Relevant bibliographies by topics / Hypothesis sampling

Academic literature on the topic 'Hypothesis sampling'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Hypothesis sampling"

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Givaruangsawat, Sumalee, Govinda J. Weerakkody, and Patrick D. Gerard. "Hypothesis Testing in Two-Stage Cluster Sampling." Australian New Zealand Journal of Statistics 40, no. 3 (September 1998): 335–44. http://dx.doi.org/10.1111/1467-842x.00037.

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Bernstein, Joseph, Kevin McGuire, and Kevin B. Freedman. "Statistical Sampling and Hypothesis Testing in Orthopaedic Research." Clinical Orthopaedics and Related Research 413 (August 2003): 55–62. http://dx.doi.org/10.1097/01.blo.0000079769.06654.8c.

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Paul, Jack W. "Explaining attributes-sampling concepts: A hypothesis-testing framework." Journal of Accounting Education 12, no. 1 (December 1994): 43–58. http://dx.doi.org/10.1016/0748-5751(94)90018-3.

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Perron, Pierre. "Test Consistency with Varying Sampling Frequency." Econometric Theory 7, no. 3 (September 1991): 341–68. http://dx.doi.org/10.1017/s0266466600004503.

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This paper considers the consistency property of some test statistics based on a time series of data. While the usual consistency criterion is based on keeping the sampling interval fixed, we let the sampling interval take any equispaced path as the sample size increases to infinity. We consider tests of the null hypotheses of the random walk and randomness against positive autocorrelation (stationary or explosive). We show that tests of the unit root hypothesis based on the first-order correlation coefficient of the original data are consistent as long as the span of the data is increasing. Tests of the same hypothesis based on the first-order correlation coefficient of the first-differenced data are consistent against stationary alternatives only if the span is increasing at a rate greater than T½, where T is the sample size. On the other hand, tests of the randomness hypothesis based on the first-order correlation coefficient applied to the original data are consistent as long as the span is not increasing too fast. We provide Monte Carlo evidence on the power, in finite samples, of the tests Studied allowing various combinations of span and sampling frequencies. It is found that the consistency properties summarize well the behavior of the power in finite samples. The power of tests for a unit root is more influenced by the span than the number of observations while tests of randomness are more powerful when a small sampling frequency is available.
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KNEZ, IGOR. "Interaction of data and hypotheses in probabilistic inference tasks: Rejection of the hypothesis sampling model?" Scandinavian Journal of Psychology 32, no. 1 (March 1991): 57–69. http://dx.doi.org/10.1111/j.1467-9450.1991.tb00853.x.

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Lifshits, Mikhail, and Michel Weber. "Sampling the Lindelöf Hypothesis with the Cauchy random walk." Proceedings of the London Mathematical Society 98, no. 1 (June 24, 2008): 241–70. http://dx.doi.org/10.1112/plms/pdn026.

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Bahr, R. K., and J. A. Bucklew. "Optimal sampling schemes for the Gaussian hypothesis testing problem." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 10 (1990): 1677–86. http://dx.doi.org/10.1109/29.60099.

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DWORIN, LOWELL, and RICHARD A. GRIMLUND. "A comprehensive hypothesis testing approach to dollar unit sampling." Contemporary Accounting Research 5, no. 2 (March 1989): 674–91. http://dx.doi.org/10.1111/j.1911-3846.1989.tb00733.x.

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Harrison, M. T. "Conservative hypothesis tests and confidence intervals using importance sampling." Biometrika 99, no. 1 (February 7, 2012): 57–69. http://dx.doi.org/10.1093/biomet/asr079.

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Arnoldsson, Göran. "OPTIMAL DESIGNS FOR BINOMIAL SAMPLING UNDER A MIXTURE HYPOTHESIS." Communications in Statistics - Theory and Methods 30, no. 5 (April 30, 2001): 897–911. http://dx.doi.org/10.1081/sta-100002265.

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Dissertations / Theses on the topic "Hypothesis sampling"

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Koziuk, Andzhey. "Re-sampling in instrumental variables regression." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/20869.

