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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS2019

Analytic Spacecraft Attitude and Rate Estimation Performance During Attitude Sensor Outages

Analytic expressions for spacecraft attitude and rate estimation performance of an attitude estimation filter in terms of sensor specifications are useful tools for spacecraft design. Farrenkopf (1978) famously found analytic expressions for steady-state pre-update and post-update attitude and gyro bias estimate error variances for an attitude estimation filter for a single-axis spacecraft with a Rate Output Gyro (ROG). Markley and Reynolds (2000) extended the analysis for a Rate-Integrating Gyro (RIG) with angle white noise. These expressions allow for the rapid evaluation of system performance during preliminary mission design phases. One contribution of this paper is the analytic calculation of the steady-state pre-update and post-update angular rate estimate uncertainty for both the ROG and RIG cases. The primary contribution of this paper is the extension of the results for both the ROG and the RIG cases to the situation of an attitude sensor outage. This situation arises frequently in practice; for example when a star sensor’s field of view is occluded, when a star sensor’s readings are unreliable during a thruster burn that vibrates the spacecraft, or during star sensor outages due to radiation upsets. Analytic expressions for the attitude estimate uncertainty, gyro bias estimate uncertainty, and angular rate estimate uncertainty are given in terms of the attitude sensor outage interval, the star tracker measurement noise, and gyro noise parameters. Validity of the analytic results is demonstrated via Monte Carlo simulation.

Galante, Joseph M.↗
NASA NTRS2019

Analytic Spacecraft Attitude and Rate Estimation Performance During Attitude Sensor Outages

Analytic expressions for spacecraft attitude and rate estimation performance of an attitude estimation filter in terms of sensor specifications are useful tools for spacecraft design. Farrenkopf (1978) famously found analytic expressions for steady-state pre-update and post-update attitude and gyro bias estimate error variances for an attitude estimation filter for a single-axis spacecraft with a Rate Output Gyro (ROG). Markley and Reynolds (2000) extended the analysis for a Rate-Integrating Gyro (RIG) with angle white noise. These expressions allow for the rapid evaluation of system performance during preliminary mission design phases. One contribution of this paper is the analytic calculation of the steady-state pre-update and post-update angular rate estimate uncertainty for both the ROG and RIG cases. The primary contribution of this paper is the extension of the results for both the ROG and the RIG cases to the situation of an attitude sensor outage. This situation arises frequently in practice; for example when a star sensor’s field of view is occluded, when a star sensor’s readings are unreliable during a thruster burn that vibrates the spacecraft, or during star sensor outages due to radiation upsets. Analytic expressions for the attitude estimate uncertainty, gyro bias estimate uncertainty, and angular rate estimate uncertainty are given in terms of the attitude sensor outage interval, the star tracker measurement noise, and gyro noise parameters. Validity of the analytic results is demonstrated via Monte Carlo simulation.

Galante, Joseph M.↗
NASA NTRSDate not supplied

Using Distributed Fiber-optic Strain Sensing to Estimate Generalized Modal Coordinates from Flight-test Data

A method for estimating the generalized modal coordinates of an aircraft during flight has been developed. The Fiber-optic Sensing System (FOSS) offers an efficient and cost-effective method of measuring the strain at thousands of points along the wings. The estimation of modal coordinates was implemented as a two-step process. First, a maximum likelihood method is used to estimate the statistical properties of the sensors and generalized modal coordinates. Second, the strain mode shapes from the finite element model are used along with the statistical properties from the first step to estimate the generalized modal coordinates over time. Using simulated data from the X-56A Multi-Utility Technology Testbed (MUTT), different methods of modal coordinate estimation were compared to demonstrate the benefits and weaknesses of each. These were compared against the exact solution and an ordinary least squares (a more traditional method) estimate. Modal coordinate estimation methods were then applied to flight-test data from the X-56A aircraft to show that the method continues to work well with actual test data. The new estimation method provides insights unavailable from more classical approaches.

Jeffrey Ouellette↗
NASA NTRSDate not supplied

Multi-source Estimates of Land / Ocean Moisture Transport Variability over the Satellite Era

