Model-dependent Estimates Research Articles

The Hubble tension from the standpoint of quantum cosmology

The Hubble tension is analyzed in the framework of quantum cosmological approach. It is found that there arises a new summand in the expression for the total energy density stipulated by the quantum Bohm potential. This additional energy density acts similarly to a stiff matter component, modifying the expansion history of the early universe and decaying with scale factor a as a-6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$a^{-6}$$\\end{document}, faster than radiation, in late universe. Taking account of this matter-energy component of quantum nature can, in principle, eliminate a discrepancy between the direct late time model-independent measurements of the Hubble constant and its indirect model dependent estimates. The considered model allows one to extend the standard cosmology to quantum sector.

Quantifying the role of internal variability in the temperature we expect to observe in the coming decades

On short (15-year) to mid-term (30-year) time-scales how the Earth’s surface temperature evolves can be dominated by internal variability as demonstrated by the global-warming pause or ‘hiatus’. In this study, we use six single model initial-condition large ensembles (SMILEs) and the Coupled Model Intercomparison Project 5 (CMIP5) to visualise the role of internal variability in controlling possible observable surface temperature trends in the short-term and mid-term projections from 2019 onwards. We confirm that in the short-term, surface temperature trend projections are dominated by internal variability, with little influence of structural model differences or warming pathway. Additionally we demonstrate that this result is independent of the model-dependent estimate of the magnitude of internal variability. Indeed, and perhaps counter intuitively, in all models a lack of warming, or even a cooling trend could be observed at all individual points on the globe, even under the largest greenhouse gas emissions. The near-equivalence of all six SMILEs and CMIP5 demonstrates the robustness of this result to the choice of models used. On the mid-term time-scale, we confirm that structural model differences and scenario uncertainties play a larger role in controlling surface temperature trend projections than they did on the shorter time-scale. In addition we show that whether internal variability still dominates, or whether model uncertainties and internal variability are a similar magnitude, depends on the estimate of internal variability, which differs between the SMILEs. Finally we show that even out to thirty years large parts of the globe (or most of the globe in MPI-GE and CMIP5) could still experience no-warming due to internal variability.

The TRENDS High-contrast Imaging Survey. VIII. Compendium of Benchmark Objects

Abstract The physical properties of faint stellar and substellar objects often rely on indirect, model-dependent estimates. For example, the masses of brown dwarfs are usually inferred using evolutionary models, which are age dependent and have yet to be properly calibrated. With the goal of identifying new benchmark objects to test low-mass stellar and substellar models, we have carried out a comprehensive adaptive optics survey as part of the TaRgetting bENchmark-objects with the Doppler Spectroscopy high-contrast imaging program. Using legacy radial velocity measurements from the High Resolution Echelle Spectrometer at Keck, we have identified several dozen stars that show long-term Doppler accelerations. We present follow-up high-contrast observations from the campaign and report the discovery of 31 comoving companions, as well as 11 strong candidate companions, to solar-type stars with well-determined parallax and metallicity values. Benchmark objects of this nature lend themselves to orbit determinations, dynamical mass estimates, and independent compositional assessment. This compendium of benchmark objects will serve as a convenient test group to substantiate theoretical evolutionary and atmospheric models near the hydrogen fusing limit.

Model-based small area estimation with no samples within the areas, by benchmarking to marginal cross-sectional and time-series estimates

ABSTRACT Official monthly U.S. labour force estimation at the sub-State level (mostly counties) is based on what is known as the ‘Handbook’ (HB) method, one of the earliest uses of administrative data for small area estimation. The administrative data, however, are poor in coverage and have conceptual deficiencies. Past attempts to correct for the resulting bias of the HB estimates by informal (implicit) modelling have not been successful, due to the absence of regular direct monthly survey estimates at the sub-State level. Benchmarking the sub-State HB estimates each month to the State model dependent estimates helps to correct for an overall bias, but not in individual areas. In this article we propose benchmarking additionally to the annual model-dependent area estimates. The annual models include known administrative data as covariates, and are used to define corresponding monthly sub-State models, which in turn enable producing monthly synthetic estimates as possible substitutes for the HB estimates in real time production. Variance estimates, which account for sampling errors and the errors of the model dependent estimators are developed. Data for sub-State areas in the State of Arizona are used for illustration. Although the methodology developed in this article stems from a particular (but very important) application, it is general and applicable to other similar problems.