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Diese Arbeit behandelt die Instrumentalvariablenregression im Kontext der Stichprobenwiederholung. Es wird ein Rahmen geschaffen, der das Ziel der Inferenz identifiziert. Diese Abhandlung versucht die Instrumentalvariablenregression von einer neuen Perspektive aus zu motivieren. Dabei wird angenommen, dass das Ziel der Schätzung von zwei Faktoren gebildet wird, einer Umgebung und einer zu einem internen Model spezifischen Struktur. Neben diesem Rahmen entwickelt die Arbeit eine Methode der Stichprobenwiederholung, die geeignet für das Testen einer linearen Hypothese bezüglich der Schätzung des Ziels ist. Die betreffende technische Umgebung und das Verfahren werden im Zusammenhang in der Einleitung und im Hauptteil der folgenden Arbeit erklärt. Insbesondere, aufbauend auf der Arbeit von Spokoiny, Zhilova 2015, rechtfertigt und wendet diese Arbeit ein numerisches ’multiplier-bootstrap’ Verfahren an, um nicht asymptotische Konfidenzintervalle für den Hypothesentest zu konstruieren. Das Verfahren und das zugrunde liegende statistische Werkzeug wurden so gewählt und angepasst, um ein im Model auftretendes und von asymptotischer Analysis übersehenes Problem zu erklären, das formal als Schwachheit der Instrumentalvariablen bekannt ist. Das angesprochene Problem wird jedoch durch den endlichen Stichprobenansatz von Spokoiny 2014 adressiert.
Instrumental variables regression in the context of a re-sampling is considered. In the work a framework is built to identify an inferred target function. It attempts to approach an idea of a non-parametric regression and motivate instrumental variables regression from a new perspective. The framework assumes a target of estimation to be formed by two factors - an environment and an internal, model specific structure. Aside from the framework, the work develops a re-sampling method suited to test linear hypothesis on the target. Particular technical environment and procedure are given and explained in the introduction and in the body of the work. Specifically, following the work of Spokoiny, Zhilova 2015, the writing justifies and applies numerically 'multiplier bootstrap' procedure to construct confidence intervals for the testing problem. The procedure and underlying statistical toolbox were chosen to account for an issue appearing in the model and overlooked by asymptotic analysis, that is weakness of instrumental variables. The issue, however, is addressed by design of the finite sample approach by Spokoiny 2014.
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Tucker, Joanne M. (Joanne Morris). "Robustness of the One-Sample Kolmogorov Test to Sampling from a Finite Discrete Population." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278186/.

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One of the most useful and best known goodness of fit test is the Kolmogorov one-sample test. The assumptions for the Kolmogorov (one-sample test) test are: 1. A random sample; 2. A continuous random variable; 3. F(x) is a completely specified hypothesized cumulative distribution function. The Kolmogorov one-sample test has a wide range of applications. Knowing the effect fromusing the test when an assumption is not met is of practical importance. The purpose of this research is to analyze the robustness of the Kolmogorov one-sample test to sampling from a finite discrete distribution. The standard tables for the Kolmogorov test are derived based on sampling from a theoretical continuous distribution. As such, the theoretical distribution is infinite. The standard tables do not include a method or adjustment factor to estimate the effect on table values for statistical experiments where the sample stems from a finite discrete distribution without replacement. This research provides an extension of the Kolmogorov test when the hypothesized distribution function is finite and discrete, and the sampling distribution is based on sampling without replacement. An investigative study has been conducted to explore possible tendencies and relationships in the distribution of Dn when sampling with and without replacement for various parameter settings. In all, 96 sampling distributions were derived. Results show the standard Kolmogorov table values are conservative, particularly when the sample sizes are small or the sample represents 10% or more of the population.
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Michel, Frank [Verfasser], Carsten [Akademischer Betreuer] Rother, Stefan [Akademischer Betreuer] Gumhold, Carsten [Gutachter] Rother, and Carsten [Gutachter] Steger. "Hypothesis Generation for Object Pose Estimation From local sampling to global reasoning / Frank Michel ; Gutachter: Carsten Rother, Carsten Steger ; Carsten Rother, Stefan Gumhold." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1226897592/34.

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Grabaskas, David. "Efficient Approaches to the Treatment of Uncertainty in Satisfying Regulatory Limits." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345464067.

