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Manufacturers and government agencies frequently use acceptance sampling to decide whether a lot from a supplier or exporting country should be accepted or rejected. International standards on acceptance sampling provide sampling plans for specific circumstances.The aim of this package is to provide an easy-to-use interface to visualize single, double or multiple sampling plans. In addition, methods have been provided to enable the user to assess sampling plans against pre-specified levels of performance, as measured by the probability of acceptance for a given level of quality in the lot.

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]

Detectability issues create uncertainty in field surveys of animal and plant populations. Detectability correction is one method employed to deal with this problem when there is reasonable certainty that detectability is roughly constant with time or in different areas. Two new reduced-variance estimators of detectability are introduced and evaluated for the case of using a detectability correction for new areas that are surveyed only once. The new estimates are unbiased or nearly unbiased and produce population estimates with smaller variance than the Lincoln–Petersen estimate. [ABSTRACT FROM AUTHOR]

We consider data with a nominal grouping variable and a binary response variable. The grouping variable is measured without error, but the response variable is measured using a fallible device subject to misclassification. To achieve model identifiability, we use the double-sampling scheme which requires obtaining a subsample of the original data or another independent sample. This sample is then classified by both the fallible device and another infallible device regarding the response variable. We propose two Wald tests for testing the association between the two variables and illustrate the test using traffic data. The Type-I error rate and power of the tests are examined using simulations and a modified Wald test is recommended. [ABSTRACT FROM AUTHOR]

Population estimates, often difficult to acquire, warrantee the use of an index as an economical substitute for rapid assessments of populations. We estimated population size of the little known social, semi-fossorial Indian desert jird ( Meriones hurrianae) in Kachchh, Gujarat, India under closed population capture-mark-recapture (CMR) framework to calibrate a burrow count index for the species. A total of 147 individuals were trapped in 16 colonies using baited Sherman traps and the number of burrow entrances at each colony was recorded. Data from colonies with low number of captures were pooled to estimate capture probability using Huggins heterogeneity models with gender, site, body weight and age category as covariates in Program MARK. Colony sizes ranged from 2 to 46 individuals. The number of burrow entrances was calibrated against CMR-based population estimates using least squares regression ( n = 16, adjusted R = 0.96, t = 18.18, P < 0.001). The index was further validated using Jackknife (JK) analysis where JK-predicted population estimates strongly correlated with CMR estimates ( r = 0.96, P < 0.001). In habitats and climatic conditions similar to Kachchh and within the range of colony sizes sampled, our calibrated index can be a valuable and effective tool for large scale surveys of the desert jird, which occupies a keystone trophic level in the semi-arid ecosystem. [ABSTRACT FROM AUTHOR]

An 11-b 300-MS/s double sampling pipelined ADC with on-chip digital calibration for memory effects is presented. In double-sampling pipelined ADC architecture, memory effect of residual charge occurs due to sharing an op-amp between two channels of pipelined ADC. The proposed foreground calibration technique removes the memory effect error in digital domain without additional analog circuit. Thus, the technique simplifies the analog circuits, which extends the operation speed over 300∼MHz. The chip is fabricated in a 40 nm CMOS and occupies 0.42 mm^2 including digital calibration logic. The ADC consumes 40 mW from a 1.8 V supply, and FoM is 0.24-pJ/conversion-step. [ABSTRACT FROM AUTHOR]

This study, carried out for the United States Bureau of Reclamation (BOR), demonstrated methods for surveying large landscapes using small-scale, habitat-based stratification, a common problem that has heretofore received little attention. The goal was to design a sampling plan for detecting change in the density of breeding birds of 6 species occurring along the Colorado River around and south of Lake Mead in the southwestern United States. The main problem in designing the study was that the focal species were concentrated in small, irregularly shaped patches of habitat. We partitioned the study area into >15,000 plots configured to enclose the high-quality habitat in the fewest possible plots with the constraint that plots could be surveyed in 1 morning by 1 person. Because of the irregular plot shapes and extremely dense vegetation, we used area searches to carry out the surveys. We used double-sampling, including a large sample of plots surveyed with a rapid method and a subsample of plots surveyed intensively, to estimate detection ratios. A simulation study helped allocate effort between rapid and intensive surveys and indicated that conducting 80 surveys per year would achieve high power to detect a 50% decline occurring during 20 years. This is one of the first studies to show how large landscapes can be sampled using small-scale stratification so that effort can be concentrated in the habitats of greatest interest. © 2012 The Wildlife Society. [ABSTRACT FROM AUTHOR]

