Showing papers on "Sampling (statistics) published in 1993"

Statistics for Analytical Chemistry

Abstract: Introduction. Errors in Classical Analysis - Statistics of Repeated Measurements. Significance Tests. Quality Control and Sampling. Errors in Instrumental Analysis. Regression and Correlation. Non-parametric and Robust Methods. Experimental Design. Optimization and Pattern Recognition. Solutions to Exercises. Appendix 1: Summary of Statistical Tests. Appendix 2: Statistical Tests.

Estimating the Number of Species: A Review

Abstract: How many kinds are there? Suppose that a population is partitioned into C classes. In many situations interest focuses not on estimation of the relative sizes of the classes, but on estimation of C itself. For example, biologists and ecologists may be interested in estimating the number of species in a population of plants or animals, numismatists may be concemed with estimating the number of dies used to produce an ancient coin issue, and linguists may be interested in estimating the size of an author's vocabulary. In this article we review the problem of statistical estimation of C. Many approaches have been proposed, some purely data-analytic and others based in sampling theory. In the latter case numerous variations have been considered. The population may be finite or infinite. If finite, samples may be taken with replacement (multinomial sampling) or without replacement (hypergeometric sampling), or by Bernoulli sampling; if infinite, sampling may be multinomial or Bernoulli, or the sample may be th...

The Role of Sampling Weights when Modeling Survey Data

Abstract: Summary The purpose of this paper is to provide a critical survey of the literature, directed at answering two main questions. i) Can the use of the sampling weights be justified for analytic inference about model parameters and if so, under what circumstances? ii) Can guidelines be developed for how to incorporate the weights in the analysis? The general conclusion of this study is that the weights can be used to test and protect against informative sampling designs and against misspecification of the model holding in the population. Six approaches for incorporating the weights in the inference process are considered. The first four approaches are intended to yield design consistent estimators for corresponding descriptive population quantities of the model parameters. The other two

Advances in Shannon's Sampling Theory

Abstract: Introduction and a Historical Overview. Shannon Sampling Theorem and Band-Limited Signals. Generalizations of Shannon Sampling Theorems. Sampling Theorems Associated with Sturm-Liouville Boundary-Value Problems. Sampling Theorems Associated with Self-Adjoint Boundary-Value Problems. Sampling by Using Green's Function. Sampling Theorems and Special Functions. Kramer's Sampling Theorem and Lagrange-Type Interpolation in N Dimensions. Sampling Theorems for Multidimensional Signals-The Feichtinger-Grochenig Sampling Theory. Frames and Wavelets: A New Perspective on Sampling Theorems.

Generalized Raking Procedures in Survey Sampling

Abstract: We propose the name generalized raking for the class of procedures developed in this article, because the classical raking ratio of W. E. Deming is a special case. Generalized raking can be used for estimation in surveys with auxiliary information in the form of known marginal counts in a frequency table in two or more dimensions. An important property of the generalized raking weights is that they reproduce the known marginal counts when applied to the categorical variables that define the frequency table. Our starting point is a class of distance measures and a set of original weights in the form of the standard sampling weights 1/π k , where π k is the inclusion probability of element k. New weights are derived by minimizing the total distance between original weights and new weights. The article makes contributions in three areas: (1) statistical inference conditionally on estimated cell counts, (2) simple calculation of variance estimates for the generalized raking estimators, and (3) presen...

The Effectiveness of Random, Consecutive Day and Constructed Week Sampling in Newspaper Content Analysis:

Abstract: This study compares 20 sets each of samples of four different sizes (n=7, 14, 21 and 28) using simple random, constructed week and consecutive day samples of newspaper content. Comparisons of sampl...

Sampling of Populations: Methods and Applications

Abstract: Tables. Boxes. Figures. Getting Files from the Wiley ftp and Internet Sites. List of Data Sites Provides on Web Site. Preface to the Fourth Edition. Part 1: Basic Concepts. 1. Use of Sample Surveys. 2. The Population and the Sample. Part 2: Major Sampling Designs and Estimation Procedures. 3. Simple Random Sampling. 4. Systematic Sampling. 5. Stratification and Stratified Random Sampling. 6. Stratified Random Sampling: Further Issues. 7. Ratio Estimation. 8. Cluster Sampling: Introduction and Overview. 9. Simple One-Stage Cluster Sampling. 10. Two-Stage Cluster Sampling: Clusters Sampled with Equal Probability. 11. Cluster Sampling in Which Clusters Are Sampled with Unequal Probability: Probability Proportional to Size Sampling. 12. Variance Estimation in Complex Sample Surveys. Part 3: Selected Topics in Sample Survey Methodology. 13. Nonresponse and Missing Data in Sample Surveys. 14. Selected Topics in Sample Design and Estimation Methodology. 15. Telephone Survey Sampling (Michael W. Link and Mansour Fahimi). 16. Constructing the Survey Weights (Paul P. Biemer and Sharon L. Christ). 17. Strategies for Design-Based Analysis of Sample Survey Data. Appendix. Answers to Selected Exercises. Index.

