periodicity, and structure-function (SF)]. We constructed a multiple-regression model to statistically identify the cadence-formal error pattern knowing AGN time-lags and periodicity from different surveys. We defined the simple metric for the SF’s properties, accounting for the ‘observed’ SF’s deviation relative to those obtained from the homogenously sampled light curves. We tested the regression models on different observing strategies: the optical data set of long light curves of eight AGN with peculiarities and the artificial data sets based on several idealized and LSST-like cadences. The SFs metric is assessed on synthetic data sets. The regression models (for both data types) predict similar cadences for time-lags and oscillation detection, whereas for light curves with low variability (${\sim}10{{\ \rm per\ cent}}$), cadences for oscillation detection differ. For higher variability (${\sim}20{{\ \rm per\ cent}}$), predicted cadences are larger than for $F_{var}\sim 10{{\ \rm per\ cent}}$. The predicted cadences are decreasing with redshift. SFs with dense and homogenous cadences are more likely to behave similarly. SFs with oscillatory signals are sensitive to the cadences, possibly impacting LSST-like operation strategy. The proposed proxies can help to select spectroscopic and photometric-surveys cadence strategies, and they will be tested further in larger samples of objects.

FUNCTIONAL IN SILICO ANALYSIS OF GENETIC VARIANTS FROM GENOME-WIDE ASSOCIATION STUDIES OF BLOOD PRESSURE AND HYPERTENSION

Objective: Hypertension is a complex disease with a strong genetic component. Genome wide association studies (GWAS) have identified hundreds of variants associated with hypertension and its defining traits [systolic blood pressure(SBP), diastolic blood pressure(DBP), pulse pressure(PP)]. Our goal was to catalogue all sentinel variants identified by GWAS of hypertension and investigate their functional effects on transcription/translation in silico. Design and method: We compiled a list of all common (minor allele frequency &gt;0.05) sentinel variants associated with SBP, DBP and PP in at least one GWAS at genome-wide level of statistical significance (P &lt; 5x10-8). In silico analyses were conducted to characterise putative biological consequences of these variants at mRNA/protein level. All sentinel variants (and their statistical proxies) were characterised using Ensembl variant effect predictor (VEP) GRCh37 release 90 to identify effects on coding and non-coding exons. Data from the University of California Santa Cruz (UCSC) CpG islands was used to map variants to regulatory regions of open chromatin. Combined Annotation Dependent Depletion (CADD) scores quantified the overall genetic deleteriousness of variants. Results: A total of 892 variants were significantly associated with at least one BP-defining trait in GWAS. The effects of each allele copy of these variants on blood pressure ranged from 0.15–1.58mmHg (for SBP) and 0.09–0.81mmHg (for DBP). A total of 599 (67%) variants received at least one functional annotation. Only 92 (10.3%) variants led to missense changes in encoded proteins. A significant number of variants directly mapped to regulatory regions of active chromatin such as active enhancers and transcription start sites [258 (28.9%) and 225 (25.2%), respectively]. Only three variants [rs16859180 in the gene Serine/Threonine Kinase 36(STK36), rs328 in lipoprotein lipase(LPL) and rs11556924 in zinc finger C3HC-type protein 1(ZC3HC1)] had CADD scores &gt;30 (placing them in the top 0.1% of most deleterious variants in the human genome). Conclusions: The majority of BP GWAS variants do not alter the amino-acid content of encoded proteins. A significant proportion are located within regions of regulatory importance to transcription, suggesting that many BP GWAS variants operate through quantitative effect on expression of key genes in specific cells and tissues.

Proxy modelling for rapid optimisation of miscible CO&lt;SUB align="right"&gt;2-WAG injection in heterogeneous clastic reservoirs: a case study from Southern Iraqi oil fields

Multiple simulation runs are required to optimise and quantify uncertainty and sensitivity analysis to determine and rank the impact of reservoir and operating parameters on the incremental oil recovery (ΔRF) under miscible CO2-WAG flooding. The central composite design (CCD) was employed to create a statistical proxy model for ΔRF as a function of seven reservoir and operating parameters. Statistical analysis was employed to check the model adequacy. The importance and effect of variables that result to higher predicted ΔRF were investigated. As an analogue of the Nahr Umr reservoir, the reservoir understudy, the proxy model can be roughly applied to predict the ΔRF during the miscible CO2-WAG injection at depending on uncertainty. The reliability of the proxy model has been demonstrated based on many statistical tools. [Received: 29 July 2019; Accepted: 24 November 2019]

