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Accurately estimating forage biomass is essential for understanding the nutritional value of vegetation communities for wild herbivores. However, clipping, drying, and weighing vegetation by species requires substantial labor, which restricts the size and number of plots that can be sampled over a large geographical area. Labor‐intensive sampling effort can be reduced by using a double sampling strategy that pairs visual estimates of horizontal cover of vegetation with a subset of clipped plots to create equations that convert horizontal cover to biomass. However, sampling strategy and environmental covariates have rarely been considered when developing cover‐biomass equations. We evaluated environmental and sampling factors that could affect the cover‐biomass relationship using 3,404 cover‐biomass pairs measured in the Clearwater River Basin in North‐central Idaho, USA, collected from May 31–October 4 in 2016 and 2017. Our study demonstrated that when carefully applied, double sampling techniques can streamline field sampling while providing cover‐biomass conversion equations with moderate to high predictive ability (pseudo R2 = 0.25–0.99, x¯ $\bar{x}$ = 0.70) for both biomass of current annual growth and high‐quality (e.g., leaves of shrubs) portion of plants (pseudo R2 = 0.65–1). Based on our results, we recommend comparing fit and predictive ability of regression types (linear, log‐linear, Gamma) and collecting >20 cover‐biomass pairs spanning a wide range of horizontal cover (1 to >50%) for each plant species. We found that accounting for overstory canopy cover and season through interaction terms improved the fit of equations by as much as 20% for over half of the 81 species‐specific and 12 structure groups. We found a modest degree of observer bias; thus, training should reduce observer bias and improve consistency but may not alleviate inconsistencies among studies. Careful classification of plant species into plant structure groups can reduce the number of equations, but species‐specific equations for the most abundant and objectively relevant plant species should be developed. [ABSTRACT FROM AUTHOR]

Abstract: In this article, two modified ratio estimators of the population mean are suggested provided that the first or third quartiles of the auxiliary variable can be established when the mean of the auxiliary variable is known. The double-sampling method is used to estimate the mean of the auxiliary variable if it is unknown. The suggested estimators are investigated under simple random sampling (SRS) and median ranked set sampling (MRSS) schemes. The new estimators when using MRSS are compared to their counterparts under SRS. The bias and the mean square error of the proposed estimators are derived. It turns out that the estimators are approximately unbiased and the MRSS estimators are more efficient than the SRS estimators, on the basis of the same sample size, correlation coefficient, and quartile. A real data set is used for illustration. [Copyright &y& Elsevier]

Abstract: It is desirable to estimate disease prevalence based on data collected by a gold standard test, but such a test is often limited due to cost and ethical considerations. Data with partial validation series thus become an alternative. The construction of confidence intervals for disease prevalence with such data is considered. A total of 12 methods, which are based on two Wald-type test statistics, score test statistic, and likelihood ratio test statistic, are developed. Both asymptotic and approximate unconditional confidence intervals are constructed. Two methods are employed to construct the unconditional confidence intervals: one involves inverting two one-sided tests and the other involves inverting one two-sided test. Moreover, the bootstrapping method is used. Two real data sets are used to illustrate the proposed methods. Empirical results suggest that the 12 methods largely produce satisfactory results, and the confidence intervals derived from the score test statistic and the Wald test statistic with nuisance parameters appropriately evaluated generally outperform the others in terms of coverage. If the interval location or the non-coverage at the two ends of the interval is also of concern, then the aforementioned interval based on the Wald test becomes the best choice. [Copyright &y& Elsevier]

Abstract: In this article we derive likelihood-based confidence intervals for the risk ratio using over-reported two-sample binary data obtained using a double-sampling scheme. The risk ratio is defined as the ratio of two proportion parameters. By maximizing the full likelihood function, we obtain closed-form maximum likelihood estimators for all model parameters. In addition, we derive four confidence intervals: a naive Wald interval, a modified Wald interval, a Fieller-type interval, and an Agresti–Coull interval. All four confidence intervals are illustrated using cervical cancer data. Finally, we conduct simulation studies to assess and compare the coverage probabilities and average lengths of the four interval estimators. We conclude that the modified Wald interval, unlike the other three intervals, produces close-to-nominal confidence intervals under various simulation scenarios examined here and, therefore, is preferred in practice. [Copyright &y& Elsevier]

