Average Of Estimates Research Articles

Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects

In cluster randomized trials (CRTs), the hierarchical nesting of participants (level 1) within clusters (level 2) leads to two conceptual populations: clusters and participants. When cluster sizes vary and the goal is to generalize to a hypothetical population of clusters, the unit average treatment effect (UATE), which averages equally at the cluster level rather than equally at the participant level, is a common estimand of interest. From an analytic perspective, when a generalized estimating equations (GEE) framework is used to obtain averaged treatment effect estimates for CRTs with variable cluster sizes, it is natural to specify an inverse cluster size weighted analysis so that each cluster contributes equally and to adopt an exchangeable working correlation matrix to account for within-cluster correlation. However, such an approach essentially uses two distinct weights in the analysis (i.e. both cluster size weights and covariance weights) and, in this article, we caution that it will lead to biased and/or inefficient treatment effect estimates for the UATE estimand. That is, two weights “make a wrong” or lead to poor estimation characteristics. These findings are based on theoretical derivations, corroborated via a simulation study, and illustrated using data from a CRT of a colorectal cancer screening program. We show that, an analysis with both an independence working correlation matrix and weighting by inverse cluster size is the only approach that always provides valid results for estimation of the UATE in CRTs with variable cluster sizes.

Mallows Model Averaging Estimation for Linear Regression Model with Right Censored Data

Mallows Model Averaging Estimation for Linear Regression Model with Right Censored Data

Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model

In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved to be asymptotically optimal. It is shown that the Kullback–Leibler model averaging (KLMA) estimator asymptotically minimizes the in‐sample Kullback–Leibler divergence and improves the forecast accuracy of out‐of‐sample even under different loss functions. In simulations, we show that the KLMA estimator compares favorably with smooth‐AIC estimator (SAIC), smooth‐BIC estimator (SBIC), and Mallows model averaging estimator (MMA), especially when some nonlinear noise is added to the data generation process. The empirical applications in the daily range of S&P500 and price duration of IBM show that the out‐of‐sample forecasting capacity of the KLMA estimator is better than that of other methods.

Evaluation of the Validity and Reliability of Connected Insoles to Measure Gait Parameters in Healthy Adults.

The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe® insoles against the GAITRite® mat reference. The mean values and the values of variability were calculated per subject for GAITRite® and insoles. A t-test and Levene’s test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson’s correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson’s correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite® to estimate the mean and variability of gait parameters.

Assessment of technical condition of high-voltage overhead power transmission lines at the stage of their ageing

The organization of operation, maintenance and repair of the basic technological facilities of electric power systems (EPS), which are beyond their designed service life (hereinafter referred to as ageing facilities, or AFs) is one of the problems that determine the energy security of many countries, including economically developed nations. The principal cause of insufficient overall performance of AFs is the traditional focus of the EPS management on economic efficiency and the insufficient attention to reliability and safety of AFs. The tendency to nonlinear growth in the frequency of occurrence of unacceptable consequences in the EPS requires ensuring the operational reliability and safety of AFs. The averaged estimates of reliability and safety used at designing power facilities are not suitable for characterization of overall operational performance. Among the basic and the least investigated (in terms of operational reliability and safety) EPS facilities are overhead power transmission lines (OPL) with a voltage of 110 кV and above. This is for a reason. OPL are electric power facilities with elements distributed along a multi-kilometer line (supports, insulators, wires, accessories, etc.). That is what makes the organization of continuous monitoring of the technical condition of each of these elements, and, consequently, the assessment of operational reliability and safety, so problematic. A method is suggested for assessment of “weak links” among the operated OPL on operative intervals of time along with a method for assessment of the technical condition of OPL at examination of a representative sample.