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: Spatially patchy populations present challenges for precise estimation of abundance from surveys, which typically result in high estimation errors compared to surveys of more evenly distributed species. In this study, we used simulations to evaluate the performance of the Trawl and Acoustic Presence/Absence Survey design (TAPAS) in reducing the variability in estimated biomass. This approach is a double sampling design in which high-density patches observed in a first phase using hydroacoustics are subsequently more intensively sampled (relative to non-patch areas) in a second phase using trawls and area-swept methods. Information on Gulf of Alaska Pacific ocean perch (Sebastes alutus), a patchily distributed rockfish species, was used to parameterize the simulations. The performance of the TAPAS design depends upon the degree to which high acoustic backscatter represents “patch” areas of high density and variability, as the relationship between backscatter and abundance of a given species can be affected by areas unavailable for sampling (i.e., the “dead zone”) and the contribution of multiple species to the backscatter intensity. With a strong relationship between backscatter intensity and density, the TAPAS design resulted in improved precision compared to simple random sampling (SRS). Additionally, more intensive sampling of the patches occurred when areas of high backscatter intensity were randomly distributed over space rather than located in spatially correlated clusters. However, with a weak relationship between backscatter intensity and density, the precision of the TAPAS design was not substantially larger than SRS. The potential improvement in precision when a strong relationship exists between backscatter intensity and fish density offers motivation to continue to refine relationships between underlying fish density, acoustic backscatter, and trawl catches. [Copyright &y& Elsevier]

Using data from the USDA Forest Service's Forest Inventory and Analysis (FIA) program, we developed a mapping tool for displaying statistical forestland information. To develop this technique, we used data from Nevada's pinyon-juniper dryland forests, a major component of Nevada's forest cover important for food and wood sources, wildlife cover, and carbon storage. Land managers must grapple with issues, such as the health of these forests, the explanation of their encroachment into grasslands and shrublands, and causes of pinyon mortality associated with drought and bark-beetle activity. Our methodology con assist managers by offering the potential to map FIA statistics, such as carbon, volume, and stand structure to local conditions. FIA plots were classified according to the basal area occupied by dominant species groups. Employing this classification, we modeled the probability of pinyon-juniper occurrence at stand scale in Nevada from FIA data, using readily available remote sensing and geographic information system variables. Then, using the model with a Nevada-specific classification rule, we produced a cover mop for the state. Finally, we linked our model classification rule to FIA summary data to create a detailed statistical map of Nevada forest carbon distribution-a visual representation of FIA data on the landscape. [ABSTRACT FROM AUTHOR]

In this paper, some exponential type estimators have been proposed for estimating the finite population mean in double sampling using information on two auxiliary variables. The estimators have been discussed for independent and dependent samples under simple random sampling without replacement. The expressions for mean square errors and biases have been derived. An empirical study has been carried out to compare the efficiency of proposed estimators with some available estimators in literature. [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]

Investigating the prevalence of a disease is an important topic in medical studies. Such investigations are usually based on the classification results of a group of subjects according to whether they have the disease. To classify subjects, screening tests that are inexpensive and nonintrusive to the test subjects are frequently used to produce results in a timely manner. However, such screening tests may suffer from high levels of misclassification. Although it is often possible to design a gold-standard test or device that is not subject to misclassification, such devices are usually costly and time-consuming, and in some cases intrusive to the test subjects. As a compromise between these two approaches, it is possible to use data that are obtained by the method of double-sampling. In this article, we derive and investigate four test statistics for testing a hypothesis on disease prevalence with double-sampling data. The test statistics are implemented through both the asymptotic method suitable for large samples and approximate unconditional method suitable for small samples. Our simulation results show that the approximate unconditional method usually produces a more satisfactory empirical type I error rate and power than its asymptotic counterpart, especially for small to moderate sample sizes. The results also suggest that the score test and the Wald test based on an estimate of variance with parameters estimated under the null hypothesis outperform the others. An real example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

