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We derive and examine the interval width and coverage properties of an integrated‐likelihood‐ratio confidence interval for the binomial parameter p using a double‐sampling scheme. The data consist of a relatively large fallible sample containing underreported data and a relatively small infallible subsample. Via Monte Carlo simulations, we determine that the new integrated‐likelihood‐ratio interval estimator displays slightly conservative to moderately conservative coverage properties for small to medium sample sizes and can have shorter average‐interval width than two previously proposed confidence intervals when p < 0.10 or p > 0.90. We also apply the integrated‐likelihood‐ratio confidence interval to a real‐data set and determine that the integrated‐likelihood‐ratio interval has superior performance when contrasted to two properties of two competing confidence intervals. [ABSTRACT FROM AUTHOR]

Ratio estimators have been used in survey sampling for estimating population mean when the population mean of an auxiliary variable is known. However, as the auxiliary information is not always available we cannot gain any benefit from using ratio estimators to increase the efficiency of the population mean estimator. New ratio estimators for population mean obtained using the quartile function of an auxiliary variable using double sampling have been proposed based upon the modified ratio estimators proposed by Subramani and Kumarapandiyan (2012). We propose the use of double sampling in order to estimate the unknown quartile function of an auxiliary variable. A simulation study has been conducted to compare these new ratio estimators with the classical ratio estimator using a percent relative efficiency using double sampling. The results show that the proposed estimators perform better than the classical ratio estimator using double sampling.

This paper proposes an exponential estimator of a population mean under double sampling using one auxiliary variable. The proposed estimator is modified from the estimator of Khatua and Misha (2013) and Ozgul and Cingi (2014). The bias and mean square error of the proposed estimator are derived under the second order of approximation. The efficiency of the proposed estimator is compared with the existing estimators based on mean square error. The results indicate that the proposed estimator is more efficient than others under empirical study with real datasets.

Double sampling technique and control charts are used for predicting about unknown parameters of the big population and developing algorithms for imposing control over growth factor. This sampling procedure has two approaches like sub-sample and independent sample. Aim is to estimate mean filesize by both and to find out which approach is better in big data setup. Comparative mathematical tools used herein are mean squared error, confidence interval, relative confidence interval length measure and control charts of digital file-size for monitoring. Estimation strategies are proposed and confidence intervals are computed over multiple points of time. At each time, it was found that confidence intervals are catching the true values. First kind of approach (as case I) of double sampling found better than the second. A new simulation strategy is proposed who is observed efficient for comparison purpose. Single-valued simulated confidence intervals are obtained using the new simulation strategy and found covering the truth in its range. As an application of outcomes, control charts are developed to monitor the parametric growth over long duration. Upper and Lower control limits are drawn for business managers to keep a watch on digital file-size estimates whether their growth under control? Outcomes may be extended for reliability evaluation under discrete time domain. The content herein is a piece of thought, idea and analysis developed by deriving motivation from past references to handle big data using double sampling. Findings of the study can be used for developing software based monitoring system using process control charts for managers. [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]

Milk production is an important aspect of the dairy animals. The present study was conducted to estimate the effect of different diseases on per day milk yield in the buffaloes. Data from a sample of 1,214 livestock owners in two divisions of Uttar Pradesh namely Agra and Aligarh were collected. The value of reduction in per day milk yield due to different diseases under two different situations, viz. when strata weights were known and when strata weights were not known was estimated. The estimated value of reduction in milk yield under the situation when strata weights were known was 4.28 kg with variance 0.014 kg2. Mastitis is a disease which has a large impact on milk yield in buffaloes. But under the situation when strata weights were unknown and were estimated by double sampling, post stratification and published sources, the estimated value of average reduction in per day milk yield due to different diseases were 4.33 kg, 4.33 kg and 4.35 kg with variance 0.015 kg2, 0.017 kg2 and 0.012 kg2 respectively. In buffaloes, the best estimated value of average reduction in milk was provided by double sampling estimator.