In this paper, a low-voltage fourth-order 2-2 cascade delta–sigma (\Delta\Sigma) modulator using the proposed double-sampling switched-operational-amplifier (SOP)-based integrator is presented. In the analog part of the \Delta\Sigma modulator, most of the power consumption comes from the SOP used in the integrator. Hence, the requirement of the SOP must effectively be relaxed to reduce the power consumption of the modulator. In each cascade stage, the second-order \Delta\Sigma modulator with a cascade-of-integrators input feedforward structure is used to reduce the output swing. The second integrator output of the first stage is directly connected to the second stage to simplify circuit design on the analog part. Furthermore, the double-sampling SOP-based integrator is also adopted to reduce the applied clock frequency by half. In this paper, systematic means of designing the presented modulator and searching the minimum current of the SOP in a specified supply voltage are also developed. The presented \Delta\Sigma modulator is fabricated in a 0.18-\mu\m 1P6M CMOS technology. The chip core area without PADs is 1.57 \mm^2. The modulator achieves an 84-dB peak signal-to-noise plus distortion ratio and an 88-dB dynamic range in 20-kHz signal bandwidth with a clock frequency of 2 MHz. The power consumption of the presented modulator core is 0.66 mW at a supply voltage of 1 V. The presented modulator can also be operated in a wide range of supply voltages from 1.8 V down to 0.9 V without seriously degrading the performance. [ABSTRACT FROM PUBLISHER]

Abstract: The present paper develops an outlier model suitable for problems wherein identification of outliers is essential and, applied areas of statistics are abound with such examples. One of the peculiarities of outliers in survey sampling is that there could be observed as well as unobserved outliers; the paper assumes that there are no unobserved outliers. We use a generalized linear model (GLM) with higher variances for the outlying units. Count data are treated through overdispersed GLM of . Error components of the link function are assumed to have scale mixtures of normal distributions. The framework covers both standard survey sampling and small area estimation problems. The number as well as the set of outliers are assumed to be unknown. Posterior joint distribution is found using the reversible jump Markov chain and Metropolis–Hastings algorithm. We also use properties of the deviance function of GLM () for posterior computations. The basic framework is extended to various models appropriate in survey sampling such as double sampling and conditional autoregressive models. The method is illustrated using leukaemia patients data of , Scottish lip cancer data, Missouri lung cancer data and Baltimore census data. [Copyright &y& Elsevier]

In a double-sampling quadrature bandpass sigma-delta modulator, path mismatch between the double-sampling branches and between the I/Q paths occurs. In this paper, an analytical study is presented which shows that this causes quantization noise and input signals to fold from the image band into the signal band and that this also results in a self-image component. To reduce the folding from the image band, a novel resonator is presented. This resonator has a bilinear input circuit so that noise and signals exhibits first-order shaping before folding in the band of interest. Next, three different modulator architectures based on the novel resonator are introduced. Finally, the remaining problem of self-image is tackled with a simple, yet efficient offline calibration strategy. Various design examples are shown and simulated to illustrate and prove the effectiveness of the proposed architectures and methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we derive five first-order likelihood-based confidence intervals for a population proportion parameter based on binary data subject to false-positive misclassification and obtained using a double sampling plan. We derive confidence intervals based on certain combinations of likelihood, Fisher-information types, and likelihood-based statistics. Using Monte Carlo methods, we compare the coverage properties and average widths of three new confidence intervals for a binomial parameter. We determine that an interval estimator derived from inverting a score-type statistic is superior in terms of coverage probabilities to three competing interval estimators for the parameter configurations examined here. Utilizing the expressions derived, we also determine confidence intervals for a binary parameter using real data subject to false-positive misclassification. [Copyright &y& Elsevier]