Sampling Variability of Performance Assessments.

Abstract: In this article, performance assessments are cast within a sampling framework. More specifically, a performance assessment is viewed as a sample of student performance drawn from a complex universe defined by a combination of all possible tasks, occasions, raters, and measurement methods. Using generalizability theory, we present evidence bearing on the generalizability and convergent validity of performance assessments sampled from a range of measurement facets and measurement methods. Results at both the individual and school level indicate that task-sampling variability is the major source ofmeasurment error. Large numbers of tasks are needed to get a reliable measure of mathematics and science achievement at the elementary level. With respect to convergent validity, results suggest that methods do not converge. Students' performance scores, then, are dependent on both the task and method sampled.

The Effect of Sample Size on Estimated Limits for and X Control Charts

Abstract: The results of this study indicate that charts with subgroups of size n require about 400/(n-1) samples, and X charts require about 300 values to estimate control limits that perform like known limits. The results also indicate that using estimated con..

Sampling Methods for Multiresource Forest Inventory

Abstract: Focus, Fundamental Concepts, and Theory. Probabilistic Sampling Strategies. Forest Sampling--Single Level. Multi-Information Sources for Sampling. Model-Based Inference. Mensurational Aspects of Forest Inventory. Related Sampling Topics. Related Estimation Topics. Future Directions in Multiresource Sampling in Forestry. References. Answers to the Problems. Index.

Extended Kalman Filter Based Nonlinear Model Predictive Control

Abstract: This paper formulates a nonlinear model predictive control algorithm based on successive linearization. The extended Kalman filter (EKF) technique is used to develop multi-step prediction of future states. The prediction is shown to be optimal under an affine approximation of the discrete state / measurement equations (obtained by integrating the nonlinear ODE model) made at each sampling time. Connections with previously available successive linearization based MPC techniques by Garcia (NLQDMC, 1984) and Gattu & Zafiriou (1992) are made. Potential benefits and shortcomings of the proposed technique are discussed using a bilinear control problem of paper machine.

Application of sampling methodologies to network traffic characterization

Abstract: The relative performance of different data collection methods in the assessment of various traffic parameters is significant when the amount of data generated by a complete trace of a traffic interval is computationally overwhelming, and even capturing summary statistics for all traffic is impractical. This paper presents a study of the performance of various methods of sampling in answering questions related to wide area network traffic characterization. Using a packet trace from a network environment that aggregates traffic from a large number of sources, we simulate various sampling approaches, including time-driven and event-driven methods, with both random and deterministic selection patterns, at a variety of granularities. Using several metrics which indicate the similarity between two distributions, we then compare the sampled traces to the parent population. Our results revealed that the time-triggered techniques did not perform as well as the packet-triggered ones. Furthermore, the performance differences within each class (packet-based or time-based techniques) are small.

Locating 2-D bar codes

Abstract: After a two-dimensional bar code is scanned to provide a stored image, the location (i.e., bounds, including orientation) of the bar code image within the scanned area image must be determined to enable decoding. Methods for locating the bar code image include the steps of sampling the stored image, identifying sampling traversals of bar code start and stop patterns, and correlating the identified start and stop pattern traversals to a common bar code image. The correlating data is then used to identify a nominally rectangular area bounding the bar code image. A bounding box may be identified as the smallest area capable of encompassing all of the start and stop pattern traversals related to a common bar code image. Output location data may specify the coordinates of the four corners of a bounding box for use in subsequent decoding. Related systems are described.

Advanced topics in Shannon sampling and interpolation theory

Abstract: "Advanced Topics in Shannon Sampling and Interpolation Theory" is the second volume of a textbook on signal analysis solely devoted to the topic of sampling and restoration of continuous time signals and images. Sampling and reconstruction are fundamental problems in any field that deals with real-time signals or images, including communication engineering, image processing, seismology, speech recognition, and digital signal processing. This second volume includes contributions from leading researchers in the field on such topics as Gabor's signal expansion, sampling in optical image formation, linear prediction theory, polar and spiral sampling theory, interpolation from non-uniform samples, an extension of Papoulis' generalized sampling expansion to higher dimensions, and applications of sampling theory to optics and to time-frequency representations.