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

Evidence of a Sub-Saturn around EPIC 211945201

Abstract We report here strong evidence for a sub-Saturn around EPIC 211945201 and confirm its planetary nature. EPIC 211945201b was found to be a planetary candidate from K2 photometry in Campaigns 5 and 16, transiting a bright star (V mag = 10.15, G0 spectral type) in a 19.492 day orbit. However, the photometric data combined with false positive probability calculations using VESPA was not sufficient to confirm the planetary scenario. Here, we present high-resolution spectroscopic follow up of the target using the PARAS spectrograph (19 radial velocity observations) over a time baseline of 420 days. We conclusively rule out the possibility of an eclipsing binary system and confirm the 2σ detection of a sub-Saturn planet. The confirmed planet has a radius of 6.12 ± 0.1 R ⊕, and a mass of . We also place an upper limit on the mass (within the 3σ confidence interval) at 42 M ⊕ above the nominal value. This results in the Saturn-like density of . Based on the mass and radius, we provide a preliminary model-dependent estimate that the heavy element content is 60%–70% of the total mass. This detection is important as it adds to a sparse catalog of confirmed exoplanets with masses between 10 and 70 M ⊕ and radii between 4 and 8 R ⊕, whose masses and radii are measured to a precision of 50% or better (only 23 including this work).

K→π matrix elements of the chromomagnetic operator on the lattice

We present the results of the first lattice QCD calculation of the $K \to \pi$ matrix elements of the chromomagnetic operator $O_{CM} = g\, \bar s\, \sigma_{\mu\nu} G_{\mu\nu} d$, which appears in the effective Hamiltonian describing $\Delta S = 1$ transitions in and beyond the Standard Model. Having dimension 5, the chromomagnetic operator is characterized by a rich pattern of mixing with operators of equal and lower dimensionality. The multiplicative renormalization factor as well as the mixing coefficients with the operators of equal dimension have been computed at one loop in perturbation theory. The power divergent coefficients controlling the mixing with operators of lower dimension have been determined non-perturbatively, by imposing suitable subtraction conditions. The numerical simulations have been carried out using the gauge field configurations produced by the European Twisted Mass Collaboration with $N_f = 2+1+1$ dynamical quarks at three values of the lattice spacing. Our result for the B-parameter of the chromomagnetic operator at the physical pion and kaon point is $B_{CMO}^{K \pi} = 0.273 ~ (70)$, while in the SU(3) chiral limit we obtain $B_{CMO} = 0.072 ~ (22)$. Our findings are significantly smaller than the model-dependent estimate $B_{CMO} \sim 1 - 4$, currently used in phenomenological analyses, and improve the uncertainty on this important phenomenological quantity.

Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations.

In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. On the other hand, a statistical test via the HR or its asymptotically equivalent counterpart, the logrank test, is asymptotically distribution-free. In this article, we assess the relative efficiency of the hazard ratio and t-MST tests with respect to the statistical power under various PH and non-PH models theoretically and empirically. When the PH assumption is valid, the t-MST test performs almost as well as the HR test. For non-PH models, the t-MST test can substantially outperform its HR counterpart. On the other hand, the HR test can be powerful when the true difference of two survival functions is quite large at end but not the beginning of the study. Unfortunately, for this case, the HR estimate may not have a simple clinical interpretation for the treatment effect due to the violation of the PH assumption.

In search of the dark matter dark energy interaction: a kinematic approach

The present work deals with a kinematic approach to modelling the late time dynamics of the universe. This approach is based upon the assumption of constant value of cosmological jerk parameter, which is the dimensionless representation of the third order time derivative of the scale factor. For the CDM model, the value of jerk parameter is −1 throughout the evolution history. Now any model dependent estimation of the value of the jerk parameter would indicate the deviation of the model from the cosmological constant. In the present work, it has also been shown that for a constant jerk parameter model, any deviation of its value from −1 would not allow the dark matter to have an independent conservation, thus indicating an interaction between dark matter and dark energy. Statistical analysis with different observational data sets (namely the observational Hubble parameter data (OHD), the type Ia supernova data (SNe), and the baryon acoustic oscillation data (BAO)) lead to well constrained values of the jerk parameter and the model remains at a very close proximity of the CDM. The possibility of interaction is found to be more likely at high redshift rather than at the present epoch.

Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data

As a non-Annex 1 Party to the United Nations Framework Convention on Climate Change (UNFCCC), Tanzania has committed to submit the results of the measurement, reporting, and verification of greenhouse gas (GHG) emissions reductions and removals by sinks under the Biennial Update Reports of national GHG inventory and mitigation actions. Thus, implementation of a robust and cost-efficient carbon accounting system is one of the prerequisites for receiving financial benefits under the REDD+ mechanism. For estimation targeting large areas (i.e., country, region, and sub-regional), remote sensing techniques like airborne laser scanning (ALS) enable precise and timely estimation of biomass stocks and biomass changes. The main objective of the study was to compare alternative sampling strategies using ALS for estimating aboveground biomass (AGB) changes in miombo woodlands that would be suitable components of the National Forestry Resources Monitoring and Assessment (NAFORMA) program of Tanzania and of the Biennial Update Reports under UNFCCC. NAFORMA is the national forest inventory of Tanzania. The study area of 15,867km2 was located in the administrative district of Liwale, south-eastern Tanzania and dominated by miombo woodlands. A longitudinal survey was carried out during two temporally consistent field and ALS data acquisition campaigns in 2012 and 2014. The field data were collected on permanent plots of the NAFORMA program. ALS measurements were acquired along 32 parallel strips spaced 5km apart and covering approximately 25% of the study area. Estimation methods (1) solely based on the ground inventory data provided by NAFORMA and (2) by incorporating field observations and auxiliary variables derived from ALS data were pursued for assessing AGB change as the difference between estimated AGB in 2012 and 2014, together with the uncertainties related to sampling variability. The AGB estimates varied from 58.48 to 59.55Mgha−1 in 2012 and between 58.20 and 59.29Mgha−1 in 2014. The sampling variability (in terms of standard errors) was between 1.37 and 4.79Mgha−1 in 2012, and 1.77 and 5.03Mgha−1 in 2014. The mean AGB change varied from 0.11 to 0.35Mgha−1 with standard errors of 0.31 to 0.85Mgha−1. Compared to direct estimation based on field data only, the efficiency gain under ALS-assisted (design-based, model-assisted) estimation was 5–22%, and approximately 66% when using ALS data in a model-dependent estimation. The results provide statistically significant evidence of change in AGB over the 2-yr period spanned by the study only under model-dependent estimation at 50% confidence level, illustrating the great challenges of change estimation over such short time periods as requested for UNFCCC reporting.

Propensity score and proximity matching using random forest

In order to derive unbiased inference from observational data, matching methods are often applied to produce balanced treatment and control groups in terms of all background variables. Propensity score has been a key component in this research area. However, propensity score based matching methods in the literature have several limitations, such as model mis-specifications, categorical variables with more than two levels, difficulties in handling missing data, and nonlinear relationships. Random forest, averaging outcomes from many decision trees, is nonparametric in nature, straightforward to use, and capable of solving these issues. More importantly, the precision afforded by random forest (Caruana et al., 2008) may provide us with a more accurate and less model dependent estimate of the propensity score. In addition, the proximity matrix, a by-product of the random forest, may naturally serve as a distance measure between observations that can be used in matching. The proposed random forest based matching methods are applied to data from the National Health and Nutrition Examination Survey (NHANES). Our results show that the proposed methods can produce well balanced treatment and control groups. An illustration is also provided that the methods can effectively deal with missing data in covariates.

The inner knot of the Crab nebula

We model the inner knot of the Crab Nebula as a synchrotron emission coming from the non-spherical MHD termination shock of relativistic pulsar wind. The post-shock flow is mildly relativistic; as a result the Doppler-beaming has a strong impact on the shock appearance. The model can reproduce the knot location, size, elongation, brightness distribution, luminosity and polarization provided the effective magnetization of the section of the pulsar wind producing the knot is low, $\sigma \leq 1$. In the striped wind model, this implies that the striped zone is rather wide, with the magnetic inclination angle of the Crab pulsar $\ge 45^\circ$; this agrees with the previous model-dependent estimate based on the gamma-ray emission of the pulsar. We conclude that the tiny knot is indeed a bright spot on the surface of a quasi-stationary magnetic relativistic shock and that this shock is a site of efficient particle acceleration. On the other hand, the deduced low magnetization of the knot plasma implies that this is an unlikely site for the Crab's gamma-ray flares, if they are related to the fast relativistic magnetic reconnection events.