Proxy modelling for rapid optimisation of miscible CO&lt;SUB align="right"&gt;2-WAG injection in heterogeneous clastic reservoirs: a case study from Southern Iraqi oil fields

Multiple simulation runs are required to optimise and quantify uncertainty and sensitivity analysis to determine and rank the impact of reservoir and operating parameters on the incremental oil recovery (ΔRF) under miscible CO2-WAG flooding. The central composite design (CCD) was employed to create a statistical proxy model for ΔRF as a function of seven reservoir and operating parameters. Statistical analysis was employed to check the model adequacy. The importance and effect of variables that result to higher predicted ΔRF were investigated. As an analogue of the Nahr Umr reservoir, the reservoir understudy, the proxy model can be roughly applied to predict the ΔRF during the miscible CO2-WAG injection at depending on uncertainty. The reliability of the proxy model has been demonstrated based on many statistical tools. [Received: 29 July 2019; Accepted: 24 November 2019]

Surrogate-Based Prediction and Optimization of Multilateral Inflow Control Valve Flow Performance with Production Data

Summary Smart completions enable physical measurements over space and time, which provides large volumes of information at unprecedented rates. However, optimizing inflow control valve (ICV) settings of smart multilateral wells is a challenging task. Traditionally, ICV field tests, evaluating well performance at different ICV settings, are conducted to observe flow behavior and configure ICVs; however, this is often suboptimal. This study investigated a surrogate-based optimization algorithm that minimizes the number of ICV field tests required, predicts well performance of all unseen combination of ICV settings, and determines the optimal ICV setting and net present value (NPV). A numerical model of a real offshore field in Saudi Arabia was used to generate scenarios involving a two-phase (oil and water) reservoir with trilateral producers. Multiple scenarios were examined with variations in design parameters, mainly well count, placement, and configuration. Eight discrete settings were assumed to match the commonly installed ICV technology, where all possible scenarios were simulated to establish ground truth. The investigation considered three major algorithmic components: sampling, machine learning, and optimization. The sampling strategy compared physics-based initialization, space-filling sampling, and triangulation-based adaptive sampling. A cross-validated neural network was used to fit a surrogate (in this case, machine learning algorithm) dynamically, whereas enumeration was adopted for optimization to avoid errors arising from using common optimizers. This study evaluated two sampling techniques: space-filling and adaptive sampling. The latter was found superior in capturing reservoir behavior with the smallest number of simulation runs (i.e., ICV field tests). Algorithm performance was evaluated based on the number of ICV field tests required to exceed an R2 threshold of 90% on all unseen scenarios and match the optimal ICV settings and NPV. Surface and downhole flow profile prediction and optimization were achieved successfully using this approach. To determine the diminishing value of additional ICV field tests, the triangulation sampling loss was used as a stoppage criterion. When running the algorithm on a single producer for both surface and downhole oil and water flow prediction, the algorithm required only 6 and 11 ICV field tests to achieve 80% and 90% R2 across the different cases of this real reservoir model. Fishbone wellbore configurations were found to pose a more challenging task because changes in any ICV pressure decrease affects multiple laterals simultaneously, which increases the level of interdependence. The resultant surrogate was used to decide on the optimal settings of ICV devices and effectively predict the NPV. Surrogates, in this approach, are statistical proxies of the targeted ground-truth production function. Further improvement was accomplished through adaptively sampling and fitting surrogates to predict NPV explicitly, where NPV predictions were generated with nearly 95% R2 given only 10 ICV field tests. Using adaptive sampling and machine learning proved effective in the prediction of surface and downhole flow profiles and optimization of smart wells. The method further allows for dynamically optimizing field strategy in a reinforcement learning setting where production data are used continuously to further improve the prediction performance.

