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The recently proposed semantic-aligned matching detection transformer (SAM–DETR model) accelerates the convergence of the detection transformer (DETR) by mapping object queries into an identical embedding space as the encoder's output feature map. However, SAM–DETR model has the problem of low detection accuracy compared to other DETR variants. We observe that the lower detection accuracy of SAM–DETR model is caused by the insufficient number of sample points and the inaccurate localization of the sample points during re-sampling, which blurs the generated attention map. This paper proposes an object detector based on a feature correction and double sampling DETR (FCDS-DETR) to solve this problem. FCDS-DETR takes SAM–DETR model as a baseline and builds on it by adding a feature correction module and a double sampling mechanism to achieve further improvement in detection accuracy with a limited number of additional parameters without sacrificing convergence speed. Firstly, FCDS-DETR improves the sampling point localization accuracy by adding a feature correction module to model the inter-channel dependence of the feature maps to be sampled. Secondly, the number of sampled points is increased by the double sampling mechanism, and attention fusion is used to fuse the attention weight maps corresponding to the two sets of sampled points to improve the recognizability of the attention weight maps. The experimental results show that the average precision is improved by +0.7 on the COCO dataset compared with the SAM–DETR model, and the number of parameters is increased by only 10.34 % , which improves the detection performance of the model very well. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control (MPC) with a double-sampling technique to spread the current spectrum. Finite control set MPC has the characteristic of a switching frequency that fluctuates. Thus, the components of the switching frequency and harmonics are reduced in the current spectrum. When MPC is utilized to control current, virtual vectors are added to ensure a low current total harmonic distortion (THD). However, increasing the voltage vector reduces the switching frequency variation, which leads to degradation of the spread-spectrum performance. The proposed method allows MPC to have an additional feature to change the harmonic components while reducing the current THD. To verify the proposed scheme, a comparison of its performance according to the sampling techniques and number of the voltage vectors has been conducted. The simulation and experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Survey practitioners commonly encounter various types of nonresponse and strive to implement methods that mitigate any resulting bias when reporting results. In national forest inventories (NFI), complete or partial nonresponse usually results from hazardous conditions or lack of plot access permission. While many factors may be related to nonresponse, the two primary factors in the NFI of the USA are public/private land ownership and office/field plot status. To ameliorate potential nonresponse bias, these factors should be accounted for in the estimation process. An estimation method is presented where response homogeneity groups (RHGs) account for differential nonresponse rates between forest/nonforest plots. In a post-stratified estimation context, ratio-to-size estimators are used in RHGs within post-strata to avoid potential bias in variance estimates arising from partial plot nonresponse. Combining RHGs within post-strata requires a complex variance estimator that includes four sources of uncertainty. Testing of the estimation method on a synthetic population showed the approach is essentially unbiased. Application to NFI data from 10 states in the USA consistently showed the RHG method produced state-level estimates of forestland area that were 0.1%–3.6% larger than the current post-stratified estimation procedure. It is suggested that these differences are indicative of the nonresponse bias present when plots having differential nonresponse rates are not accounted for. [ABSTRACT FROM AUTHOR]

In sampling theory, the auxiliary information is widely used to increase precision of the estimators when the study variable is correlated with the auxiliary variable. In several practical situations, the auxiliary information is available in the form of the auxiliary attribute(s). In this paper, we proposed a generalized estimator for the estimation of finite population mean using the auxiliary attribute under stratified two-phase sampling. The mathematical expressions of approximate bias and mean square error (MSE) of the proposed estimator are obtained to the first-order. Many special cases of the proposed estimator are also obtained using the known parameters of the auxiliary attribute. The algebraic comparisons of the MSE of the proposed estimator have been made the MSE of the competing estimators. The performance of the proposed estimator is evaluated using real data-sets. The results illustrate the good performance of the proposed estimators and its sub-cases. [ABSTRACT FROM AUTHOR]

Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. [ABSTRACT FROM AUTHOR]