LiDAR-supported estimation of change in forest biomass with time-invariant regression models

A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg·ha−1) in three forest strata in the Våler forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 m2circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR-based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and “permanent” plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors.

Model errors in tree biomass estimates computed with an approximation to a missing covariance matrix

BackgroundBiomass and carbon estimation has become a priority in national and regional forest inventories. Biomass of individual trees is estimated using biomass equations. A covariance matrix for the parameters in a biomass equation is needed for the computation of an estimate of the model error in a tree level estimate of biomass. Unfortunately, many biomass equations do not provide key statistics for a direct estimation of model errors. This study proposes three new procedures for recovering missing statistics from available estimates of a coefficient of determination and sample size. They are complementary to a recently published study using a computationally intensive Monte Carlo approach.ResultsOur recovery approach use survey data from the population targeted for an estimation of tree biomass. Examples from Germany and Mexico illustrate and validate the methods. Applications with biomass estimation and robust recovered fit statistics gave reasonable estimates of model errors in tree level estimates of biomass.ConclusionsIt is good practice to provide estimates of uncertainty to any model-dependent estimate of above ground biomass. When a direct approach to estimate uncertainty is impossible due to missing model statistics, the proposed robust procedure is a first step to good practice. Our recommended approach offers protection against inflated estimates of precision.

Modelling a disease-relevant contact network of people who inject drugs

This study uses social network analysis to model a contact network of people who inject drugs (PWID) relevant for investigating the spread of an infectious disease (hepatitis C). Using snowball sample data, parameters for an exponential random graph model (ERGM) including social circuit dependence and four attributes (location, age, injecting frequency, gender) are estimated using a conditional estimation approach that respects the structure of snowball sample designs. Those network parameter estimates are then used to create a novel, model-dependent estimate of network size. Simulated PWID contact networks are created and compared with Bernoulli graphs. Location, age and injecting frequency are shown to be statistically significant attribute parameters in the ERGM. Simulated ERGM networks are shown to fit the collected data very well across a number of metrics. In comparison with Bernoulli graphs, simulated networks are shown to have longer paths and more clustering. Results from this study make possible simulation of realistic networks for investigating treatment and intervention strategies for reducing hepatitis C prevalence.

Determination of Torsade-Causing Potential of Drug Candidates Using One-Class Classification and Ensemble Modelling Approaches

Adverse drug reactions (ADRs) are a main problem faced by drug companies and regulatory authorities. Not only do they contribute heavily to late-phase failure of drug development and withdrawal of drugs from the market, they also pose significant health risks to patients. Rare and severe ADRs are even harder to detect, and sufficient attention has not been paid to them. Torsade de pointes (TdP), an atypical ventricular tachycardia which is potentially life-threatening, is one of them. The objective of this project is to develop a computational model to predict TdP-causing potential of drug candidates. A total of 260 marketed drugs were collected and screened for their potential to cause TdP. 103 drugs were classified as TdP+ and 157 were likely to be TdP-. One-class classification methods were used to construct multiple base models. A model dependent applicability domain estimation method was used to determine the applicability of the base models for future dataset. A final ensemble model was constructed based on selected base models and it had sensitivity and specificity value of 78.4% and 90% respectively when estimated using external cross validation method. The result suggests that the ensemble model developed in this study is potentially useful for facilitating the prediction of TdP in drug candidates. The ensemble model is made available via the free software, PaDEL-DDPredictor.

Detectability of changes to the Atlantic meridional overturning circulation in the Hadley Centre Climate Models

The Atlantic meridional overturning circulation (MOC) is responsible for a climatically significant northward heat transport that is expected to decrease in response to anthropogenic global warming. Here, simulations from an ensemble of UK Met Office Hadley Centre Climate Models (HadGEM1, HadGEM2 and a 22 member perturbed physics ensemble of HadCM3-like models) are used to evaluate detection times for different MOC observing strategies. Six different detection statistics are compared, including direct observations of the MOC at two latitudes (26°N and 50°N) and several multivariate detection variables based on an optimal fingerprint of MOC change previously identified using HadCM3 (Vellinga and Wood in Geophys Res Lett 31(14):L14203, 2004). Using these models, and assuming perfectly observed conditions, we find no evidence to suggest that detection times would be significantly reduced by measuring the MOC at 50°N instead of (or in addition to) measurements at 26°N. Our results suggest that complementary observations of hydrographic properties in the North Atlantic may help reduce MOC detection times, but the benefits are not universal across models, nor as large as previously suggested. In addition, detection times calculated using optimal fingerprint methods are sensitive to the model-dependent estimates of covariances describing internal climate variability. This last result presents a strong case for deriving fingerprints of MOC change using dynamical/physical arguments, rather than statistical methods, in order to promote more robust results across a range of models.