Combining information from two or more independent surveys is a problem frequently encountered in survey sampling. We consider the case of two independent surveys, where a large sample from survey 1 collects only auxiliary information and a much smaller sample from survey 2 provides information on both the variables of interest and the auxiliary variables. We propose a model-assisted projection method of estimation based on a working model, but the reference distribution is design-based. We generate synthetic or proxy values of a variable of interest by first fitting the working model, relating the variable of interest to the auxiliary variables, to the data from survey 2 and then predicting the variable of interest associated with the auxiliary variables observed in survey 1. The projection estimator of a total is simply obtained from the survey 1 weights and associated synthetic values. We identify the conditions for the projection estimator to be asymptotically unbiased. Domain estimation using the projection method is also considered. Replication variance estimators are obtained by augmenting the synthetic data file for survey 1 with additional synthetic columns associated with the columns of replicate weights. Results from a simulation study are presented. [ABSTRACT FROM PUBLISHER]

This paper presents a 0.5-V 1.5-bit double-sampled \Delta\Sigma modulator for audio applications. Unlike existing double-sampled designs, the proposed double-sampled \Delta\Sigma modulator employs an input-feedforward topology to reduce internal signal swings, thereby relaxing design requirements for the low-voltage building blocks and reducing distortion. Moreover, in order to avoid instability and noise shaping degradation, the proposed architecture restores the noise transfer function (NTF) of the double-sampled modulator to its single-sampled equivalent with the help of compensation loops. In the circuit implementation, the proposed fully-differential amplifier adopts an inverter output stage and a common-mode feedback (CMFB) circuit with a global feedback loop in order to reduce power consumption. A resistor-string-reference switch matrix based on a direct summation quantizer is used to simplify the analog compensation loop. The chip prototype has been fabricated in a 0.13-\mum CMOS technology with a core area of 0.57 \mm^2. The measured results show that when operating from a 0.5-V supply and clocked at 1.25∼MHz, the modulator achieves a peak signal-to-noise and distortion ratio (SNDR) of 81.7 dB, a peak signal-to-noise ratio (SNR) of 82.4 dB and a dynamic range (DR) of 85.0 dB while consuming 35.2\ \mu\W for a 20-kHz signal bandwidth. [ABSTRACT FROM AUTHOR]

Abstract: To meet Kyoto Protocol obligations, New Zealand is required to estimate forest carbon stock change over the first commitment period (2008–2012). New Zealand has three subcategories of forest, namely: Natural forest; Pre-1990 forest; and Post-1989 forest. The Post-1989 forest carbon inventory undertaken in 2008 used discrete return airborne LiDAR and ground-based measurements of 0.06ha circular plots located on a 4-km×4-km grid. The national carbon stock estimate was based on a double sampling scheme consisting of 246 plots from which both ground and LiDAR data were obtained, supplemented with 46 additional plots assessed using only LiDAR. This paper describes the relationships established between carbon stocks estimated using ground-based measurements and LiDAR metrics. A regression model explaining 74% of the variation in total carbon was developed using LiDAR 30th percentile height (P30ht) and canopy cover (%Cover). The regression estimator improved the precision of the national carbon stock estimate in 2008 by 6% compared to the ground-based estimate. This inventory indicates that it is possible to reduce the cost of obtaining carbon stock estimates to a specified level of precision using a combination of ground-based and LiDAR measurements in a double sampling approach. The theoretical maximum improvement in precision expected in 2012, when additional LiDAR data are expected to be available, is 50–55%. [Copyright &y& Elsevier]

Misclassified binary data result from using a fallible classifier for classifying units into two categories. If an infallible classifier is also available, a random subsample of this misclassified data can be further classified using the infallible classifier. For such data, the existing methods for exact confidence interval are too conservative, and the existing Bayesian credible interval suffers from computational difficulty. We derive a closed-form Bayesian algorithm which draws a posterior sample of the proportion parameter from the exact marginal posterior distribution. Our simulations show that our Bayesian algorithm is easy to implement and has nominal coverage. [ABSTRACT FROM AUTHOR]

We consider two-sample binary data consisting of two independent studies. One study is the main study where individuals are classified using a fallible classifier prone to error; the other study is a validation study where individuals are classified using both the fallible classifier and a gold standard which does not misclassify individuals. For such data, we propose a Bayesian model for making statistical inference for all model parameters and particularly the risk difference. We derive a closed-form algorithm for sampling from the posterior distribution. We then illustrate our algorithm using a real data example and conduct Monte Carlo simulation studies to show that our algorithm performs very well under various scenarios. [ABSTRACT FROM PUBLISHER]