It is widely appreciated that atmospheric transport of water from the world’s oceans is a process key to planetary energy balance as well as Earth's habitability. What is not yet clear is the extent of variability in moisture transports, the relative importance of interdecadal variability versus climate change signals, and importantly, our ability to quantify these changes. This work assesses variations in moisture transport variability during the satellite era (~1980 to present) by comparing several different estimates. (i) The most direct estimate is the vertically integrated flux convergence of moisture from reanalyses which use observed wind and moisture information. (ii) One alternative estimate comes from P-ET over land taken from global hydrologic models constrained with precipitation and near-surface meteorology. Here we use an ensemble of six models. An adjunct to this method is to employ satellite derive ET (e.g., GLEAM or DOLCE). (iii) Complementary to this is E-P over the global oceans derived from satellite estimates of P such as TRMM, GPM and GPCP and SeaFlux V3 or J-OFURO3 estimates of E, all relying heavily upon microwave measurements. Transport between land and oceans must essentially balance at monthly scales, i.e., vanish globally. (iv) a fourth perspective comes from estimate of terrestrial RO + storage rate, delta S. G-RUN Ensemble which uses observed streamflow and P measurements to calibrate a statistical model provides the former while GRACE, GRACE-FO provide total water storage anomalies used to calculate storage rate changes. GRACE REC uses GRACE data to train a precipitation-driven statistical model to extend storage estimates before the GRACE era. (All of these alternatives to reanalysis estimates also consider the small atmospheric column water vapor contribution.) We examine the transport changes from these three different methodologies, their relative accuracies and discuss the origin of their differences. Regional trends in moisture flux divergence and their role in multi-decadal trends are considered. Interannual variability arising in connection with ENSO variability is a dominant signal, driven largely by P changes. Trends since 1980 include reductions in moisture delivery to the western U.S., eastern Brazil, and central Africa with recovery of moisture convergence to the Sahel and parts of eastern North America.

Franklin Robertson↗
NASA NTRS2023

Harmonising the Land-Use Flux Estimates of Global Models and National Inventories for 2000–2020

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr −1 ) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr −1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO 2 yr −1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr −1 ), with differences also for deforestation (2.4 GtCO 2 yr −1 ), for fluxes from other land uses (1.0 GtCO 2 yr −1 ) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO 2 yr −1 ) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr −1 ) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement.

Giacomo Grassi↗
DOE OSTI2014

Estimating Power System Dynamic States Using Extended Kalman Filter

Abstract—The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.

Huang, Zhenyu↗
DOE OSTI2016

Dynamic estimator for determining operating conditions in an internal combustion engine

Methods and systems are provided for estimating engine performance information for a combustion cycle of an internal combustion engine. Estimated performance information for a previous combustion cycle is retrieved from memory. The estimated performance information includes an estimated value of at least one engine performance variable. Actuator settings applied to engine actuators are also received. The performance information for the current combustion cycle is then estimated based, at least in part, on the estimated performance information for the previous combustion cycle and the actuator settings applied during the previous combustion cycle. The estimated performance information for the current combustion cycle is then stored to the memory to be used in estimating performance information for a subsequent combustion cycle.

42 ENGINEERING↗
DOE OSTI2019

Dynamic parameter estimation of generators

Various examples are provided for parameter estimation of generators. In one example, among others, a method includes collecting data corresponding to a generator using a phasor management unit (PMU) and estimating dynamic parameters of the generator using extended Kalman filtering (EKF) and the collected PMU data. In another example, a system includes at least one application executable in a processing device that obtains operational data corresponding to a generator and estimates a dynamic parameter of the generator using EKF and the operational data. In another example, an EKF estimator includes a dynamics estimator configured to estimate a state variable of a generator, a geometry estimator configured to estimate phasor values associated with the generator, and a Kalman filter gain configured to determine a correction to the state variable.

Miao, Zhixin↗
DOE OSTI2019

OPERATIONAL TOPOLOGY ESTIMATION OF THREE PHASE UNBALANCED POWER DISTRIBUTION SYSTEMS WITH OUTAGES

Distribution grid, in general, is constructionally loopy but a radially operated network. Opening and closing of suitable tie-switches allows transition from one radial topology to another. Tie-switches ensure reliability and provide restoration paths when faults (outages) occur. The estimation of the system's current operational topology is a precursor to implementing any optimal control actions (during normal operation) or restorative actions (during outage condition). The existing literature in topology detection suffers with several limitations including: (1) computationally intensive nonlinear formulations that are not scalable for large distribution systems, (2) methods requiring exhaustive search for the correct topology by enumerating the complete topological search space, and (3) approaches requiring large volumes of time-series measurements and are not applicable for estimating continuously changing operational topology. Furthermore, the existing literature on distribution grid topology estimation cannot be extended to estimate the operational topology during outage conditions. For operational topology estimation during outages, our objective is to estimate both energized radial feeder and the outaged sections. Starting with a radial topology, these methods identify outaged feeder sections and hence, are inapplicable when the current radial operational topology is not known. In this work, we present a mathematical programming approach to estimate the operational topology of a three-phase unbalanced power distribution system under both outage and normal operating conditions. The aforementioned limitations of existing literature were countered by modeling the problem as a mixed-integer linear programming (MILP) problem which minimizes the errors in available measurements while satisfying the power flow and structural constraints of the system. Specically, a minimum weighted least absolute value estimator is proposed that uses the instantaneous line flow measurements, historical/ forecasted load data, and ping measurements from a limited set of smart meters and solves an MILP to identify the operational topology based on a linearized three-phase unbalanced power flow model. The proposed approach is tested on IEEE 123-bus feeder and 1069 bus multi-feeder test system and the numerical results prove the applicability of the proposed method to large three phase unbalanced grids with high errors in measurements. It is observed that the approach performs well even at high percentages of measurement errors.