Extrinsic factors, endocrine mechanisms, and behavioral indicators of migratory restlessness in wintering whooper swans (Cygnus cygnus)

Extrinsic factors, endocrine mechanisms, and behavioral indicators of migratory restlessness were studied in wintering whooper swans (Cygnus cygnus) in the Sanmenxia Swan National Wetland Park in western Henan Province, central China. First, the fecal glucocorticoid metabolite (FGM) concentration was established and related to mean air temperature or photo period (day length) using simple linear or non-linear regression models. After a model selection procedure, the best fitted model revealed that an increase of FGM concentration was associated with an increase in the squared mean air temperature (R2 = 0.88). Other models showed an increasing FGM concentration to correspond with increasing values of day length, squared day length, and mean air temperature—however without statistical support. In a second step, behavioral frequencies of seven behaviors were condensed into three behavioral principal components (PCs) using principal components analysis. Behavioral PCs largely corresponded to three activity phases described for wintering whooper swans in central China and were correlated with the FGM concentration using Spearman's rank-order correlations. Results revealed a significant correlation between FGM and behavioral PC2 (positive factor loadings from vigilance and preening, negative loading from foraging). Finally, we tested for an effect of behavioral PCs on changes in winter home range size using a set of multiple linear regression models. Results of averaged model parameter estimates showed only the behavioral PC3 (positive factor loadings from fighting and calling, negative loading from locomotion) had a marginal significant effect on home range size. Results confirmed findings of previous studies on migratory restlessness in whooper swans. However, due to the small sample size (N = 15 weeks) the effect of PC3 on home range size was weak and should be viewed with caution.

Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

Multi-hop sensor network scheduling for optimal remote estimation

This paper studies a design problem of how a group of wireless sensors are selected and scheduled to transmit data efficiently over a multi-hop network subject to energy considerations, when the sensors are observing multiple independent discrete-time linear systems. Each time instant, a subset of sensors is selected to transmit their measurements to a remote estimator. We formulate an optimization problem, in which a network schedule is searched to minimize a linear combination of the averaged estimation error and the averaged transmission energy consumption. It is shown that the optimal network schedule forms a tree with root at the gateway node. From this observation, we manage to separate the optimization problem into two subproblems: tree planning and sensor selection. We solve the sensor selection subproblem by a Markov decision process, showing that the optimal solution admits a periodic structure when the transmission cost is sufficiently low. Efficient algorithms are proposed and they are shown to reduce the computational complexity of the original optimization problem. Numerical studies illustrate the effectiveness of the proposed algorithms, and show that they are scalable to large networks.

Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.

Model averaging prediction for nonparametric varying-coefficient models with B-spline smoothing

Model averaging has been demonstrated as a powerful tool in statistical prediction over the past decade. However, a majority of related works focus on the parametric model averaging. In this paper, we propose a model averaging estimation under nonparametric varying-coefficient models. Differing from existing works, our proposal concentrates on the development of the B-spline approximation to nonparametric varying coefficient functions for model average estimator, rendering the computational burden more cheaply than the kernel smoothing based estimator. Furthermore, our procedure is asymptotically optimal under mild conditions. The asymptotic optimality established in current paper is in terms of conditional quadratic loss function when the variance of model error is known or unknown, respectively. Three different cases of candidate models are considered. Extensive simulations are carried out to evaluate the finite-sample performance of our estimator. A real data is analyzed for illustration as well.

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-, and 64-kt wind radii) and intensity [maximum wind speed (MWS)] from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50, and R64 are 31.2, 21.8, and 17.0 nmi (1 nmi = 1.852 km) for radiometer, and 21.7, 16.5, and 16.3 nmi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared with the best-track reports, the bias and RMSE for the MWS estimates are -0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17-80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with a long lifecycle, such as Typhoon Noru, we demonstrate that the high-resolution and multitemporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

Airborne formaldehyde and volatile organic compound measurements over the Daesan petrochemical complex on Korea’s northwest coast during the Korea-United States Air Quality study