SOUNDING OPEN CLUSTERS: ASTEROSEISMIC CONSTRAINTS FROM KEPLER ON THE PROPERTIES OF NGC 6791 AND NGC 6819

We present initial results on some of the properties of open clusters NGC 6791 and NGC 6819 derived from asteroseismic data obtained by NASA's Kepler mission. In addition to estimating the mass, radius, and log g of stars on the red giant branch (RGB) of these clusters, we estimate the distance to the clusters and their ages. Our model-independent estimate of the distance modulus of NGC 6791 is (m − M)0 = 13.11 ± 0.06. We find (m − M)0 = 11.85 ± 0.05 for NGC 6819. The average mass of stars on the RGB of NGC 6791 is 1.20 ± 0.01 M☉, while that of NGC 6819 is 1.68 ± 0.03 M☉. It should be noted that we do not have data that cover the entire RGB and the actual mass will be somewhat lower. We have determined model-dependent estimates of ages of these clusters. We find ages between 6.8 and 8.6 Gyr for NGC 6791, however, most sets of models give ages around 7 Gyr. We obtain ages between 2 and 2.4 Gyr for NGC 6819.

Isoeffect calculations with the linear quadratic and its extensions: An examination of model-dependent estimates at doses relevant to hypofractionation

The linear quadratic is the standard model for calculating isoeffects in the range of conventional dose per fraction. However, the use of hypofractionation and stereotactic body radiation therapy can call for isoeffect calculations for large doses per fraction. The purpose of this work is to investigate the linear quadratic at large doses per fraction. The linear quadratic is compared to models that incorporate effects such as dose protraction, whose purpose is to extend the useful range of the linear quadratic to larger doses. The linear quadratic and extended linear quadratic models are fit to 4 data sets. The model-predicted isoeffects for these data sets are calculated. It is found that the linear quadratic and extended linear quadratic predict different isoeffect curves for certain data sets. However, for these data sets, by appropriate selection of a α/β ratio, the linear quadratic can well approximate the extended linear quadratic models. In particular, it is found that a α/β ratio of 0.5 well approximates the extended linear quadratic isoeffect curve for 2 prostate cell lines for conventional and moderate doses per fraction.

Improved radial velocity orbit of the young binary brown dwarf candidate Cha Hα8

The very young brown dwarf candidate ChaHa8 was recently discovered to have a close (~1AU) companion by means of radial velocity monitoring. We present additional radial velocity data obtained with UVES/VLT between 2007 and 2010, which significantly improve the orbit of the system. The combined data set spans ten years of radial velocity monitoring for ChaHa8. A Kepler fit to the data yields an orbital period of 5.2 yrs, an eccentricity of 0.59, and a radial velocity semi-amplitude of 2.4 km/s. A companion mass M2sini (which is a lower limit due to the unknown orbital inclination) of 25 Mjup and of 31 Mjup is derived when using model-dependent mass estimates for the primary of 0.07 and 0.10 Msun, resp. Assuming random orientation of orbits in space, we find a very high probability that the companion of Chaha8 is of substellar nature: With a greater than 87% probability, the companion mass is between 30 and 69 Mjup and the mass ratio < 0.7. The absence of any evidence of the companion in the cross-correlation function together with the size of the radial velocity amplitude also indicate a mass ratio of at most 0.7, and likely smaller. Furthermore, the new data exclude the possibility that the companion has a mass in the planetary regime (<13 Mjup). We show that the companion contributes significantly to the total luminosity of the system: model-dependent estimates provide a minimum luminosity ratio L2/L1 of 0.2. ChaHa8 is the 4th known spectroscopic brown dwarf or very low-mass stellar binary with determined orbital parameters, and the 2nd known very young one. With an age of only ~3 Myr it is of particular interest to very low-mass formation and evolution theories. In contrast to most other spectroscopic binaries, it has a relatively long period and it might be possible to determine the astrometric orbit of the primary and, thus, the orbital inclination.