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Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2019 / This research study has demonstrated the complexity involved in complex survey sample design (CSSD). Furthermore the study has proposed methods to account for each step taken in sampling and at the estimation stage using the theory of survey sampling, CSSD-based case studies and practical implementation based on census attributes. CSSD methods are designed to improve statistical efficiency, reduce costs and improve precision for sub-group analyses relative to simple random sample(SRS).They are commonly used by statistical agencies as well as development and aid organisations. CSSDs provide one of the most challenging fields for applying a statistical methodology. Researchers encounter a vast diversity of unique practical problems in the course of studying populations. These include, interalia: non-sampling errors,specific population structures,contaminated distributions of study variables,non-satisfactory sample sizes, incorporation of the auxiliary information available on many levels, simultaneous estimation of characteristics in various sub-populations, integration of data from many waves or phases of the survey and incompletely specified sampling procedures accompanying published data. While the study has not exhausted all the available real-life scenarios, it has outlined potential problems illustrated using examples and suggested appropriate approaches at each stage. Dealing with the attributes of CSSDs mentioned above brings about the need for formulating sophisticated statistical procedures dedicated to specific conditions of a sample survey. CSSD methodologies give birth to a wide variety of approaches, methodologies and procedures of borrowing the strength from virtually all branches of statistics. The application of various statistical methods from sample design to weighting and estimation ensures that the optimal estimates of a population and various domains are obtained from the sample data.CSSDs are probability sampling methodologies from which inferences are drawn about the population. The methods used in the process of producing estimates include adjustment for unequal probability of selection (resulting from stratification, clustering and probability proportional to size (PPS), non-response adjustments and benchmarking to auxiliary totals. When estimates of survey totals, means and proportions are computed using various methods, results do not differ. The latter applies when estimates are calculated for planned domains that are taken into account in sample design and benchmarking. In contrast, when the measures of precision such as standard errors and coefficient of variation are produced, they yield different results depending on the extent to which the design information is incorporated during estimation. The literature has revealed that most statistical computer packages assume SRS design in estimating variances. The replication method was used to calculate measures of precision which take into account all the sampling parameters and weighting adjustments computed in the CSSD process. The creation of replicate weights and estimation of variances were done using WesVar, astatistical computer package capable of producing statistical inference from data collected through CSSD methods. Keywords: Complex sampling, Survey design, Probability sampling, Probability proportional to size, Stratification, Area sampling, Cluster sampling.

Data that can be represented as a network, where there are measurements both on units and on pairs of units, are becoming increasingly prevalent in the social sciences and public health. Homophily in network data, or the tendency of units to connect based on similar nodal attribute values (i.e. income, HIV status) more often than expected by chance is receiving strong attention from researchers in statistics, medicine, sociology, public health and others. Respondent-Driven Sampling (RDS) is a link-tracing network sampling strategy heavily used in public health worldwide that is cost efficient and allows us to survey populations inaccessible by conventional techniques. Via extensive simulation we study the performance of existing methods of estimating population averages, and show that they have poor performance if there is homophily on the quantity surveyed. We propose the first model-based approach for this setting and show its superiority as a point estimator and in terms of uncertainty intervals coverage rates, and demonstrate its application to a real life RDS-based survey. We study how the strength of homophily effects can be estimated and compared across networks and different binary attributes under several network sampling schemes. We give a proof that homophily can be effectively estimated under RDS and propose a new homophily index. This work moves towards a deeper understanding of network structure as a function of nodal attributes and network sampling under homophily. / Statistics

Thesis (M.S.)--University of Michigan, 1975.

Thesis (M.S.)--University of Michigan, 1975.

Master of Science / Department of Statistics / Perla E. Reyes Cuellar / The term network has become part of our everyday vocabulary. The more popular are perhaps the social ones, but the concept also includes business partnerships, literature citations, biological networks, among others. Formally, networks are defined as sets of items and their connections. Often modeled as the mathematic object known as a graph, networks have been studied extensively for several years, and research is widely available. In statistics, a variety of modeling techniques and statistical terms have been developed to analyze them and predict individual behaviors. Specifically, certain statistics like degree distribution, clustering coefficient, and so on are considered important indicators in traditional social network studies. However, while conventional network models assume that the whole network population is known, complete information is not always available. Thus, different sampling methods are often required when the population data is inaccessible. Less time has been dedicated to studying the accuracy of these sampling methods to produce a representative sample. As such, the aim of this report is to identify the capacity of sampling techniques to reflect the features of the original network. In particular, we study Anti-cluster Respondent Driven Sampling (AC-RDS). We also explore whether standard modeling techniques paired with sample data could estimate statistics often used in the study of social networks. Respondent Driven Sampling (RDS) is a chain referral approach to study rare and/or hidden populations. Originating from the link-tracing design, RDS has been further developed into a series of methods utilized in social network studies, such as locating target populations or estimating the number and proportion of needle-sharing among drug addicts. However, RDS does not always perform as well as expected. When the social network contains tight communities (or clusters) with few connections between them, traditional RDS tends to oversample one community, introducing bias. AC-RDS is a special Markov chain process that collects samples across communities, capturing the whole network. With special referral requests, the initial seeds are more likely to refer to the individuals that are outside their communities. In this report, we fitted the Exponential Random Graph Model (ERGM) and a Stochastic Block Model (SBM) to an empirical study of the Facebook friendship network of 1034 participants. Then, given our goal of identifying techniques that will produce a representative sample, we decided to compare two version of AC-RDSs, in addition to traditional RDS, with Simple Random Sampling (SRS). We compared the methods by drawing 100 network samples using each sampling technique, then fitting an SBM to each sample network we used the results to project the network into one of population size. We calculated essential network statistics, such as degree distribution, of each sampling method and then compared the result to the original network observed statistics.