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Carroll, James Lamond. "A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3413.pdf.

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LEONARD, ANTHONY CHARLES. "HYPOTHESIS TESTING WITH THE SIMILARITY INDEX." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005680996.

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Silva, Ricardo Gonçalves da. ""Testes de hipótese e critério bayesiano de seleção de modelos para séries temporais com raiz unitária"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19082004-163615/.

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A literatura referente a testes de hipótese em modelos auto-regressivos que apresentam uma possível raiz unitária é bastante vasta e engloba pesquisas oriundas de diversas áreas. Nesta dissertação, inicialmente, buscou-se realizar uma revisão dos principais resultados existentes, oriundos tanto da visão clássica quanto da bayesiana de inferência. No que concerne ao ferramental clássico, o papel do movimento browniano foi apresentado de forma detalhada, buscando-se enfatizar a sua aplicabilidade na dedução de estatísticas assintóticas para a realização dos testes de hipótese relativos à presença de uma raíz unitária. Com relação à inferência bayesiana, foi inicialmente conduzido um exame detalhado do status corrente da literatura. A seguir, foi realizado um estudo comparativo em que se testa a hipótese de raiz unitária com base na probabilidade da densidade a posteriori do parâmetro do modelo, considerando as seguintes densidades a priori: Flat, Jeffreys, Normal e Beta. A inferência foi realizada com base no algoritmo Metropolis-Hastings, usando a técnica de simulação de Monte Carlo por Cadeias de Markov (MCMC). Poder, tamanho e confiança dos testes apresentados foram computados com o uso de séries simuladas. Finalmente, foi proposto um critério bayesiano de seleção de modelos, utilizando as mesmas distribuições a priori do teste de hipótese. Ambos os procedimentos foram ilustrados com aplicações empíricas à séries temporais macroeconômicas.
Testing for unit root hypothesis in non stationary autoregressive models has been a research topic disseminated along many academic areas. As a first step for approaching this issue, this dissertation includes an extensive review highlighting the main results provided by Classical and Bayesian inferences methods. Concerning Classical approach, the role of brownian motion is discussed in a very detailed way, clearly emphasizing its application for obtaining good asymptotic statistics when we are testing for the existence of a unit root in a time series. Alternatively, for Bayesian approach, a detailed discussion is also introduced in the main text. Then, exploring an empirical façade of this dissertation, we implemented a comparative study for testing unit root based on a posteriori model's parameter density probability, taking into account the following a priori densities: Flat, Jeffreys, Normal and Beta. The inference is based on the Metropolis-Hastings algorithm and on the Monte Carlo Markov Chains (MCMC) technique. Simulated time series are used for calculating size, power and confidence intervals for the developed unit root hypothesis test. Finally, we proposed a Bayesian criterion for selecting models based on the same a priori distributions used for developing the same hypothesis tests. Obviously, both procedures are empirically illustrated through application to macroeconomic time series.
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Sun, Yiping. "Rank-sum test for two-sample location problem under order restricted randomized design." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180147276.

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Erfmeier, Alexandra. "Ursachen des Invasionserfolges von Rhododendron ponticum L. auf den Britischen Inseln Einfluss von Habitat und Genotyp /." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=975033476.

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Su, Weizhe. "Bayesian Hidden Markov Model in Multiple Testing on Dependent Count Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751403094066.

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Books on the topic "Hypothesis sampling"

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Permutation, parametric and bootstrap tests of hypotheses. 3rd ed. New York: Springer, 2005.

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Lin, Nancy Pei-ching. A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures. [Tʻai-pei shih: Ching sheng wen wu kung ying kung ssu, 1985.

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Siegmund, David. Sequential analysis: Tests and confidence intervals. New York: Springer-Verlag, 2010.

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Loether, Herman J. Descriptive and inferential statistics: An introduction. 3rd ed. Boston: Allyn and Bacon, 1988.

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G, McTavish Donald, ed. Descriptive and inferential statistics: An introduction. 4th ed. Boston: Allyn and Bacon, 1993.

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Böhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.

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Odeh, Robert E. Sample size choice: Charts for experiments with linear models. 2nd ed. New York, N.Y: M. Dekker, 1991.

