Separate Estimates Research Articles

Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise

We considered the problem of the estimation of signal-to-noise ratio (SNR) with a real deterministic sinusoid with unknown frequency, phase and amplitude in additive Gaussian noise of unknown variance. A blind SNR estimator that does not require the knowledge of the instantaneous frequency of the sinusoid, through separate estimation of signal and noise power, was derived using the method of moments, a general method to derive estimators based on high-order moments. Statistical performances of the proposed estimators were studied theoretically through derivation of Cramer–Rao lower bounds (CRLBs) and asymptotic variances. Furthermore, results from Monte-Carlo simulations that confirm the validity of the theoretical analysis are presented along with some comments on the use of proposed estimators in practical applications.

Simultaneous Densitometric Determination of Aspirin and Omeprazole by High-Performanee Thin-Layer Chromatography

A new, simple, accurate, and precise high-performance thin-layer chromatographic (HPTLC) method has been developed for the separation and simultaneous estimation of aspirin and omeprazole. Standard and sample solutions of aspirin and omeprazole were applied to aluminum foil silica gel G 60 F254 HPTLC plates. The plates were developed with ethyl acetate–dichloromethane–glacial acetic acid 8:2:0.1 (V/V) as the solvent system. HPTLC detection was performed densitometrically at 241 nm. The linear range was 160–960 ng per band for aspirin and 80–480 ng per band for omeprazole, and the correlation coefficients (R2) were 0.991 and 0.998, respectively. The limits of quantification (LOQ) were 31.89 and 8.01 ng per band, respectively, and the respective limits of detection (LOD) were 10.53 and 2.64 ng per band. Analysis of standard solutions revealed that precision and repeatability were good for both compounds studied.

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer\u2019s Disease Data

There is well-documented evidence of brain network differences between individuals with Alzheimer’s disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.

Estimation of urine volume in municipal sewage originating from patients receiving antibiotics at a private clinic in Crete, Greece

This study presents an estimation of the urine volume in the wastewater from a real, private clinic in Crete, Greece, during a seven-month period (01/06/2018 to 31/12/2018). Separate estimates were obtained for the volume of urine belonging to patients receiving antibiotics. It was found that the clinic disposed into the local municipal sewage network on the average 3,263 L/month of urine, from which 1,331 L/month (40.8%) belonged to patients receiving antibiotics. According to the pharmacy department of the private clinic, during the period of the study, the most frequently administered groups of antibiotics were on the average 779 g/month cephalosporins (68.1%), 108 g/month fluoroquinolones (9.5%) and others (11.2%), with various active substances including cefuroxime, ciprofloxacin, metronidazole and doxycycline. These active substances act like pollutants when disposed via the municipal sewer network into the environment.

Distributed simultaneous inference in generalized linear models via confidence distribution

We propose a distributed method for simultaneous inference for datasets with sample size much larger than the number of covariates, i.e., N≫p, in the generalized linear models framework. When such datasets are too big to be analyzed entirely by a single centralized computer, or when datasets are already stored in distributed database systems, the strategy of divide-and-combine has been the method of choice for scalability. Due to partition, the sub-dataset sample sizes may be uneven and some possibly close to p, which calls for regularization techniques to improve numerical stability. However, there is a lack of clear theoretical justification and practical guidelines to combine results obtained from separate regularized estimators, especially when the final objective is simultaneous inference for a group of regression parameters. In this paper, we develop a strategy to combine bias-corrected lasso-type estimates by using confidence distributions. We show that the resulting combined estimator achieves the same estimation efficiency as that of the maximum likelihood estimator using the centralized data. As demonstrated by simulated and real data examples, our divide-and-combine method yields nearly identical inference as the centralized benchmark.

Estimation of dyadic characteristics of family networks using sample survey data

We consider the use of sample survey data to estimate dyadic characteristics of family networks, with an application to noncoresident parent–child dyads. We suppose that survey respondents report either from a parent or child perspective about a dyad, depending on their membership of the dyad. We construct separate estimators of common dyadic characteristics using data from both a parent and a child perspective and show how comparisons of these estimators can shed light on data quality issues including differential missingness and reporting error. In our application we find that a simple missingness model explains some striking patterns of discrepancies between the estimators and consider the use of poststratification and a related redefinition of count variables to adjust for these discrepancies. We also develop approaches to combining the separate estimators efficiently to estimate means and frequency distributions within subpopulations.