Background: A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods: The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce's formula. Results: Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions: A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1 n where n is the number of sample points. [ABSTRACT FROM AUTHOR]

Background: The double sampling method known as "big BAF sampling" has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume. A well-known method for variance determination, Bruce's method, is customarily used because the volume estimator takes the form of a product of random variables. However, the genesis of Bruce's method is not known to most foresters who use the method in practice. Methods: We establish that the Taylor series approximation known as the Delta method provides a plausible explanation for the origins of Bruce's method. Simulations were conducted on two different tree populations to ascertain the similarities of the Delta method to the exact variance of a product. Additionally, two alternative estimators for the variance of individual tree volume-basal area ratios, which are part of the estimation process, were compared within the overall variance estimation procedure. Results: The simulation results demonstrate that Bruce's method provides a robust method for estimating the variance of inventories conducted with the big BAF method. The simulations also demonstrate that the variance of the mean volume-basal area ratios can be computed using either the usual sample variance of the mean or the ratio variance estimators with equal accuracy, which had not been shown previously for Big BAF sampling. Conclusions: A plausible explanation for the origins of Bruce's method has been set forth both historically and mathematically in the Delta Method. In most settings, there is evidently no practical difference between applying the exact variance of a product or the Delta method—either can be used. A caution is articulated concerning the aggregation of tree-wise attributes into point-wise summaries in order to test the correlation between the two as a possible indicator of the need for further covariance augmentation. [ABSTRACT FROM AUTHOR]

The implementation of an adaptive cluster sampling design often becomes logistically challenging because variation in the final sampling effort introduces uncertainty in survey planning. To overcome this drawback, an inexpensive and easy to measure auxiliary variable could be used in a two-phase survey strategy, called adaptive cluster double sampling (Félix-Medina and Thompson in Biometrika 91:877-891, 2004). In this paper, a two-phase sampling strategy is proposed which combines the idea of adaptive cluster double sampling with the principle of post-stratification. In the first-phase an adaptive cluster sample is selected by means of an inexpensive auxiliary variable. Networks from the first phase sampling are then post-stratified according to their size. In the second-phase, the network structure is used to select a subsample of units by means of stratified random sampling. The proposed sampling strategy employs stratificationwithout requiring an a priori delineation of the strata. Indeed, the strata sizes are estimated in the course of the two-phase sampling process. Therefore, it is suitable for situations where stratification is suspected to be efficient but strata cannot be easily delineated in advance. In this framework, a new type of estimator for the population mean which mimics the stratified sampling mean estimator and an estimator of the sampling variance are proposed. The results of a simulation study confirm, as expected, that the use of post-stratification leads to gain in precision for the estimator. The proposed sampling strategy is applied for targeting an epiphytic lichen community Lobarion pulmonariae in a forest area of the Northern Apennines (N-Italy), characterized by several species of conservation concern. [ABSTRACT FROM AUTHOR]

Abstract: A Bayesian approach is considered for the interval estimation of a binomial proportion in doubly sampled data. The coverage probability and the expected width of the Bayesian confidence interval are compared with likelihood-related confidence intervals. It is shown that a hierarchical Bayesian approach provides relatively simple and effective confidence intervals. In addition, it is shown that Agresti–Coull type confidence interval, discussed by Lee and Choi (2009), can be justified by the Bayesian framework. [Copyright &y& Elsevier]

A double-sampling split ΣΔ-ADC with bilinear integrators and a 7-level quantizer is presented. It achieves third order noise shaping with a second order modulator through quantization noise-coupling. The modulator is integrated in a 130 nm CMOS technology. For a clock frequency of 48 MHz and an oversampling ratio of 20 (2.4 MHz signal bandwidth), it achieves 72 dB DR and 68 dB SNR. The prototype consumes 8 mW from a 1.2 V voltage supply. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a chain ratio–product type estimators has been developed for estimating population mean of the study variable using two auxiliary variables under double sampling scheme, when the information on another additional auxiliary character is available along with the main auxiliary character. The optimum property of the proposed strategy has been identified in two different cases with their variances. Theoretical and empirical studies have been done to demonstrate the efficiency of the proposed estimator over other estimators, which utilized the information on two auxiliary characteristics. [Copyright &y& Elsevier]