Ph.D. / A lot of chemicals are used in agriculture to increase production per cost. Unfortunately most of these chemicals find their way into the final agricultural product or get washed off into other systems where they may pose health and environmental concerns. As such monitoring of these compounds where they are not needed is necessary to avoid unwarranted pollution and the effects associated with them. This requires the use of analytical techniques and instrumentation. Analytical chemistry, especially in the area of sample preparation and clean-up, has shifted focus mainly on greener methods that produce only minute quantities of waste without sacrificing efficiency. This has led to the conception of a term QuEChERS (acronym for Quick, Easy, Cheap, Effective, Robust and Safe) to reflect this trend in sample preparation. Efforts have been made in this regard to reduce the amount of chemicals used; as a result miniaturised techniques have evolved. One such technique that is easiest and cheapest is solvent micro-extraction especially the single-drop format, wherein only a few micro-litres of an organic solvent are used to sample the aqueous solution with the same volume being transferred into the instrument for analysis. This study reports the advances made in development of a modified single-drop micro-extraction (SDME) technique through the deliberate introduction of an air-bubble to facilitate mass transfer. This is termed bubble-in-drop single-drop micro-extraction (BID-SDME). The method has been reported for the first time in our earlier work. This study started off with the validation of the method using triazine mixture (TP 619) as model herbicides. The method was validated for linearity (0.05 – 5 ng/mL), reproducibility and repeatability (%RSD < 10%), matrix effect, limits of detection (pg/mL range) and quantification as well as accuracy.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22476号 / 農博第2380号 / 新制||農||1074(附属図書館) / 学位論文||R2||N5256(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 中村 公人, 教授 星野 敏, 教授 藤原 正幸 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Rabies has been reported in Tanzania, mainly in the southern highland regions, since 1954. To date, rabies is endemic in all districts in Tanzania and efforts are being made to contain the disease. It was determined that mass vaccination of at least 70% of an animal population is most effective, in terms of profitability and cost, in reducing transmission of rabies. The current approach for vaccination in Tanzanian villages takes some features from the EPI method but is rather basic and unreliable. This mini-dissertation proposes using a sampling technique which incorporates the spatial component of the village data and minimises the walking distance between the sampled houses while ensuring the 70% coverage of the animal population. / Mini Dissertation(MSc)--University of Pretoria, 2017. / STATOMET The Centre for Artificial Intelligence Research (CAIR) National Research Foundation of South Africa (NRF CSUR grant number 90315) / Statistics / MSc / Unrestricted

Campylobacter and Salmonella are foodborne pathogens commonly associated with raw poultry. Although there has been much research done on isolating these pathogens from poultry production environments using cloacal swabs, fecal samples, intestinal tract contents and dissection, research involving environmental sampling has been limited. New and/or improved environmental sampling methods may provide an easy, convenient, and less time-consuming way to collect samples. Coupling these sampling methods with PCR may provide a relatively simple, rapid, and robust means of testing for foodborne pathogens in a chicken house or flock prior to slaughter. Air, boot and sponge samples were collected from three commercial chicken grow-out houses located in southwestern Virginia when flocks were three, four, and five weeks old. Air samples were collected onto gelatin filters. Fecal/litter samples were collected from disposable booties worn over investigator's protective shoe coverings. Pre-moistened sponges were used to sample house feed pans and water dispensers on drink lines. A PCR method was used to qualitatively detect Campylobacter jejuni and Salmonella spp. Campylobacter jejuni was detected at each farm (house), across all three ages (3, 4, and 5 weeks), and from each sample type. Salmonella was not detected in any of the environmental samples. For all 270 samples, 41% (110/270) were positive for Campylobacter. Collectively, 28% (25/90) of air, 44% (40/90) of sponge, and 50% (45/90) of bootie samples were positive for Campylobacter. The methods used in this study are non-invasive to live animals, relatively rapid and specific, and could enable poultry processing facilities to coordinate scheduled processing of flocks with lower pathogen incidence, as a way to reduce post-slaughter pathogen transmission. / Master of Science