Visual and manual demands of in-car controls and displays

Abstract: This is a study of the visual and manual demands of in-car controls and displays. It begins with a discussion of conceptual models of in-car tasks. It includes task classification, the relationship to cognitive load, and elementary sampling models of the drier. This is followed by a literature review of supporting sampling models. The study concludes with remedies and recommendations to reduce visual load.

Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs

Abstract: This paper focuses on Benders decomposition techniques and Monte Carlo sampling (importance sampling) for solving two-stage stochastic linear programs with recourse, a method first introduced by Dantzig and Glynn [7]. The algorithm is discussed and further developed. The paper gives a complete presentation of the method as it is currently implemented. Numerical results from test problems of different areas are presented. Using small test problems, we compare the solutions obtained by the algorithm with universe solutions. We present the solutions of large-scale problems with numerous stochastic parameters, which in the deterministic formulation would have billions of constraints. The problems concern expansion planning of electric utilities with uncertainty in the availabilities of generators and transmission lines and portfolio management with uncertainty in the future returns.

Application of repeated measures designs in environmental impact and monitoring studies

Abstract: Traditional environmental studies have employed sampling at different times, but based on re-randomized ‘replicate’ samples taken at each time. For example, in a 4 year monitoring study of near-shore marine benthic communities there might be three box cores collected annually at each of three depths along each of three transects. Repeated measures designs, long used in medicine and the social sciences, are based on resampling replicates (e.g. sites) at a series of times. In such designs spatial sampling variability is not used for tests of environmental impact. Error for such tests is based on variability of time trends among similar sites (similar with respect to impact). For example in a tropical oil spill study five oiled and five unoiled coral reefs were studied over 5 years. Error for tests of oil impact was based on variability among reefs (within degree-of-oiling category) in the year-to-year trends of biological response variables. It was not based on variability among field samples within reefs at given times. The two approaches (univariate and multivariate) to repeated measures analysis of variance are described. The pros and cons of each are discussed, as are the assumptions and consequences of their violations. Emphasis is especially placed on the adequacy of error degrees of freedom in the two approaches, and some exploration of power to detect impact is presented. Examples of application of repeated measures designs to various impact and monitoring studies are presented and discussed, including (i) interpretation of significant effects; (ii) decomposition of effects by contrasts (e.g. before vs after impact); and (iii) modelling time trends by polynomial and cosine functions.

Irregular sampling and frames

Abstract: . A theory of irregular sampling is developed for the class of real sampling sequences for which there are L 2 -convergent sampling formulas. The sampling sequences are effectively characterized, and the formulas are accompanied by methods of computing coefficients. These sampling formulas depend on the theory of coherent state (Gabor) frames and an analysis of the inverse frame operator. The results include regular sampling theory and the irregular sampling theory of Paley-Wiener, Levinson, Beutler, and Yao-Thomas. The chapter also presents a new aliasing technique, perspective on stability and uniqueness, and references to recent contributions by others.

Monitoring airborne fungal spores in an experimental indoor environment to evaluate sampling methods and the effects of human activity on air sampling

Abstract: Aerobiological monitoring was conducted in an experimental room to aid in the development of standardized sampling protocols for airborne microorganisms in the indoor environment. The objectives of this research were to evaluate the relative efficiencies of selected sampling methods for the retrieval of airborne fungal spores and to determine the effect of human activity on air sampling. Dry aerosols containing known concentrations of Penicillium chrysogenum spores were generated, and air samples were taken by using Andersen six-stage, Surface Air System, Burkard, and depositional samplers. The Andersen and Burkard samplers retrieved the highest numbers of spores compared with the measurement standard, an aerodynamic particle sizer located inside the room. Data from paired samplers demonstrated that the Andersen sampler had the highest levels of sensitivity and repeatability. With a carpet as the source of P. chrysogenum spores, the effects of human activity (walking or vacuuming near the sampling site) on air sampling were also examined. Air samples were taken under undisturbed conditions and after human activity in the room. Human activity resulted in retrieval of significantly higher concentrations of airborne spores. Surface sampling of the carpet revealed moderate to heavy contamination despite relatively low airborne counts. Therefore, in certain situations, air sampling without concomitant surface sampling may not adequately reflect the level of microbial contamination in indoor environments.