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Randomization tests. 2nd ed. New York: M. Dekker, 1987.

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Patrick, Onghena, ed. Randomization tests. 4th ed. Boca Raton, Fla: Taylor & Francis, 2007.

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Edgington, Eugene S. Randomization tests. 3rd ed. New York: M. Dekker, 1995.

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Book chapters on the topic "Hypothesis sampling"

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Hirsch, Robert. "Sampling." In Statistical Hypothesis Testing with Microsoft ® Office Excel ®, 11–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04202-7_2.

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Yadav, Shakti Kumar, Sompal Singh, and Ruchika Gupta. "Sampling Distribution and Hypothesis Testing." In Biomedical Statistics, 95–98. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9294-9_11.

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Leishman, Chris. "Sampling Theory and Hypothesis Testing." In Real Estate Market Research and Analysis, 34–55. London: Macmillan Education UK, 2003. http://dx.doi.org/10.1007/978-1-137-11281-1_3.

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Pfahler, V., J. Adu-Gyamfi, and F. Tamburini. "How to Design a Study Including the Analysis of δ18OP." In Oxygen Isotopes of Inorganic Phosphate in Environmental Samples, 51–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97497-8_5.

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AbstractTo plan a research study, one needs to (1) establish a research question, (2) make a set of observations, (3) form a hypothesis in an attempt to explain the observations and (4) test the hypothesis based on the data collected. The following questions should be addressed when designing a study including the analysis of δ18OP: (i) what is the research hypothesis? (ii) what is the main objective of the study? (iii) what are the aims to address these objectives? and (iv) which techniques are appropriate to address such research question. In addition, one needs to consider (1) which kind of samples needs to be collected, e.g. soil, vegetation or water? (2) in case of soil and sediment samples, which sampling depths and increments need to be sampled? (3) which P pools need to be extracted and analysed for the corresponding δ18OP values? (4) when and how often should samples be taken and (5) how many samples can be processed per week?
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Russo, Riccardo. "Sampling distribution of the mean, its use in hypothesis testing and the one-sample t-test (Frequentist approach)." In Statistics for the Behavioural Sciences, 143–67. Second edition. | Abingdon, Oxon; New York, NY : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9781315200415-ch07.

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Ige, George Olanrewaju, Oluwole Matthew Akinnagbe, Olalekan Olamigoke Odefadehan, and Opeyemi Peter Ogunbusuyi. "Constraints to Farmers’ Choice of Climate Change Adaptation Strategies in Ondo State of Nigeria." In African Handbook of Climate Change Adaptation, 601–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_103.

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AbstractNigeria being dependent on rain-fed agriculture and with low level of socioeconomic development is highly affected and vulnerable to climate change. It is crucial for farmers to adapt to the never ending climate change. However, there are constraints to adaptation strategies used by the farmers. This study therefore identified some of the constraints to the farmers’ choice of climate change adaptation strategies in Ondo State, Nigeria. A multistage sampling procedure was used in selecting one hundred and sixty respondents for the study. Data collected with a well-structured interview schedule were analyzed using frequency, percentage, and mean statistic, while Pearson Product Moment Correlation was used to test hypothesis. Crops competing for nutrient, inadequate access to climate information, inadequate finance, scarcity of labor, and inadequate farm input supplies were among the major constraints to choice of climate change adaptation strategies used by the respondents. The study recommended that weather forecast information should be published and made available to the farmers through agricultural extension agents. Training on how to improve mixed cropping technique and avoid vulnerability should be pursued.
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Hui, Cang, Pietro Landi, and Guillaume Latombe. "The role of biotic interactions in invasion ecology: theories and hypotheses." In Plant invasions: the role of biotic interactions, 26–44. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789242171.0026.