ACTIGRAPH’S LOW-FREQUENCY EXTENSION FILTER FOR ESTIMATING WRIST-WORN PHYSICAL ACTIVITY IN OLDER ADULTS

Advancements in body-worn activity devices make them valuable for objective physical activity measurement. Research-grade monitors utilize software algorithms developed with younger populations using waist-worn devices. ActiGraph offers the low frequency extension (LFE) filter which reduces the movement threshold to capture low acceleration activity that is more common in older adults. It is unclear how this filter changes activity variable calculations in older adults. We investigated the effects of the LFE filter on wrist-worn activity estimates in this population. Participants were 21 older adults who wore the GT9X on their non-dominant wrist for 7 days in a free-living environment. Activity counts were estimated both with and without the LFE filter. Paired samples t-tests revealed that the LFE estimated significantly higher number of counts than non-LFE calculated counts per minute on all three axes (p < .001). Step count estimates were higher with (M = 20,780.09, SD = 5300.85) vs. without (M = 10,896.54, SD = 3489.45) the LFE filter, (t (20) = -22.21, p < .001). These differences have implications for calculations based on axis counts (e.g., Axis-1 calculated steps, intensity level classifications) that rely on waist-worn standards. For example, even without the filter, the GT9X calculated an average of 10,897 steps, which is likely an overestimate in this population. This suggests that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should aim to develop separate wrist and waist-worn standard estimates of these variables in older adult populations.

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not “abrupt” (that is, they should be smooth). As such, spatially and temporally pooling all data and analyzing each spatial/temporal point separately are either inappropriate or ineffective. In this article, we develop and implement a spatial- and temporal-smoothing technique, which can effectively accommodate spatial/temporal variations and realize information borrowing across spatial/temporal points. Simulation demonstrates effectiveness of the proposed approach in improving estimation. Data on a total of 123,571 patients with brain tumors diagnosed between 1911 and 2010 from 16 SEER sites is analyzed. Findings different from separate estimation and simple pooling are made. Overall, this study may provide a practically useful way for modeling the survival of brain tumor (and other cancers) using population data.

Estimating population size with imperfect detection using a parametric bootstrap

We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N. We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X. We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P(X=0)&gt;0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.

An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management

As a critically important power source for modern electric vehicles, lithium-ion batteries need to operate safely, reliably, and efficiently, which entails effective battery management systems (BMSs). How to accurately estimate internal battery states constitutes a key functionality in a BMS and needs to be designed meticulously. In this paper, a novel co-estimation hierarchy for state of charge (SOC), state of health (SOH) and state of power (SOP) in lithium-ion batteries is devised and validated experimentally. Considering the underlying coupling and characteristics among these states, a multi-time-scale estimation framework is developed for accurate estimates and moderate computational cost. First, the online, model-based SOC estimation is fulfilled by modified moving horizon estimation (mMHE) for better convergence and fault tolerance. Second, the model parameters are periodically updated by virtue of the mMHE-type optimization with a relatively long horizon. Third, the ampere-hour integral and the estimated SOC are employed to realize the capacity estimation offline. Given updated states and parameters, the model-based real-time SOP estimation reliably predicts the battery peak power respecting multiple operational constraints. Finally, the effectiveness and resilience of the joint SOC/SOH/SOP estimation is demonstrated through a number of experiments. Experimental results show that the proposed co-estimation hierarchy presents remarkable benefits, compared to separate estimation solutions. The estimation errors of SOC, voltage and capacity are less than 3%, 25 mV and 3%, respectively, for both fresh and aged cells.

Combined Wireless Ranging and Frequency Transfer for Internode Coordination in Open-Loop Coherent Distributed Antenna Arrays

Beamforming in distributed antenna arrays requires accurate coordination of the relative phasing between the separate wireless nodes. Principally, this coordination constitutes measuring the node separations and transferring a stable frequency reference, enabling beamsteering and ensuring that the local oscillators on the separate platforms are locked. A 4.5-GHz wireless system for simultaneous high-accuracy estimation of node separation and estimation of a wirelessly transferred 10-MHz frequency reference is presented in this article. Ranging is achieved using a spectrally sparse two-tone waveform with a tone separation of 200 MHz and pulsewidth of $2~\mu \text{s}$ ; a 10-MHz frequency reference is modulated onto one of the ranging tones. The use of spectrally sparse waveforms allows simple transceiver designs compared to wideband ranging systems since the tones can be individually generated and received. The theoretical bounds for range and frequency estimation are derived for a set of common modulation formats, and experimental measurements of ranging accuracy and frequency estimation accuracy are presented, demonstrating that the proposed waveform can achieve high accuracy in both domains, with range accuracy of 3.35 mm and phase errors less than 18°, supporting coherent distributed array operations up to 5.96 GHz. The results show that the ranging estimation is effectively independent of the modulation format of the reference frequency and the frequency estimation performance is independent of the sparse waveform design for ranging. Thus, the performance of range estimation and frequency transfer may be approached independently but in the same waveform.