In this study, an economic-statistical design of the double sampling (DS) s chart is investigated. The optimal design parameters of the DS s chart are obtained by minimizing the cost subject to statistical constraints. The effectiveness of the DS s chart is evaluated by comparing its minimized cost against the cost of the Shewhart s chart. The comparison demonstrates the superiority of the DS s chart over the Shewhart s chart, where the DS s chart is economically superior to the Shewhart s chart. Sensitivity analyses are performed to investigate the effect of the cost parameters, process parameters and statistical properties on the design parameters, as well as the corresponding in-control average time to signal, out-of-control average time to signal and cost.

In many applications, the quality of a process is characterized by the functional relationship between a response variable and one or more explanatory variables. This functional relationship is referred to as a profile. We extend two existing methods for monitoring the shift of slope of linear profiles. We consider the single sampling (SS) and the double sampling (DS) scheme for the economic-statistical design (ESD) model of profile monitoring in Phase II. The results of a numerical example indicate the DS β ^ 1 control chart has better performance than the DS T 2 control chart when ρ β ^ 0 , β ^ 1 is low. Comparison of the ESD of DS β 1 ^ and the SD of DS β 1 ^ control charts show that an ESD has an improvement over the pure SD. Also, we compare the ESD of the DS β 1 ^ with the ESD of the SS β 1 ^ control chart. The results show that the DS β 1 ^ control charts are more efficient in terms of minimizing the E (L). [ABSTRACT FROM AUTHOR]

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets. [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]

Control charts based on inspection with gauges are an interesting alternative to monitor quantitative variables when measurement is complex or expensive. The main advantage over variable control charts lies in greater operational simplicity and economy. However, gauge-based control charts are usually less powerful in order to detect shifts in the process than variables control charts, when the same sample size is used. To improve its performance, the use of double sampling on gauge-based monitoring is proposed. The new chart shows better performance than X ¯ , S and X ¯ − S charts, for the same average sample size, keeping low sampling cost. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Field Ornithology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is costly to collect the household- and individual-level data that underlie official estimates of poverty and health. For this reason, developing countries often do not have the budget to update estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data by means of double sampling, where the expensive outcome variable is collected for a subsample and its predictors for all. This study finds that double sampling yields only modest reductions in financial costs when imposing a statistical precision constraint in a wide range of realistic empirical settings. There are circumstances in which the gains can be more substantial, but these denote the exception rather than the rule. The recommendation is to rely on real data whenever there is a need for new data and to use prediction estimators to leverage existing data. [ABSTRACT FROM AUTHOR]

This paper presents a 2-2 multi-stage-noise-shaping (MASH) ΔΣ analog-to-digital converter (ADC), in which an integrator with dual double sampling (DDS) technique and stability compensation is proposed to save the area of capacitors without increasing the thermal noise. In addition, a fast-charge correlated-level-shifting (CLS) input buffer is also proposed to improve the linearity with acceptable power consumption. The buffer and the ADC are fabricated in a 0.18-μm CMOS process with an active area of 2.41 mm2, with 3∼5V supply voltage for the input buffer and 1.8-V power supply for the ADC, the overall ADC chip achieves a signal-to-noise-and-distortion ratio (SNDR) of 103.3 dB, a spurious-free dynamic range (SFDR) of 113.2 dB and an integral nonlinearity (INL) of ±2.5 ppm/full-scale range (FSR). The overall power consumption is 14.88 mW. [ABSTRACT FROM AUTHOR]