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Abstract Changes in biotic interactions in the native and invaded range can enable a non-native species to establish and spread in novel environments. Invasive non-native species can in turn generate impacts in recipient systems partly through the changes they impose on biotic interactions; these interactions can lead to altered ecosystem processes in the recipient systems. This chapter reviews models, theories and hypotheses on how invasion performance and impact of introduced species in recipient ecosystems can be conjectured according to biotic interactions between native and non-native species. It starts by exploring the nature of biotic interactions as ensembles of ecological and evolutionary games between individuals of both the same and different groups. This allows us to categorize biotic interactions as direct and indirect (i.e. those involving more than two species) that emerge from both coevolution and ecological fitting during community assembly and invasion. We then introduce conceptual models that can reveal the ecological and evolutionary dynamics between interacting non-native and resident species in ecological networks and communities. Moving from such theoretical grounding, we review 20 hypotheses that have been proposed in invasion ecology to explain the invasion performance of a single non-native species, and seven hypotheses relating to the creation and function of assemblages of non-native species within recipient ecosystems. We argue that, although biotic interactions are ubiquitous and quintessential to the assessment of invasion performance, they are nonetheless difficult to detect and measure due to strength dependency on sampling scales and population densities, as well as the non-equilibrium transient dynamics of ecological communities and networks. We therefore call for coordinated efforts in invasion science and beyond, to devise and review approaches that can rapidly map out the entire web of dynamic interactions in a recipient ecosystem.
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"Fundamentals of Hypothesis Testing." In Forest Sampling Desk Reference, 381–448. CRC Press, 2000. http://dx.doi.org/10.1201/9781420042498-16.

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"Fundamentals of Hypothesis Testing." In Forest Sampling Desk Reference, 361–428. CRC Press, 2000. http://dx.doi.org/10.1201/9781420042498.ch14.

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"Sampling and Hypothesis Testing Theory." In Introductory Mathematics and Statistics for Islamic Finance, 271–300. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118779712.ch13.

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Conference papers on the topic "Hypothesis sampling"

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Price, Andrew, Kun Huang, and Dmitry Berenson. "Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561136.

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Lambrecht, Jens, Richard Viehover, and Chunyang Chang. "Predictive Sampling and Hypothesis Matching for Realtime Cloud Control of Mobile Robots." In 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). IEEE, 2022. http://dx.doi.org/10.1109/isie51582.2022.9831501.

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Ragi, Shankarachary, and Hans D. Mittelmann. "Random-Sampling Multipath Hypothesis Propagation for Cost Approximation in Long-Horizon Optimal Control." In 2020 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2020. http://dx.doi.org/10.1109/ccta41146.2020.9206334.

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Bily, Christopher S., and Richard J. Malak. "Efficient Sampling Methods for Tradeoff Studies Under Uncertainty." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70551.

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Tradeoff studies help designers better understand how different design considerations relate to one another and to make decisions. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly using the Pareto dominance criterion and explore more carefully those that remain. An analogous procedure is possible when design criteria are uncertain. This approach is based on stochastic dominance principles that involve comparisons of probability distributions defined in the design criteria space. Although this is well-founded mathematically, the procedure can be computationally expensive because it typically entails a sampling-based uncertainty propagation method (e.g. Monte Carlo or quasi-Monte Carlo methods) for each alternative being considered. In this paper we describe a statistically sound method which allows designers to sample the minimum number of samples necessary to eliminate dominated design alternatives under uncertainty. Dominance is evaluated using the appropriate hypothesis testing with specified confidence for a small sample, and the sample incrementally increased until dominance conditions can be determined. The method is demonstrated in the context of a tradeoff study for an automobile transmission.
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Bruce, Mary. "The Use of Random Data in Online Discussion Boards to Promote Student Understanding of Sampling Distributions." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.hirmq.

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With the recent pandemic, education systems responded to this challenge with reliance on virtual instruction and distance learning. As a result, instructors were pushed to create learning environments that foster meaningful learning through discussion boards and video conferencing. This paper seeks to illustrate how the use of random data in online discussion boards provides a setting rich in embedded statistical theory that serves as an engaging and fruitful environment for instruction in sampling distributions and hypothesis testing in an introductory statistics course.
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Higo, Eishiro, and Mahesh D. Pandey. "Value of Information and Hypothesis Testing Approaches for Sample Size Determination in Engineering Component Inspection: A Comparison." In ASME 2016 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/pvp2016-63932.