Alternative Land‐Price Indexes for Commercial Properties in Tokyo

The System of National Accounts (SNA) requires separate estimates for the land and structure components of a commercial property. Using transactions data for the sales of office buildings in Tokyo, a hedonic regression model (the “builder’s model”) was estimated and this model generated an overall property price index as well as subindexes for the land and structure components of the office buildings. The builder’s model was also estimated using appraisal data on office building real estate investment trusts (REITs) for Tokyo. These hedonic regression models also generated estimates for net depreciation rates, which can be compared. Finally, the Japanese government constructs annual official land prices for commercial properties based on appraised values. The paper compares these official land prices with the land prices generated by the hedonic regression models based on transactions data and on REIT data. The results reveal that commercial property indexes based on appraisal and assessment prices lag behind the indexes based on transaction prices.

Characterization of two-phase displacement mechanisms in porous media by capillary and viscous forces estimation using the lattice Boltzmann simulations

This paper proposes a method for characterization of the two-phase displacement mechanisms (viscous fingering, capillary fingering, stable front, and crossover) in porous media. The approach is based on separate estimation of the capillary and viscous forces and subsequent calculation of the ratio between them (designated as W). The value W determines the dominance of capillary force and is essentially the inverse capillary number. The advantage of this approach consists in taking into account the scale of the samples and predicting the flow mechanisms using parameters measured in laboratory conditions. The balance between viscous and capillary forces is controlled by surface tension and fluid viscosities. For flow simulation, the lattice Boltzmann equations and the color-gradient model are applied. The values of W equal to 0.5 and 2, which define the boundary of the crossover zone and separate it from the flow mechanisms, were determined and the proposed method was successfully validated on three two-dimensional samples of porous media with different permeability coefficients. It revealed a directly proportional effect of permeability on W. As a result, the surface tensions, which determine the boundaries of viscous fingering, capillary fingering, stable front, are shifted towards smaller values with increasing permeability. Also, the effect of the permeability and displacement mechanisms on the displacement efficiency before, after and at quasi-steady regime was investigated. The tendency to efficiency decrease with increasing surface tension and decrease in viscosity ratio was revealed for quasi-steady regime. The absence of the mobile wetting fluid after breakthrough is detected for crossover and capillary fingers. No-displacement regime after breakthrough and, consequently, effectiveness drop to minimal values in quasi-steady regime occurs at lower surface tensions with increasing permeability.

Estimating the seasonal energy balance in Asian black bears and associated factors

AbstractEnergy balance (i.e., energy intake minus energy expenditure) is an essential factor when evaluating an animal's nutritional state. We aimed to identify periods of the year that are most important for Asian black bears (Ursus thibetanus) in terms of energy balance. We estimated bears’ daily energy balance by estimating energy intake based on the energy content (kcal/g) of major food items, their average ingestion rate (g/min), and daily feeding time (min) and by estimating energy expenditure using an equation for the costs of resting, traveling, and feeding based on behavioral data of 34 bears fitted with GPS collars in central Japan. Because food habits of bears change seasonally, we estimated daily energy balance for each month. And, because bears change their feeding behavior depending on the availability of hard mast, we made separate estimations for years of good and poor mast conditions during autumn. We identified major food items from fecal analysis and calculated the gross energy content per unit for each item. Based on direct observation of bears feeding over 10 yr, we measured ingestion rates of the major food items; Quercus crispula acorns on the tree had the highest ingestion rate, whereas the values of other items did not show notable characteristics. The variability of energy expenditure rose moderately in good mast years, except for males. On the other hand, there was a bimodal pattern of energy intake and energy balance, declining from May to July, rising again from August to October, and declining in November. The peak of energy intake and energy balance was larger in good mast years than in poor mast years only for females, whereas the cumulative energy balance in good mast years was larger than in poor mast years for both sexes. After poor mast years, the cumulative energy balance of males becomes negative in February, during hibernation, and did not exceed zero until August, even if they could start feeding in May. Thus, further longitudinal studies that examine cumulative energy balance, rather than energy balance alone, are necessary to clarify the seasonal change in the nutritional state of Asian black bears.