Publications in peer-reviewed journals are often looked upon as tenets on which future scientific thought is built. Published information is not always flawless and errors in published research should be expediently reported, preferably by a peer-review process. We review a recent publication by Gopalaswamy et al. (10.1111/2041-210X.12351) that challenges the use of “double sampling” in large-scale animal surveys. Double sampling is often resorted to as an established economical and practical approach for large-scale surveys since it calibrates abundance indices against absolute abundance, thereby potentially addressing the statistical shortfalls of indices. Empirical data used by Gopalaswamy et al. (10.1111/2041-210X.12351) to test their theoretical model, relate to tiger sign and tiger abundance referred to as an Index-Calibration experiment (IC-Karanth). These data on tiger abundance and signs should be paired in time and space to qualify as a calibration experiment for double sampling, but original data of IC-Karanth show lags of (up to) several years. Further, data points used in the paper do not match the original sources. We show that by use of inappropriate and incorrect data collected through a faulty experimental design, poor parameterization of their theoretical model, and selectively picked estimates from literature on detection probability, the inferences of this paper are highly questionable. We highlight how the results of Gopalaswamy et al. were further distorted in popular media. If left unaddressed, the paper of Gopalaswamy et al. could have serious implications on statistical design of large-scale animal surveys by propagating unreliable inferences.

An economic-statistical design of the synthetic double sampling (synDS) T2 chart is presented in this study. The cost function is minimized to obtain the optimal design parameters of the synDS T2 chart by incorporating the statistical constraints or the constraints on the average number of samples. An example is provided and a sensitivity analysis is conducted to study the effect of model parameters on the optimal solution of the design. The numerical comparison shows that the synDS T2 chart performs better than the synthetic T2 chart and the multivariate exponentially weighted moving average chart, in terms of the cost. [ABSTRACT FROM AUTHOR]

Many clinical or prevention studies involve missing or censored outcomes. Maximum likelihood (ML) methods provide a conceptually straightforward approach to estimation when the outcome is partially missing. Methods of implementing ML methods range from the simple to the complex, depending on the type of data and the missing-data mechanism. Simple ML methods for ignorable missing-data mechanisms (when data are missing at random) include complete-case analysis, complete-case analysis with covariate adjustment, survival analysis with covariate adjustment, and analysis via propensity-to-be-missing scores. More complex ML methods for ignorable missing-data mechanisms include the analysis of longitudinal dropouts via a marginal model for continuous data or a conditional model for categorical data. A moderately complex ML method for categorical data with a saturated model and either ignorable or nonignorable missing-data mechanisms is a perfect fit analysis, an algebraic method involving closed-form estimates and variances. A complex and flexible ML method with categorical data and either ignorable or nonignorable missing-data mechanisms is the method of composite linear models, a matrix method requiring specialized software. Except for the method of composite linear models, which can involve challenging matrix specifications, the implementation of these ML methods ranges in difficulty from easy to moderate. [ABSTRACT FROM AUTHOR]

Publications in peer-reviewed journals are often looked upon as tenets on which future scientific thought is built. Published information is not always flawless and errors in published research should be expediently reported, preferably by a peer-review process. We review a recent publication by Gopalaswamy et al. (10.1111/2041-210X.12351) that challenges the use of "double sampling" in largescale animal surveys. Double sampling is often resorted to as an established economical and practical approach for large-scale surveys since it calibrates abundance indices against absolute abundance, thereby potentially addressing the statistical shortfalls of indices. Empirical data used by Gopalaswamy et al. (10.1111/2041-210X.12351) to test their theoretical model, relate to tiger sign and tiger abundance referred to as an Index-Calibration experiment (IC-Karanth). These data on tiger abundance and signs should be paired in time and space to qualify as a calibration experiment for double sampling, but original data of IC-Karanth show lags of (up to) several years. Further, data points used in the paper do not match the original sources. We show that by use of inappropriate and incorrect data collected through a faulty experimental design, poor parameterization of their theoretical model, and selectively picked estimates from literature on detection probability, the inferences of this paper are highly questionable. We highlight how the results of Gopalaswamy et al. were further distorted in popular media. If left unaddressed, the paper of Gopalaswamy et al. could have serious implications on statistical design of large-scale animal surveys by propagating unreliable inferences. [ABSTRACT FROM AUTHOR]