Using double sampling technique, a generalized difference-cum-ratio type estimator has been proposed for estimating the population mean. The expressions for bias and mean square error of proposed estimator have been obtained. The conditions under which proposed estimator is better than the regression estimator and mean per unit estimator have also been obtained. The gain in efficiency over the existing ones have been illustrated numerically as well as graphically. Simulation study has also been done to support the results by generating data. [ABSTRACT FROM AUTHOR]

For estimating the population variance of variable under study, a generalized difference-cum-ratio type estimator has been proposed. Expressions for bias and mean square error have been obtained using random sampling at both the phases. The expressions have also been derived using simple random sampling at both the phases as a special case. Then the comparison has been made with the regression-type estimator and sample variance. Results have also been illustrated numerically and graphically. [ABSTRACT FROM AUTHOR]

Vertical point sampling has seen relatively little use in practical forestry, in part because existing field techniques are difficult. We show how vertical point sampling can be implemented quickly and easily using a camera. We give tables and equations for calculating the height-squared factor, which plays a role similar to that of the basal area factor in horizontal point sampling. Some suggestions for choosing a height-squared factor are discussed, along with potential applications for further exploration. We illustrate the technique using a case study in southern Maine. Direct estimates with no statistically detectable bias were obtained using height-squared factors greater than 3. The results also suggested that the technique could be used as a correlate in double sampling for variables such as cubic volume, stand density index, and biomass, and possibly board foot volume as well. [ABSTRACT FROM AUTHOR]

We consider Bayesian point and interval estimation for a risk ratio of two proportion parameters using two independent samples of binary data subject to misclassification. In order to obtain model identifiability, we apply a double sampling scheme. For the identifiable model, we propose a Bayesian method for statistical inference for a two proportion risk ratio. Specifically, we derive an easy-to-implement closed-form sampling algorithm to draw from the posterior distribution of interest. We demonstrate the efficiency of our algorithm for Bayesian inference via Monte Carlo simulation studies and using a real data example. [ABSTRACT FROM AUTHOR]

In this article, we propose a double-sampling (DS) np control chart. We assume that the time interval between samples is fixed. The choice of the design parameters of the proposed chart and also comparisons between charts are based on statistical properties, such as the average number of samples until a signal. The optimal design parameters of the proposed control chart are obtained. During the optimization procedure, constraints are imposed on the in-control average sample size and on the in-control average run length. In this way, required statistical properties can be assured. Varying some input parameters, the proposed DS np chart is compared with the single-sampling np chart, variable sample size np chart, CUSUM np and EWMA np charts. The comparisons are carried out considering the optimal design for each chart. For the ranges of parameters considered, the DS scheme is the fastest one for the detection of increases of 100% or more in the fraction non-conforming and, moreover, the DS np chart is easy to operate. [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]

The article presents a study which examines the total number of nesting Rusty Blackbirds (Euphagus carolinus) in boreal wetland habitats in Anchorage and Tanana Flats in Alaska. The study uses double sampling, and statistical analyses to estimate the number of pairs of nesting Rusty Blackbirds in Anchorage and Tanana Flats in 2007 and 2008. The result shows that habitat selection has been easily detected in Anchorage than Tanana Flats.

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]

This paper presents a low-power 10-bit 200 MS/s pipelined ADC in a 90 nm CMOS technology with 1 V supply voltage. To decrease the power dissipation efficiently, a new architecture using a combination of two power reduction techniques named double-sampling and opamp-sharing has been used to reduce the power consumption significantly, without any degradation in the performance of the ADC. In addition, the stage scaling technique has been applied to the ADC efficiently, and two-stage class A/AB and class A amplifiers and dynamic comparators have been used in sample and hold and sub-ADCs. According to HSPICE simulation results, the 10-bit 200 MSample/s pipeline ADC with a 9.375 MHz, 1-VP-P,diff input signal in a 90 nm CMOS process achieves a SNDR of 58.5 dB while consuming only 30.9 mW power from a 1 V supply voltage. [ABSTRACT FROM AUTHOR]

Shabbir and Gupta (2007) introduced an exponential ratio-type estimator for the population variance and compare its mean square error with mean square error of some of the existing estimators. But unfortunately, the expression of mean square error of their proposed estimator is incorrect and hence the comparison based on incorrect mean square error, carried by them, is wrong. In the present article, the author provides the correct expression of the mean square error, up to first order of approximation, of the same estimator. A comparison has been made by taking the corrected expression of mean square error. The corresponding exponential ratio-type estimator for the population variance, under double sampling technique, is also proposed which followed by a comparison. [ABSTRACT FROM AUTHOR]