No Evidence of Bias in Sexual Partnership Corroboration by Race and Ethnicity Among a Diverse Cohort of Young Men Who Have Sex with Men and Transgender Women.

Black men who have sex with men (MSM) continue to experience disproportionately high HIV incidence rates relative to their white peers. Yet, Black MSM do not report higher levels of sexual risk behavior, and contextual factors such as access to care and sexual networks only partially explain these disparities. However, risk misclassification could help explain this paradox, if measurement biases systematically underestimate sexual risk behavior among Black MSM relative to their peers. The current study examined variation in sexual partnership corroboration in the RADAR study, a large and diverse cohort of young MSM and transgender women. Network data were elicited regarding all sexual partners in the prior 6months, including instances where participants reported other participants as sexual partners. Using these data, anal and condomless anal sex partners were separately examined using a series of exponential random graph models to estimate the rate of corroboration of sexual connections between participants and examine whether this parameter varied by race/ethnicity. For both types of behavior, providing separate estimates for corroboration across race/ethnicity groups reduced model fit and did not significantly vary across groups. Accordingly, we found no evidence of measurement bias by race/ethnicity in the current data. However, overall rates of corroboration (41.2-50.3%) were low, suggesting substantial levels of measurement error. Accordingly, it is vital that researchers continue to improve upon methods to measure risk behavior in order to maximize their validity. We discuss the implications of these findings, including potential alternative causes of risk misclassification (e.g., sampling bias) and future directions to reduce measurement error.

Estimation of bovine equalizing units in India: A regional perspective

Study was conducted to formulate region specific standard animal units (SAUs) more comprehensively by considering labour utilization as well as size of animals. Both primary as well as secondary data were used in the study. Primary data were collected from 2,700 households from 18 districts of nine states. Conversion coefficients were formulated by aggregating coefficients of bodyweight and labour utilization and giving appropriate weights based on the experts’ opinion. We found that the dairy farmers devoted majority of their time on care and maintenance of milch animals in the herd. Heifers got almost equal or even more priority in care and maintenance than the adult male bovines. The intensity of labour utilization in crossbred cow as compared to local adult female was higher. The overall conversion coefficients were higher for crossbred cow. The estimated regional coefficients will be helpful in estimation of costs and returns in not only in dairying but also in other livestock production. Besides the overall SAU, the separate estimation of SAU based on bodyweight and labour utilization pattern will serve the purpose of both social as well as biological research studies, particularly for studies carrying capacity, housing management and overall farm planning.

Developing a fast and accurate model to estimate allometrically the total shoot leaf area in grapevines

Ten different allometric models for estimating leaf area of the primary shoots, lateral shoots and total shoot leaf area of grapevines were developed using data collected from nine different Vitis vinifera L. cultivars. Firstly, the leaf area of main or lateral shoots was estimated according to two allometric models (Lopes and Pinto; Spann and Hereema) already in use for grapevines and other fruit tree species, and three other models appositely developed in this work and obtained after slight modifications of the previous ones. Secondly, five new allometric models were hereby developed to estimate directly total leaf area of the entire vine shoot (main shoot plus laterals). Rigorous statistical analysis enabled the evaluation of the models’ performance in relation to (a) the type and number of predictors used, and (b) the shoot category of interest (i.e. leaf area of main, laterals and total leaf area of the vine shoot). This allowed selecting, in each analysis, the allometric model with the best performance. Although the suitability of previously developed allometric models for estimating the leaf area of single shoots (main or laterals) was confirmed, the current research showed that these models can be improved using power functions (r2 improved from 0.85 to 0.88 and from 0.97 to 0.98 for main and lateral shoots, respectively). However, using these models to estimate the leaf area of an entire shoot requires separate estimation of the main shoot leaf area, that of each of its laterals and then the sum all these estimates. Considering that the number of laterals on each main shoot can be high, this can be very time-consuming. In this study, we developed, for the first time, models that can predict accurately (r2 range 0.95-0.98) and directly the leaf area of the entire shoot (main plus its laterals) using a limited number (3–5) of predictors.