The U.S. National Aeronautics and Space Administration in partnership with Korea’s National Institute of Environmental Research embarked on the Korea-United States Air Quality (KORUS-AQ) study to address air quality issues over the Korean peninsula. Underestimation of volatile organic compound (VOC) emissions from various large facilities on South Korea’s northwest coast may contribute to this problem, and this study focuses on quantifying top-down emissions of formaldehyde (CH2O) and VOCs from the largest of these facilities, the Daesan petrochemical complex, and comparisons with the latest emission inventories. To accomplish this and additional goals discussed herein, this study employed a number of measurements acquired during KORUS-AQ onboard the NASA DC-8 aircraft during three Daesan overflights on June 2, 3, and 5, 2016, in conjunction with a mass balance approach. The measurements included fast airborne measurements of CH2O and ethane from an infrared spectrometer, additional fast measurements from other instruments, and a suite of 33 VOC measurements acquired by the whole air sampler. The mass balance approach resulted in consistent top-down yearly Daesan VOC emission flux estimates, which averaged (61 ± 14) × 103 MT/year for the 33 VOC compounds, a factor of 2.9 ± 0.6 (±1.0) higher than the bottom-up inventory value. The top-down Daesan emission estimate for CH2O and its four primary precursors averaged a factor of 4.3 ± 1.5 (± 1.9) times higher than the bottom-up inventory value. The uncertainty values in parentheses reflect upper limits for total uncertainty estimates. The resulting averaged top-down Daesan emission estimate for sulfur dioxide (SO2) yielded a ratio of 0.81–1.0 times the bottom-up SO2 inventory, and this provides an important cross-check on the accuracy of our mass balance analysis.

Model averaging estimation for high-dimensional covariance matrices with a network structure.

In this paper, we develop a model averaging method to estimate a high-dimensional covariance matrix, where the candidate models are constructed by different orders of polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance estimators. Finally, we conduct numerical simulations and a case study on Chinese airport network structure data to demonstrate the usefulness of the proposed approaches.

Modeling the change of water content in wood at atmospheric drying

Objective: to develop a methodology for predicting changes in the water content in wood during atmospheric drying, taking into account the fast and slow stages of this process. Research methods: mathematical modeling, applied analysis of results, comparison with experimental data known from the literature, synthesis in order to better understand the regularities of drying. Results: a two-parameter model is developed, in which a phenomenological approach is used, focused on obtaining averaged estimates of wood moisture during atmospheric drying. Causal relationships are not detailed, but are taken into account in an integral form. Calculation formulas are obtained, the use of which simplifies the procedure for predicting changes in the water content in wood during atmospheric drying. The adequacy of the model and the reliability of the calculation results are confirmed by their consistency with the experimental data known in the literature. Research prospects can be focused on adapting the model to other drying methods in order to reduce the energy and time spent on a given technological process.

The Method of the Main Tone Detection in the Structure of Electromyographic Signals for the Task of Broken Human Communicative Function Compensation

In the work the method of electromyographic signals processing for the task of broken human communicative function compensation is developed. The method allows to detect the signs of main tone in the structure of electromyographic signals, that were recorded from the surface of patients neck near the vocal folds. Using this signs it is possible to identify the mentally spoken vowel and vocalised consonant phonemes and to identify the speech of patients with broken or lost communicative function. The developed method includes two stages, namely: preparatory and basic. The purpose of the preparatory stage is to obtain data on the individual features of the patient's speach, in particular the approximate value of the main tone frequency and the frequency interval of the main tone frequency when patient is trying to utter test sequences of sounds at certain points in time. These data are necessary to enable the use of the basic stage of the method, which involves the processing of electromyographic signals recorded in an arbitrary attempt to pronounce arbitrary sounds, words or phrases by the patient. It is proposed to processing the electromyographic signals by methods of spectral-correlation analysis using the sliding window method if presenting of such biosignals in the form of a piecewise stationary random process to detect the time intervals of the presence of main tone signs. Within each sliding window the estimates of the power spectral density distribution are calculated and averaged over the frequency and power within the predetermined interval of existence of the main tone frequency. The obtained averaged estimates make it possible to set the time intervals of the main tone presence and, accordingly, the subsequent identification of vowel and consonant vocalised phonemes. The experimentally registered EMG signal was processed by the developed method with different values of the sliding window width.