This paper describes an avalanche photodetector (APD)-based optical receiver, applicable to the burst-mode operation, in 28-nm CMOS technology. With the aims of benefiting the overall optical link power efficiency and link bandwidth, the optical receiver is designed to have high sensitivity and high reconfiguration speed for burst-mode operation. The sensitivity of the receiver is optimized by adjusting the responsivity of APD via its reverse bias voltage, which leads to the highest signal-to-noise ratio (SNR) at the front end. The two-tap feed-forward equalization (FFE), along with two-tap decision feedback equalization, is implemented in a current-integrating fashion to further improve the sensitivity with superior power efficiency to their resistively loaded counterparts. Integrating dc comparator and integrating amplitude comparator are proposed to replace the conventional RC low-pass filters and peak detectors, respectively, in extracting the information of dc offset and signal amplitude within two unit intervals (UIs), empowering significant acceleration of the burst-mode reconfiguration. When the APD is biased at −16 V, its overall responsivity at 1310 nm is 4 A/W, and the optical receiver achieves bit-error-rate (BER) better than 10−12 at −16-dBm optical modulation amplitude, 2.24-ns reconfiguration time with 5-dB dynamic range, and 1.37-pJ/b energy efficiency at 25 Gb/s. [ABSTRACT FROM AUTHOR]

Calibration approach provides efficient estimators of population parameters utilizing auxiliary information. In this paper, a new calibration estimator for population mean has been proposed in stratified random sampling using known coefficient of skewness of the auxiliary variable in defining the calibration constraint. The results so obtained are also extended to stratified double sampling case. A simulation study has also been carried out on real as well as artificial datasets generated from various probability distributions like Gamma, Chi-square and Weibull distributions for various values of the parameters. The proposed estimators are compared with the estimator developed by Tracy et al. (2003) and found more efficient for all considered datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a face tracking algorithm based on NTSS and compressed sensing. It uses different search radius and step double sampling, and uses NTSS search method as the target detection method. The algorithm is tested by the international standard face database AFW and MALF video sequences, and the central distance mean algorithm is used to compare the compressed sensing algorithm with the face tracking algorithm based on NTSS compressed sensing.The results are that the face tracking algorithm can reduce the error by 59. 01%. In addition, the NTSS compressed sensing face tracking algorithm has better robustness in face occlusion re-tracking, face geometric transformation and under dark conditions. [ABSTRACT FROM AUTHOR]

Ratio estimators have been used in survey sampling for estimating population mean when the population mean of an auxiliary variable is known. However, as the auxiliary information is not always available we cannot gain any benefit from using ratio estimators to increase the efficiency of the population mean estimator. New ratio estimators for population mean obtained using the quartile function of auxiliary variable( s) using double sampling have been proposed based upon the modified ratio estimators proposed by Subramani and Kumarapandiyan (2012). We propose the use of double sampling in order to estimate an unknown quartile function of an auxiliary variable( s) . A simulation study has been conducted to compare these new ratio estimators with the classical ratio estimator using a percent relative efficiency using double sampling. The results show that the proposed estimators perform better than the classical ratio estimator using double sampling. [ABSTRACT FROM AUTHOR]

This paper proposes an exponential estimator of a population mean under double sampling using one auxiliary variable. The proposed estimator is modified from the estimator of Khatua and Misha (2013) and Ozgul and Cingi (2014). The bias and mean square error of the proposed estimator are derived under the second order of approximation. The efficiency of the proposed estimator is compared with the existing estimators based on mean square error. The results indicate that the proposed estimator is more efficient than others under empirical study with real datasets. [ABSTRACT FROM AUTHOR]

Since the run length distribution is generally highly skewed, a significant concern about focusing too much on the average run length (ARL) criterion is that we may miss some crucial information about a control chart’s performance. Thus it is important to investigate the entire run length distribution of a control chart for an in-depth understanding before implementing the chart in process monitoring. In this paper, the percentiles of the run length distribution for the double sampling (DS) X chart with estimated process parameters are computed. Knowledge of the percentiles of the run length distribution provides a more comprehensive understanding of the expected behaviour of the run length. This additional information includes the early false alarm, the skewness of the run length distribution, and the median run length (MRL). A comparison of the run length distribution between the optimal ARL-based and MRL-based DS X chart with estimated process parameters is presented in this paper. Examples of applications are given to aid practitioners to select the best design scheme of the DS X chart with estimated process parameters, based on their specific purpose.