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset included 792 pigs housed in 59 pens. Skin lesions in the anterior, central, and caudal regions of the body were counted 24 h after pig mixing. Animals were video-recorded for 9 h postmixing and trained observers recorded the type and duration of aggressive interactions between pairs of animals. The number of seconds that pairs of pigs spent engaged in reciprocal fights and unilateral attack behaviors were used to parametrize the intensity of social interactions (ISI). Three types of models were fitted: direct genetic additive model (DGE), traditional social genetic effect model (TSGE) assuming uniform interactions between dyads, and an intensity-based social genetic effect model (ISGE) that used ISI to parameterize SGE. All models included fixed effects of sex, replicate, lesion scorer, weight at mixing, premixing lesion count, and the total time that the animal spent engaged in aggressive interactions (reciprocal fights and unilateral attack behaviors) as a covariate; a random effect of pen; and a random direct genetic effect. The ISGE models recovered more direct genetic variance than DGE and TSGE, and the estimated heritabilities (h^D2) were highest for all traits (P < 0.01) for the ISGE with ISI parametrized with unilateral attack behavior. The TSGE produced estimates that did not differ significantly from DGE (P > 0.5). Incorporating the ISI into ISGE, even in a small dataset, allowed separate estimation of the genetic parameters for direct and SGE, as well as the genetic correlation between direct and SGE (r^ds), which was positive for all lesion traits. The estimates from ISGE suggest that if behavioral observations are available, selection incorporating SGE may reduce the consequences of aggressive behaviors after mixing pigs.

Adaptive platform trials: definition, design, conduct and reporting considerations

Researchers, clinicians, policymakers and patients are increasingly interested in questions about therapeutic interventions that are difficult or costly to answer with traditional, free-standing, parallel-group randomized controlled trials (RCTs). Examples include scenarios in which there is a desire to compare multiple interventions, to generate separate effect estimates across subgroups of patients with distinct but related conditions or clinical features, or to minimize downtime between trials. In response, researchers have proposed new RCT designs such as adaptive platform trials (APTs), which are able to study multiple interventions in a disease or condition in a perpetual manner, with interventions entering and leaving the platform on the basis of a predefined decision algorithm. APTs offer innovations that could reshape clinical trials, and several APTs are now funded in various disease areas. With the aim of facilitating the use of APTs, here we review common features and issues that arise with such trials, and offer recommendations to promote best practices in their design, conduct, oversight and reporting.

Estimation of Source-Specific Occupational Benzene Exposure in a Population-Based Case-Control Study of Non-Hodgkin Lymphoma.

Occupational exposures in population-based case-control studies are increasingly being assessed using decision rules that link participants' responses to occupational questionnaires to exposure estimates. We used a hierarchical process that incorporated decision rules and job-by-job expert review to assign occupational benzene exposure estimates in a US population-based case-control study of non-Hodgkin lymphoma. We conducted a literature review to identify scenarios in which occupational benzene exposure has occurred, which we grouped into 12 categories of benzene exposure sources. For each source category, we then developed decision rules for assessing probability (ordinal scale based on the likelihood of exposure > 0.02 ppm), frequency (proportion of work time exposed), and intensity of exposure (in ppm). The rules used the participants' occupational history responses and, for a subset of jobs, responses to job- and industry-specific modules. For probability and frequency, we used a hierarchical assignment procedure that prioritized subject-specific module information when available. Next, we derived job-group medians from the module responses to assign estimates to jobs with only occupational history responses. Last, we used job-by-job expert review to assign estimates when job-group medians were not available or when the decision rules identified possible heterogeneous or rare exposure scenarios. For intensity, we developed separate estimates for each benzene source category that were based on published measurement data whenever possible. Frequency and intensity annual source-specific estimates were assigned only for those jobs assigned ≥75% probability of exposure. Annual source-specific concentrations (intensity × frequency) were summed to obtain a total annual benzene concentration for each job. Of the 8827 jobs reported by participants, 8% required expert review for one or more source categories. Overall, 287 (3.3%) jobs were assigned ≥75% probability of exposure from any benzene source category. The source categories most commonly assigned ≥75% probability of exposure were gasoline and degreasing. The median total annual benzene concentration among jobs assigned ≥75% probability was 0.11 ppm (interquartile range: 0.06-0.55). The highest source-specific median annual concentrations were observed for ink and printing (2.3 and 1.2 ppm, respectively). The applied framework captures some subject-specific variability in work tasks, provides transparency to the exposure decision process, and facilitates future sensitivity analyses. The developed decision rules can be used as a starting point by other researchers to assess occupational benzene exposure in future population-based studies.