On the Green's function emergence from interferometry of seismic wave fields generated in high-melt glaciers: implications for passive imaging and monitoring

Abstract. Ambient noise seismology has revolutionized seismic characterization of the Earth's crust from local to global scales. The estimate of Green's function (GF) between two receivers, representing the impulse response of elastic media, can be reconstructed via cross-correlation of the ambient noise seismograms. A homogenized wave field illuminating the propagation medium in all directions is a prerequisite for obtaining an accurate GF. For seismic data recorded on glaciers, this condition imposes strong limitations on GF convergence because of minimal seismic scattering in homogeneous ice and limitations in network coverage. We address this difficulty by investigating three patterns of seismic wave fields: a favorable distribution of icequakes and noise sources recorded on a dense array of 98 sensors on Glacier d'Argentière (France), a dominant noise source constituted by a moulin within a smaller seismic array on the Greenland Ice Sheet, and crevasse-generated scattering at Gornergletscher (Switzerland). In Glacier d'Argentière, surface melt routing through englacial channels produces turbulent water flow, creating sustained ambient seismic sources and thus favorable conditions for GF estimates. Analysis of the cross-correlation functions reveals non-equally distributed noise sources outside and within the recording network. The dense sampling of sensors allows for spatial averaging and accurate GF estimates when stacked on lines of receivers. The averaged GFs contain high-frequency (>30 Hz) direct and refracted P waves in addition to the fundamental mode of dispersive Rayleigh waves above 1 Hz. From seismic velocity measurements, we invert bed properties and depth profiles and map seismic anisotropy, which is likely introduced by crevassing. In Greenland, we employ an advanced preprocessing scheme which includes match-field processing and eigenspectral equalization of the cross spectra to remove the moulin source signature and reduce the effect of inhomogeneous wave fields on the GFs. At Gornergletscher, cross-correlations of icequake coda waves show evidence for homogenized incident directions of the scattered wave field. Optimization of coda correlation windows via a Bayesian inversion based on the GF cross coherency and symmetry further promotes the GF estimate convergence. This study presents new processing schemes on suitable array geometries for passive seismic imaging and monitoring of glaciers and ice sheets.

Global rangeland production systems and livelihoods at threat under climate change and variability

Rangelands are one of the Earth’s major ice-free land cover types. They provide food and support livelihoods for millions of people in addition to delivering important ecosystems services. However, rangelands are at threat from climate change, although the extent and magnitude of the potential impacts are poorly understood. Any declines in vegetation biomass and fluctuations in grazing availability would be of concern for food production and ecosystem integrity and functionality. In this study, we use a global rangeland model in combination with livestock and socio-economic datasets to identify where and to what extent rangeland systems may be at climatic risk. Overall, mean herbaceous biomass is projected to decrease across global rangelands between 2000 and 2050 under RCP 8.5 (−4.7%), while inter- (year-to-year) and intra- (month-to-month) annual variabilities are projected to increase (+21.3% and +8.2%, respectively). These averaged global estimates mask large spatial heterogeneities, with 74% of global rangeland area projected to experience a decline in mean biomass, 64% an increase in inter-annual variability and 54% an increase in intra-annual variability. Half of global rangeland areas are projected to experience simultaneously a decrease in mean biomass and an increase in inter-annual variability—vegetation trends both potentially harmful for livestock production. These regions include notably the Sahel, Australia, Mongolia, China, Uzbekistan and Turkmenistan and support 376 million people and 174 million ruminant Tropical Livestock Units. Additionally, the rangeland communities currently the most vulnerable (here, with the lowest livestock productivities and economic development levels and with the highest projected increases in human population densities) are projected to also experience the most damaging vegetation trends for livestock production. Although the capacity of rangeland systems to adapt is highly complex, analyses such as these generate some of the information required to inform options to facilitate pastoral system mitigation and adaptation strategies under climate change.