A CMOS (Complementary metal-oxide-semiconductor) Hall sensor with low power consumption and simple structure is introduced. The tiny magnetic signal from Hall device could be detected by a high-resolution delta-sigma ADC in presence of offset and flickering noise. Also, the offset as well as the flickering noise are effectively suppressed by the current spinning technique combined with double sampling switches of the ADC. The double sampling scheme of the ADC reduces the operating frequency and helps to reduce the power consumption. The prototype Hall sensor is fabricated in a 0.18-µm CMOS process, and the measurement shows detection range of ±150 mT and sensitivity of 110 µV/mT. The size of active area is 0.7 mm2, and the total power consumption is 4.9 mW. The proposed system is advantageous not only for low power consumption, but also for small sensor size due to its simplicity.

A chain ratio-type estimator for the ratio of two - population means in double sampling is developed in this paper. The bias and mean squared error of the developed estimator has been derived. The developed estimator has been compared with relevant existing estimators and conditions under which the developed estimator would be better in terms of efficiency than other considered estimators have been obtained. [ABSTRACT FROM AUTHOR]

Dash and Mishra [1] suggested an improved class of estimators without defining the optimum estimator. However, they gave the wrong Taylor’s series expression of their class of estimator and their minimum mean squared error expressions are also incorrect. Here we show that Ahmed et al.’s [2] class of chain estimators is more efficient than Dash and Mishra’s [1], with minimum mean squared error.

Values that are too large or small enough can be found in many data sets. Therefore, the estimator can yield ambiguous findings if several of the incredible deals are picked for the sample. When such extreme values occur, we propose improved estimators to determine the finite population means using double sampling based on probability proportional to size sampling (PPS). The properties of estimators are obtained up to the first order of approximations. When the size of the units varies widely, the PPS sampling technique may be employed. To determine the values of Pi when using PPS, we must be acquainted with the aggregate of the auxiliary variable X i . However the designs and estimation techniques we have looked at so far are unsuccessful and are less effective when this information is difficult to locate or when other information is missing. The two-phase approach is preferable and more feasible in these kinds of circumstances. To demonstrate how effectively the recommended estimators performed, we used three actual data sets. We show mathematically and theoretically that the suggested estimators outperform alternative estimators., Competing Interests: The authors declare that they have no known cometing finincial intersts or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

In this article, we propose a method of planning and determining the optimum parameters of a SkSP-R skip-lot sampling plan by using the attribute double sampling plan as the reference plan. The SkSP-R plan is a new type of skip-lot sampling plan which has a provision for re-inspecting the submitted lots. The optimal plan parameters of the suggested sampling plan are estimated with the target that the average sample number be minimized and satisfying both the specified producer's as well as the consumer's risks simultaneously. In order to obtain the optimum parameters, tables are also built for different combinations of the acceptable quality level and the limiting quality level in conjunctions with different producer's and consumer's risks. An illustrative example is provided for the implementation of the suggested plan. The advantages of the suggested plan over the existing conventional sampling plans and other existing skip-lot sampling plans are also described. [ABSTRACT FROM AUTHOR]