This paper deals with the optimal designing of a skip-lot sampling plan of type SkSP-V by considering the double-sampling plan as the reference plan. The design parameters are determined so as to minimize the average sample number while the specified producer's risk and the consumer's risks are satisfied. The tables are constructed by considering the various combinations of acceptable and limiting quality levels, and an example is given for illustration purpose. The advantages of the proposed plan over the conventional double-sampling plan are also discussed. [ABSTRACT FROM AUTHOR]

The importance of monitoring and assessment to the management and protection of wetlands has become widely recognized. Wetland assessments are made at multiple intensity levels based on data quality, the application scale, available resources, and the level of effort involved in data collection. We used multiple regression to calibrate the output of a rapid assessment method to condition scores derived from a comprehensive assessment. Data collected at riverine, depression, and flat wetlands in the Nanticoke and Inland Bays watersheds of Delaware and Maryland were used to calibrate the Delaware Rapid Assessment Protocol to an Index of Wetland Condition (IWC) derived from hydrogeomorphic variables. The resulting calibrated rapid condition score was highly correlated with the intense IWC score in each wetland type ( r = .88 for riverine, r = .82 for depressions and flats). The calibration methodology was also robust with respect to different statistical cutoff values and model selection methods. Our approach provides a quantitative alternative to using best professional judgment to determine rapid assessment scoring coefficients. Additionally, when rapid and comprehensive methods are correlated, double sampling, a statistical sampling method, can be used to increase the overall sample size resulting in either increased precision of condition estimates or lower sampling costs. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating the population mean using information on an auxiliary variable in presence of non-response. Some modified ratio, product and regression estimators in double sampling have been suggested and their properties are studied. It is shown that to the first degree of approximation, estimators based on estimated optimum values have the same variance as that of the optimum estimators. An empirical study is carried to judge the merits of the suggested estimators over conventional unbiased estimator and other known estimators. Both theoretical and empirical study results present the soundness and usefulness of the suggested estimators in practice. [ABSTRACT FROM AUTHOR]

Assessing the spatial structure of abundance of a species is a basic requirement to carry out adequate conservation strategies. However, existing attempts to predict species abundance, particularly in absolute units and on large scales, are scarce and have led to weak results. In this work we present a scheme to obtain, in an affordable way, a predictive model of absolute animal abundance on large scales based on the modelling of data obtained from local ecological knowledge (LEK) and its calibration. To exemplify this scheme, we build and validate a predictive absolute abundance model of the endangered terrestrial tortoise Testudo graeca in Southeast Iberian Peninsula. For that purpose, we collected distribution and relative abundance data of T. graeca using a low cost methodology, such as LEK, by means of interviewing shepherds. The information from LEK was employed to build a predictive habitat-based model of relative abundance. The relative abundance model was transformed into an absolute abundance model by means of calibration with a classical absolute abundance sampling method such as distance sampling. The obtained absolute abundance model predicted the observed absolute abundances values well in independent locations when compared with other works (R2 = 36%) and thus can offer a cost-effective predictive ability. Our results show that reliable habitatbased predictive maps of absolute species abundance on regional scales can be obtained starting from low cost sampling methods of relative abundance, such as LEK, and its calibration. [ABSTRACT FROM AUTHOR]

The article discusses a study that estimates the proportion of closely related species through a two-phase ratio estimator. It evaluates the performance of the estimator based on factors, including the relative costs of classification, the phenotypic classification accuracy for the target and nontarget species and magnitude of the target species proportion. Moreover, the method was assessed under which it could be recommended.