Verifying Hybrid Systems

Abstract: Hybrid systems are modeled as phase transition systems with sampling semantics. By identifying a set of important events it is ensured that all significant state changes are observed, thus correcting previous drawbacks of the sampling computations semantics. A proof rule for verifying properties of hybrid systems is presented and illustrated on several examples.

Performance of the macroinvertebrate community index: effects of sampling method, sample replication, water depth, current velocity, and substratum on index values

Abstract: The influences of sampling method, water depth, current velocity, and substratum on two macroinvertebrate‐based biotic indices were investigated based upon 523 samples from 55 stony riffle sites on 23 New Zealand streams. A single hand‐net sample estimated the site Macroinvertebrate Community Index (MCI) within ± 15% and four replicates yielded ± 10%. Between 8 and 10 replicate Surber samples produced ± 10% precision. Quantitative MCI (QMCI) values were more variable, with 10 or 11 replicate Surber samples required for ± 10% precision. Two procedures for detection of statistically significant differences between paired MCI or QMCI values are described. The detectable difference method (equal sample sizes) is preferred for statistical reasons but a f‐test method can be used for unequal sample sizes. MCI and QMCI were relatively independent of depth, velocity, and substratum within the sampled ranges of these variables. This is a major advantage for the assessment of water pollution or enrichment u...

Statistical Process Control—Theory and Practice

Abstract: Part 1 Statistical process control: development of SPC what SPC is and is not online SPC methods off-line process control SPC methodolgy other factors affecting the success of SPC. Part 2 Some basic distributions: attribute data countable data geometric distribution the Normal distribution distributions derived from the Normal distribution application of results testing for normality the Normal approximation to the binomial distribution Normal approximation to the Poisson distribution. Part 3 Process variation: reasons for process variation types of process variation some models for process variation sampling error and measurement error. Part 5 Basic Shewhart control charts for continuous variables: control charts for average level charts for control of (within group) process spread the average run length (ARL) special problems some theoretical results for Shewhart charts charts for control of process spread. Part 6 Extensions to Shewhart charts for one-at-a-time data: one-at-a-time sampling estimation of sigma for one-at-a-time data details of further control charts for control of process average level control of process spread choice of charting method practical use of Shewhart and moving average charts properties of EWMA and MA charts. Part 7 Cumulative sum techniques for continuous variables: CuSum charts - for control of average level, for control of process spread nomogram for CuSums. Part 8 Further theoretical results on control charts for continuous variables: the effect of departures from assumption on moments of x and s(2) Shewhart charts - Markov chain approach cumulative Sum charts charts for control of process spread. Part 9 The design of control charts for specification limits: single specification limits - chart for means double specification limits - high capability processes, an alternative approach. Part 10 Control of discrete data processes: Shewhart charts - for countable data (c and u), for attribute data (np and p) CuSum charts for countable data, for attribute data comparison of Shewhart and CuSum schemes. Part 11 Sampling inspection: classification of inspection plans some properties of sampling plans methods of using sampling plans for attributes. Part 12 Inspection by variables: single specification limit - sigma known, sigma unknown estimation of fraction non-conforming, single specification limit double specification limit - sigma known, sigma unknown multivariate sampling plans. Part 13 Standard sampling systems: statement of method for inspection by attributes inspection by variables International Standards for process and quality control. Part 14 Adaptive sampling plans: CSP-1 and the AOQL criterion theory of CSP-1 the AEDL criterion. (Part contents).

Sampling to Detect Rare Species

Abstract: Often a sampling program has the objective of detecting the presence of one or more species. One night wish to obtain a species list for the habitat, or to detect the presence of a rare and possibly endangered species. How can the sampling effort necessary for the detection of a rare species can be determined? The Poisson and the negative binomial are two possible spatial distributions that could be assumed. The Poisson assumption leads to the simple relationship n = -(1/m)log @b, where n is the number of quadrats needed to detect the presence of a species having density m, with a chance @b (the Type 2 error probability) that the species will not be collected in any of the n quadrats. Even if the animals are not randomly distributed the Poisson distribution will be adequate if the mean density is very low (i.e., the species is rare, which we arbitrarily define as a true mean density of 0.95. Only 8 of the 273 cases represented rare species that failed this requirement. Thus we conclude that a Poisson-based estimate of necessary sample size will generally be adequate and appropriate.