GANDLURU NAGA, ANANDINI↗
DOE OSTI2020

Expedited Walkdowns and Preparation of Cost Estimates for Decontamination and Demolition of Excess Facilities - 20335

Lawrence Livermore National Security, LLC (LLNS) is the prime contractor responsible for managing and operating Lawrence Livermore National Laboratory (LLNL). The mission of the LLNL is to strengthen security of the United States through development and application of world-class science and technology to enhance the nation's defense, reduce the global threat from terrorism and weapons of mass destruction, and respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. To accomplish its mission, LLNL must plan and manage its campus space and facilities to optimize use of its relatively small one-square-mile footprint. LLNL currently has numerous excess facilities that require decontamination and demolition (D and D). The LLNL Legacy Facility Program is responsible for stewardship and risk reduction programs to effectively manage these excess assets at LLNL. The Legacy Facility Program identified 10 excess facilities at LLNL that were candidates for demolition based on risks from further degradation of the facilities, potential exposure to hazardous materials, or need for removal to make way for new program facilities. The buildings range from a structure built in 1943 to serve as a US Navy drill hall during World War II, to a facility that provided neutrons to study material properties for the fusion energy program. The National Nuclear Security Administration (NNSA) appropriated FY19 funding for the development of Class 3 estimates for these 10 excess facilities that would be used to develop a Program Management Plan for funding consideration. Class 3 estimates are not conceptual, nor overly detailed, but adequate to be used for budget and appropriation purposes. Using existing strategic sourcing agreements, LLNL was able to organize a team of senior subject matter experts (SMEs) with diverse technical backgrounds across the Department of Energy (DOE) Complex to complete extensive process knowledge (PK) reviews and walk-downs of each excess facility and to deliver accurate and complete estimates under an expedited schedule (i.e., within eight weeks). To facilitate the expedited schedule, LLNL was fully prepared for the arrival of the JGMS team, which consisted of the following companies: J.G. Management Systems, Inc. (JGMS); Strata-G, LLC; and Michael Baker International. LLNL provided background and PK information on the excess facilities, pictures, supporting historical documentation, and radiological survey history as well as escorted access to the facilities. The JGMS team has previous experience supporting Program Management Plans for excess facilities at the Y-12 National Security Complex. The team mobilized to LLNL and performed the facility walk-downs from January 22-25, 2019. Using a methodical evaluation process, expertise, and experience, the team was able to deliver walk-down draft reports and estimates to LLNL before their February 8, 2019 deadline. LLNL then in turn reviewed, completed the estimates by adding waste management costs and other LLNL costs, and delivered the estimates to NNSA by February 15, 2019. The completed final reports, including draft D and D estimates, were delivered to LLNL before their March 6, 2019 deadline. The work was successfully completed ahead of the expedited schedule required by LLNL, resulting in a commendation from the client. This paper describes how the partnership and collaboration as a fully integrated team comprised of the prime contractor, LLNS, and the JGMS team successfully met the needs of the US government, resulting in the performance of expedited site walk-downs, the development of summary evaluation reports, and the preparation of draft Class 3 estimates and preliminary schedules for the D and D of 10 excess facilities at LLNL. The efforts of the team will enable the D and D of the first excess facility in the 2020-2021 time frame. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Estimating Public Dose Along Hypothetical Spent Nuclear Fuel Transportation Routes

In the future, it will be necessary to transport large quantities of spent nuclear fuel (SNF) from nuclear power plant sites to an interim storage facility (ISF) or a repository when such a facility is available. Because it is impossible to shield all of the radiation from this material, the general public will receive a very small, but quantifiable, dose from this transportation campaign. This paper describes the estimated collective effective radiation dose to the public due to SNF shipments under routine conditions and compares the estimated dose figures to the natural background radiation dose. To determine the estimated collective effective dose to the public, this analysis uses the US Department of Energy's (DOE) Stakeholder Tool for Assessing Radioactive Transportation (START), a geographic information system (GIS) routing tool, and a set of unit dose factors (UDFs) which were developed to calculate the estimated dose to the public along transportation routes. The estimated doses of radiation to which the public would be exposed during shipments of SNF is expected to be a consideration, along with many other factors, when making routing decisions. In all cases studied in this paper, estimated doses to the public will be orders of magnitude lower than the natural background radiation dose and will meet all US federal regulatory requirements for radiation dose/exposure to members of the public. The sample routes analyzed in this paper are expected to be representative of future SNF shipment routes in terms of length and mode of transportation, although they do not represent actual routes or destinations. This paper considers four routes from two shutdown reactor sites to two destinations. It must be noted that the specified origin sites are not necessarily the first sites under consideration for a de-inventory process. Further, the destination sites reviewed in this report are only to illustrate estimated dose figures along different routes and should not be taken as potential sites for an ISF or repository. For illustrative purposes only, the shortest distance rail route was found between each origin and destination point, and the collective effective dose to the public from the SNF cask was estimated along each route. This paper is not intended to make recommendations for routing decisions, but rather is intended to inform further discussion about the transportation of SNF and other radioactive materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems with High Penetration of Inverter-Based Resources: Preprint

Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression- based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power- hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.

frequency response↗
DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗
DOE OSTI2020

Enhanced Tensor Completion Based Approaches for State Estimation in Distribution Systems

Grid state estimation is essential for effective control and management of distribution systems. While weighted least squares has been the conventional method for state estimation, sparsity-aware methods have become popular due to their superior performance with limited data. Matrix completion and compressed sensing-based state estimation approaches exploit the underlying smoothness in the state variables. However, classic matrix completion methods do not take into account the temporal correlation of system states. Compressed sensing methods, on the other hand, require an appropriate choice of sparsifying basis that may not be easy to identify. This paper proposes a blocktensor completion based framework which uses an alternative approach to estimate voltage phasor, power injections and branch currents. This approach utilizes the temporal correlation of the system states in a tensor trace-norm minimization formulation with power flow equations as constraints. Herein, feature scaling is introduced in the problem formulation to benefit from the improved sensitivity of the tensor trace norm to the matrix columns in the scaled unfoldings of the tensor. Weighted tensor norm is utilized to exploit the structures of the different unfoldings of the state measurement tensor to improve the voltage estimation. The estimation accuracy is further improved by alternatively estimating the tensor columns and increasing the available data at each stage in the tensor completion process. The proposed methods are evaluated on the IEEE-33, 37 test systems and a 100- node test system. The proposed methods are shown to provide significant performance gains relative to the classic matrix and tensor completion based approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Decentralizing Network Inference Problems With Multiple-Description Fusion Estimation (MDFE)

Network inference (or tomography) problems, such as traffic matrix estimation or completion and link loss inference, have been studied rigorously in different networking applications. These problems are often posed as under-determined linear inverse (UDLI) problems and solved in a centralized manner, where all the measurements are collected at a central node, which then applies a variety of inference techniques to estimate the attributes of interest. This paper proposes a novel framework for decentralizing these large-scale under-determined network inference problems by intelligently partitioning it into smaller subproblems and solving them independently and in parallel. The resulting estimates, referred to as multiple descriptions, can then be fused together to compute the global estimate. We apply this Multiple Description and Fusion Estimation (MDFE) framework to three classical problems: traffic matrix estimation, traffic matrix completion, and loss inference. Using real topologies and traces, we demonstrate how MDFE can speed up computation while maintaining (even improving) the estimation accuracy and how it enhances robustness against noise and failures. In conclusion, we also show that our MDFE framework is compatible with a variety of existing inference techniques used to solve the UDLI problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials

Herein, this article presents generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly robust augmented inverse probability weighted (AIPW) complete-case approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Modelling bivariate extreme precipitation distribution for data-scarce regions using Gumbel-Hougaard copula with maximum entropy estimation

A new method of parameter estimation in data scarce regions is valuable for bivariate hydrological extreme frequency analysis. Here, this paper proposes a new method of parameter estimation (maximum entropy estimation, MEE) for both Gumbel and Gumbel–Hougaard copula in situations when insufficient data are available. MEE requires only the lower and upper bounds of two hydrological variables. To test our new method, two experiments to model the joint distribution of the maximum daily precipitation at two pairs of stations on the tributaries of Heihe and Jinghe River, respectively, were performed and compared with the method of moments, correlation index estimation, and maximum likelihood estimation, which require a large amount of data. Both experiments show that for the Ye Niugou and Qilian stations, the performance of MEE is nearly identical to those of the conventional methods. For the Xifeng and Huanxian stations, MEE can capture information indicating that the maximum daily precipitation at the Xifeng and Huanxian stations has an upper tail dependence, whereas the results generated by correlation index estimation and maximum likelihood estimation are unreasonable. Moreover, MEE is proved tobe generally reliable and robust by many simulations under three different situations. TheGumbel–Hougaard copula with MEE can also be applied to the bivariate frequency analysis ofother extreme events in data-scarce regions.

54 ENVIRONMENTAL SCIENCES↗