With the relatively recent widespread availability of operational fine spatial resolution imagery from satellites, there is more opportunity to conduct spatial sampling with combinations of spatial resolution data. This method is generally called regression estimation. Regression estimation consists of first using a coarser spatial resolution sensor over a large area. For selected samples, a second delineation is done with a finer spatial resolution system which is presumed to be more accurate. A statistical comparison of the two estimates for the selected sample areas is made and if a suitable relationship exists, a correction factor can be determined and applied to the full study area. This manuscript reviews the regression estimation process and provides a case study. The case study used the technique to determine the extent of the winter rice crop in Bangladesh with Landsat data and statistics provided for local administrative units by agricultural agents. [ABSTRACT FROM AUTHOR]

The article presents a study on the probability of detection of nests and its significance to survey design. The researchers used a double sampling method to predict closely complete counts of birds in the intensively surveyed plots by calculating the probability of detecting active shorebird nests. Results show that the probability varies considerably by species and it suggests that the method is likely to be met for bird's nests that survive over several visits.

Consider independent observations X1, X2,... having a common normal probability density function [image omitted] with - ∞ < x < ∞ and unknown variance σ2 ( > 0). We propose to estimate f(x;σ2) by a plug-in maximum likelihood (ML) two-stage estimator under the mean integrated squared error (MISE) loss function. Our goal is to make the associated risk not to exceed a preassigned positive number c, referred to as the risk-bound. Because no fixed-sample-size methodology would handle this estimation problem, we design an appropriate two-stage estimation methodology that is shown to satisfy the asymptotic (i) first-order efficiency property (Theorem 2.1), (ii) first-order risk-efficiency property (Theorem 2.2), as well as (iii) second-order efficiency property (Theorem 2.3). The performances of the proposed methodology for small, moderate, and large sample sizes are examined with the help of simulations. We have noticed some limited robustness of the proposed methodology under mixture-normal population densities, in which case the asymptotic second-order efficiency property (Theorem 3.1) is shown. Illustrations are included with real data and analysis. Overall, we feel that the proposed two-stage plug-in density estimation methodology performs remarkably well. [ABSTRACT FROM AUTHOR]

The present study deals with the use of high-resolution satellite imagery in forest inventory of the open savanna woodlands of southern Africa. The study was carried out in Hans Kanyinga Community Forest, north-eastern Namibia. A two-phase (double) sampling design was used to estimate the variables of interest, namely stand volume, stand density and diameter distribution. QuickBird satellite images were used to extract auxiliary variables (image data), such as photogrammetric crown diameter and number of stems, using visual interpretation and measuring tools offered by Erdas 8.7 geographic imaging software. Field inventory data (terrestric data) collected in 2002 were used to obtain the terrestric data. Stepwise regression and correlation analysis between satellite imagery and field data was carried out and the results were moderate. The stand volume model explained 56%, the stand density model 81% and the diameter distribution model 43% of the variation. Cost-efficiency assessment of double sampling with regression estimators showed a considerable reduction in inventory costs by up to 24% compared to a traditional forest inventory method in Hans Kanyinga Community Forest. Double sampling was found to be cost-efficient when the proportion of variability of the models was higher than 27%. The inventory concept showed potential in providing information required both at the higher and lower levels of forest management. Follow up research is required. [ABSTRACT FROM AUTHOR]

In 2007, there were 33.2 million people around the world living with HIV/AIDS ( UNAIDS/WHO, 2007 ). In May 2003, the U.S. President announced a global program, known as the President's Emergency Plan for AIDS Relief (PEPFAR), to address this epidemic. We seek to estimate patient mortality in PEPFAR in an effort to monitor and evaluate this program. This effort, however, is hampered by loss to follow-up that occurs at very high rates. As a consequence, standard survival data and analysis on observed nondropout data are generally biased, and provide no objective evidence to correct the potential bias. In this article, we apply double-sampling designs and methodology to PEPFAR data, and we obtain substantially different and more plausible estimates compared with standard methods (1-year mortality estimate of 9.6% compared to 1.7%). The results indicate that a double-sampling design is critical in providing objective evidence of possible nonignorable dropout and, thus, in obtaining accurate data in PEPFAR. Moreover, we show the need for appropriate analysis methods coupled with double-sampling designs. [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.