A Hybrid Optimization Methodology Identifying Optimal Operating Conditions for Carbon Dioxide Injection in Geologic Carbon Sequestration

Prior to determining the optimal operating parameters for CO2 injection, conditions for both injection wellbore and storage formation should be evaluated; the build-up pressure induced by the CO2 injection could promote fractures in the storage formation, even collapsing the wellbore. In this study, a hybrid optimization methodology, which combined the proxy modeling and multi-objective optimization, was engaged in searching appropriate operating conditions for CO2 injection. The study utilized a fully coupled wellbore-reservoir (WR) model to simulate the CO2 injection scenarios. Three responses, such as pressure, temperature, and CO2 mass flow rate at the bottom-hole of injection wellbore, were investigated. To reduce the computational cost, the statistical proxy models were developed for approximating three responses. The developed fine-tuned proxy models revealed four influential factors; wellhead pressure, injected CO2 temperature, wellbore diameter, and permeability of a storage formation were significant in predicting three responses. Among these four influential factors, permeability was treated to be an uncertainty factor, while the other three factors were treated as tuning factors. According to acquired optimal solution sets, the optimum values for wellhead pressure and injected CO2 temperature were distributed around 10.0 MPa and 35 °C, respectively. For the wellbore diameter, its mean of optimal solutions was 0.1 m, and more solutions were concentrated at this mean value with a decrease in permeability.

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.

Statistical proxy modeling for life cycle assessment and energetic analysis

Bottom-up life-cycle assessment (LCA) based on engineering-based models is emerging as a way to model the energy consumption and greenhouse gas emissions from important segments of the energy sector. However, a major challenge that has hindered further application of this approach is data and computationally intensive thermodynamic modeling. In order to address this issue, we introduce a general data-driven framework to develop statistical reduced-order models (henceforth “proxy models”) from advanced thermodynamic or engineering simulations that can be utilized for bottom-up LCA and/or energetic assessment purposes of any energy system. To demonstrate the performance of the proposed framework, we simulate four important oil and gas process units with a commercial process simulation package. Using a combination of deterministic and random sampling strategy, >25,000 simulations are performed and quadratic proxy models are trained on the results to predict the energy consumption and product compositions across a wide ranges of independent variables. The simple proxy models have excellent predictive accuracy (R2 > 0.95 in most cases, R2 = 1 for pump power consumption prediction), solve nearly instantaneously, and require fewer input parameters (less than 10, varies based on the process unit). We also examine and prove the proposed methodology stability across training and validation datasets via 1,000 independent data splitting runs. We lastly implement the oil and gas proxy models in a LCA simulator, compare the results with textbook correlations, and demonstrate the improved flexibility.

Allele-specific enhancers mediate associations between LCAT and ABCA1 polymorphisms and HDL metabolism.

For most complex traits, the majority of SNPs identified through genome-wide association studies (GWAS) reside within noncoding regions that have no known function. However, these regions are enriched for the regulatory enhancers specific to the cells relevant to the specific trait. Indeed, many of the GWAS loci that have been functionally characterized lie within enhancers that regulate expression levels of key genes. In order to identify polymorphisms with potential allele-specific regulatory effects, we developed a bioinformatics pipeline that harnesses epigenetic signatures as well as transcription factor (TF) binding motifs to identify putative enhancers containing a SNP with potential allele-specific TF binding in linkage disequilibrium (LD) with a GWAS-identified SNP. We applied the approach to GWAS findings for blood lipids, revealing 7 putative enhancers harboring associated SNPs, 3 of which lie within the introns of LCAT and ABCA1, genes that play crucial roles in cholesterol biogenesis and lipoprotein metabolism. All 3 enhancers demonstrated allele-specific in vitro regulatory activity in liver-derived cell lines. We demonstrated that these putative enhancers are in close physical proximity to the promoters of their respective genes, in situ, likely through chromatin looping. In addition, the associated alleles altered the likelihood of transcription activator STAT3 binding. Our results demonstrate that through our approach, the LD blocks that contain GWAS signals, often hundreds of kilobases in size with multiple SNPs serving as statistical proxies to the true functional site, can provide an experimentally testable hypothesis for the underlying regulatory mechanism linking genetic variants to complex traits.

Analysis of telecom service operation behavior with time series

Operation of complex telecom services is a field that mixes technology, processes and teams. Despite the existence of detailed protocols and automation, the real behavior is hard to measure and predict. The human factor is a source of uncertainty, and this fact is of special relevance when facing stressful situations. Informal team working culture, time shifts or external stress are main sources of change. In this research we use time series analysis as a statistical proxy to detect this kind of drift in teams that solve network failures if three live services: IPTV, Cloud Infrastructure and IoT. This task known as incident management. This would provide not only a numerical evidence of the uncertainty in troubleshooting of digital services but also an assessment about the economic and operational impact of service releases. Changes in best fitting models may reflect different informal work cultures among the operation teams.

Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data

Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way.In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon (222Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms “learn” from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques.By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values.Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictors, while “day of the year” is a statistical proxy or surrogate for missing or unknown predictors.