The present article deals with the estimation of mean number of respondents who possess a rare sensitive character in presence of known and unknown proportion of a rare unrelated non-sensitive attribute by using the Poisson probability distribution in stratified random sampling as well as in stratified random double sampling. The variance of rare sensitive character is also derived under proportional and optimal allocation methods in stratified random sampling when stratum sizes are known and unknown. The properties of the suggested estimation procedures have been deeply examined. The proposed model is found to be dominant over Lee et al. [Estimation of a rare sensitive attribute in a stratified sample using Poisson distribution. Statistics. 2013;47:575-589] model. Numerical illustrations are presented to support the theoretical results. Results are analysed and suitable recommendations are put forward to the survey practitioners. [ABSTRACT FROM AUTHOR]

Abstract: In this article, two adaptive multivariate charts, which combine the double sampling (DS) and variable sampling interval (VSI) features, called the adaptive multivariate double sampling variable sampling interval T2 (AMDSVSI T2) and the adaptive multivariate double sampling variable sampling interval combined T2 (AMDSVSIC T2) charts, are proposed. The real purpose of using the proposed charts is to provide flexibility by enabling the sampling interval length of the DS T2 chart to be varied so that the chart's sensitivity can be enhanced. The fundamental difference between the two proposed charts is that when a second sample is taken, the AMDSVSI T2 chart uses the information of the combined sample mean vectors while the AMDSVSIC T2 chart uses the information of the combined T2 statistics, in deciding about the process status. This research is motivated by existing combined DS X ¯ and VSI X ¯ charts in the literature, which show convincing performance improvement over the standard DS X ¯ chart. Consequently, it is believed that adopting this existing approach in the multivariate case will enable superior multivariate DS charts to be proposed. Numerical results show that the proposed charts outperform the existing standard T2 and other adaptive multivariate charts, in detecting shifts in the mean vector, for the zero‐state and steady‐state cases. The performances of both charts when the shift sizes in the mean vector are unknown are also measured. The application of the AMDSVSI T2 chart is illustrated with an example. [ABSTRACT FROM AUTHOR]

A side-sensitive modified group runs double sampling (SSMGRDS) chart for detecting mean shifts is proposed. The average number of observations to signal (ANOS) and expected average number of observations to signal (EANOS) performances of the SSMGRDS, , double sampling (DS), side-sensitive group runs double sampling (SSGRDS), side-sensitive modified group runs (SSMGR) and exponentially weighted moving average (EWMA) charts are compared. For the zero-state case, the SSMGRDS chart outperforms all charts under comparison, for all sizes of mean shifts. For the steady-state case, the EWMA chart prevails for small and moderate mean shifts but the SSMGRDS chart prevails for large shifts. [ABSTRACT FROM AUTHOR]

Zero-inflated models have been used successfully to describe microbial data with excess of zero-counts. Some sampling plans such as single sampling plan (SSP), resubmitted single sampling plan (RSSP) and quick switching system (QSS) have been developed for the zero-inflated Poisson (ZIP) distribution. Two new plans such as repetitive group sampling (RGS) and multiple dependent state (MDS) sampling are proposed to compare their performance with the existing sampling plans. Additionally, we develop double sampling (DS) and sequential sampling plans for the ZIP data. The plan parameters of the sampling plans are determined by using the unity value approach. The MDS sampling plan is more economical than the other sampling plans, including SSP, RSSP, RGS and QSS, in terms of the required sample size and average sample number in most cases. The probability of a zero in the ZIP data affects the sample size required for all sampling plans. Practical guidance for selecting sampling plans under ZIP is also provided. [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]

This study presents a control chart for monitoring shifts in the covariance matrix of a multivariate normally distributed process. This chart combines the double sampling, variable sample size and variable sampling interval features, and is called the DSVSSI |S| chart. A Markov chain approach is developed to design the DSVSSI |S| chart, by minimizing the average time to signal (ATS), for a specified shift size in the covariance matrix. The DSVSSI |S| chart has a better ATS performance compared to other existing charts. An example is given to illustrate the operation of the DSVSSI |S| chart. [ABSTRACT FROM AUTHOR]