In this paper, we consider the double sampling (DS) $$\overline{X} $$ control chart for monitoring processes in which the observations can be represented as a first-order autoregressive moving average (ARMA(1, 1)) model. The properties of the DS $$\overline{X} $$ control chart with the sampling intervals driven by the rational subgroup concept are studied and compared with the Shewhart chart and the variable sample size (VSS) chart, both properly modified to account for the serial correlation. Numerical results show that the correlation within subgroups has a significant impact on the properties of the charts. For processes with low to moderate correlation levels, the DS $$\overline{X} $$ chart is substantially more efficient in detecting process mean shifts. [ABSTRACT FROM AUTHOR]

Abstract: The construction of a confidence interval based on a Bayesian approach is considered for the population proportion with double sampled data subject to false-positive misclassification. The Bayesian confidence intervals are compared with a standard frequentist confidence interval in terms of coverage probability and expected width. It is shown that a noninformative Bayes approach provides a relatively simple but effective confidence interval. [Copyright &y& Elsevier]

A postgrouped sampling is considered for estimating the (finite or finite), population mean. Double sampling and an empirical-weighted estimator is used. Unbiasedness, variance and efficiency are considered. Its properties are discussed allowing the simple random sampling with replacement (SRSWR) design in the first phase, and in each stratum for the second phase. It is shown that for a fixed sample size in each postgroup, the variance of the proposed estimator with less prior information is asymptotically equivalent to the usual stratified estimator for fixed allocation. Some examples are provided for natural populations., The method is also extended to simple random sampling without replacement (SRSWOR) design in the first phase, and in each stratum for the second phase. Unbiased variance estimation is provided for both types of sampling designs. [ABSTRACT FROM AUTHOR]

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and φ-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum φ-divergence estimators are considered and some of their properties are established. The proposed φ-divergence test statistics are obtained by calculating φ-divergences between probability density functions and by replacing parameters by their minimum φ-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example. [ABSTRACT FROM AUTHOR]

Adaptive cluster sampling (ACS) is an adaptive sampling scheme which operates under the rule that when the observed value of an initially selected sampling unit satisfies some condition of interest,C, other additional units in some pre-defined accompanying neighborhood are also added to the sample. In turn, if any of these additional units satisfyC, then their corresponding unit neighborhoods are added to the sample as well, and so on. This process stops when no additional units satisfyingCare encountered. This paper will provide a review of the major developments and issues in ACS since its introduction by Thompson (1990) [Journal of the American Statistical Association,85, 1050-1059]. [ABSTRACT FROM AUTHOR]

Double sampling scheme is used when cheap auxiliary variables may be measured to improve the estimation of a finite population parameter. Several estimators for population mean, ratio of means and variance are available, when two dependent samples are drawn. However, there are few proposals for the case of independent samples. In this paper both cases of dependent and independent samples are dealt with. A general approach for estimating a finite population parameter is given, showing that all the proposed estimators are particular cases of the same general class. The minimum variance bound for any estimator in this class is provided (at the first order of approximation). Furthermore, an optimal estimator which reaches this minimum is found. [ABSTRACT FROM AUTHOR]

Postulating a super-population regression model connecting a size variable, a cheaply measurable variable and an expensively observable variable of interest, an asymptotically optimal double sampling strategy to estimate the survey population total of the third variable is specified. To render it practicable, unknown model-parameters in the optimal estimator are replaced by appropriate statistics. The resulting generalized regression estimator is then shown to have a model-cum-asymptotic design based expected square error equal to that of the asymptotically optimum estimator itself. An estimator for design variance of the estimator is also proposed. [ABSTRACT FROM AUTHOR]

An optimal interpolation problem is considered on the Sobolev-Wiener class of smooth functions defined on the real line by double samples. We calculate the exact value of the minimal intrinsic error, identify an optimal set of sampling points and constructing an optimal linear estimator (algorithm). [ABSTRACT FROM AUTHOR]