Experience of registration of reflection of radio signals from plasma meteor tracks in the observatory of SKSU

One of the actual problems of astrophysical studies of The solar system bodies is the registration of meteor phenomena occurring in the upper layers of The earth’s atmosphere. Statistics of such events allow to obtain averaged estimates of the concentration of meteoroids in near-earth space. These data have an important practical application on the security of space infrastructure development. In this paper, it is shown that optical methods of meteor registration, despite their diversity, suffer from limitations associated with the influence of atmospheric meteorology, daylight and anthropogenic illumination of the sky. In this regard, the transition to the use of methods of registration of meteors based on the property of formed or plasma tracks to reflect radio waves is relevant.The results of work on the development of the material base and methods of radio observations of meteors in the center of astrophysical research of North Kazakhstan state University are presented. The approach of registration of the radio signal reflected from the plasma meteor track of the remote station is applied. Information processing was carried out online using standard software. Comparison of the obtained results with the published data on the application of the method showed a high degree of similarity of the amplitude-time structure of the signal with typical for the applied method. The daily frequency distribution of meteor events is also quite realistic. Thus, we can state the creation in the Republic of Kazakhstan of the first point for registration of meteors in the radio range.

Application of the Random Encounter Model in citizen science projects to monitor animal densities

AbstractAbundance and density are vital metrics for assessing a species’ conservation status and for developing effective management strategies. Remote‐sensing cameras are being used increasingly as part of citizen science projects to monitor wildlife, but current methodologies to monitor densities pose challenges when animals are not individually recognizable. We investigated the use of camera traps and the Random Encounter Model (REM) for estimating the density of West European hedgehogs (Erinaceus europaeus) within a citizen science framework. We evaluated the use of a simplified version of theREMin terms of the parameters’ estimation (averaged vs. survey‐specific) and assessed its potential application as part of a large‐scale, long‐term citizen science project. We compared averagedREMestimates to those obtained via spatial capture–recapture (SCR) using data from nocturnal spotlight surveys. There was a high degree of concordance inREM‐derived density estimates from averaged parameters versus those derived from survey‐specific parameters. AveragedREMdensity estimates were also comparable to those produced bySCRat eight out of nine sites; hedgehog density was 7.5 times higher in urban (32.3 km−2) versus rural (4.3 km2) sites. Power analyses indicated that the averagedREMapproach would be able to detect a 25% change in hedgehog density in both habitats with >90% power. Furthermore, despite the high start‐up costs associated with theREMmethod, it would be cost‐effective in the long term. The averagedREMapproach is a promising solution to the challenge of large‐scale and long‐term species monitoring. We suggest including theREMas part of a citizen science monitoring project, where participants collect data and researchers verify and implement the required analysis.

Flossing DNA in a Dual Nanopore Device.

Solid-state nanopores are a single-molecule technique that can provide access to biomolecular information that is otherwise masked by ensemble averaging. A promising application uses pores and barcoding chemistries to map molecular motifs along single DNA molecules. Despite recent research breakthroughs, however, it remains challenging to overcome molecular noise to fully exploit single-molecule data. Here, an active control technique termed "flossing" that uses a dual nanopore device is presented to trap a proteintagged DNA molecule and up to 100's of back-and-forth electrical scans of the molecule are performed in a few seconds. The protein motifs bound to 48.5 kb λ-DNA are used as detectable features for active triggering of the bidirectional control. Molecular noise is suppressed by averaging the multiscan data to produce averaged intertag distance estimates that are comparable to their known values. Since nanopore feature-mapping applications require DNA linearization when passing through the pore, a key advantage of flossing is that trans-pore linearization is increased to >98% by the second scan, compared to 35% for single nanopore passage of the same set of molecules. In concert with barcoding methods, the dual-pore flossing technique could enable genome mapping and structural variation applications, or mapping loci of epigenetic relevance.