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A sample size determination method is developed for a two-action problem that represents a component maintenance scenario requiring current state estimation. For safety and generation efficiency, each component of a nuclear power plant must be regularly inspected. In terms of safety, the larger the sample size inspected, the less the uncertainty about current and future states of the components; however, such inspections are expensive. Thus, sample size determination becomes an important problem. A key idea for solving this problem is the Value of Information (VoI) and its derivation: the Expected Net Gain of Sampling (ENGS). The ENGS is a function of sample size and represents by how much a decision maker benefits from the observed data. By maximizing the ENGS, the optimal sample size is determined in terms of cost-benefit analysis.
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Kurfess, Thomas R., and David L. Banks. "Statistical Verification of Conformance to Geometric Tolerance." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0065.

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Abstract Modern coordinate measurement machines have provided industry with new tools for inspecting complex parts. This paper develops statistical procedures that complement these inspection methods, and it ties the conformance problem into the hypothesis testing formalism of conventional statistics. Also, the paper suggests strategies for efficient sampling of the part surface, and an implementation of the usual decision-theoretic formulation of the tradeoff between false acceptance and false rejection of part geometries. These ideas are illustrated through the analysis of real and simulated data for perfect and imperfect cylinders.
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Köhler, Alexander, Ashkan Rigi, and Michael Breuß. "Fast Shape Classification Using Kolmogorov-Smirnov Statistics." In WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.22.

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The fast classification of shapes is an important problem in shape analysis and of high relevance for many possible applications. In this paper, we consider the use of very fast and easy to compute statistical techniques for assessing shapes, which may for instance be useful for a first similarity search in a shape database. To this end, we con- struct shape signatures at hand of stochastic sampling of distances between points of interest in a given shape. By employing the Kolmogorov-Smirnov statistics we then propose to formulate the problem of shape classification as a statistical hypothesis test that enables to assess the similarity of the signature distributions. In order to illus- trate some important properties of our approach, we explore the use of simple sampling techniques. At hand of experiments conducted with a variety of shapes in two dimensions, we give a discussion of potentially interesting features of the method.
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Afriyanto, Fajar, Hilda Purnamawati, and Iyan Sukiman. "THE EFFECT OF CAPITAL ADEQUACY RATIO (CAR) AND LOAN TO DEPOSIT RATIO (LDR) ON NON PERFORMING LOAN (NPL) (CASE STUDY ON CONVENTIONAL COMMERCIAL BANKS IN INDONESIA ON 2016-2020)." In Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik (SoBAT) ke-3. LPPM USB YPKP, 2021. http://dx.doi.org/10.32897/sobat3.2021.26.

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This study aims to determine the influence of the Capital Adequacy Ratio (CAR) and Loan Deposit Ratio (LDR), both partially and simultaneously, on Non Performing Loans (NPL) in Conventional Commercial Banks in Indonesia for the period 2016-2020. This research is associative research with a quantitative approach. The data used in this study is secondary data, data analysis techniques using a data regression panel. Testing panel data include CEM, FEM and REM, Chow Test, Hausman Test, Lagrange Multiplier Test. Classic assumption tests include The Test of Normality, Heteroskedastisitas, Multicollinearity and Autocorrelation. Data analysis using correlation and determination coefficient analysis, Simultaneous and Partial Hypothesis Test. The population in this study was 99 Conventional Commercial Banks in Indonesia and a sample of 25 Conventional Commercial Banks in Indonesia with purposive sampling techniques. Based on the results of simultaneous hypothesis testing where CAR and LDR jointly have a significant effect on NPL Conventional Commercial Banks in Indonesia, the results off count 6.89 > 3.42 f table with a significant rate of 0.001457 lower than the α 0.05. While the partial hypothesis testing where CAR affects NPL Conventional Commercial Banks in Indonesia seen the results of t count -3,507 < 2,068 t table with a signifies value of CAR of 0.0006 is smaller than α 0.05. LDR has no effect on NPL of Conventional Commercial Banks with the calculation result of -1,116 < 2,068 t table with a significant LDR value of 0.2665 greater than α 0.05.
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Blumberg, Carol. "Training regular education and special education teachers in the use of research methodology and statistics." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00305.

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The purpose of this paper is to discuss the needs of primary and secondary level teachers seeking Master's degrees (second degrees) in terms of knowledge and use of research methodology and statistics, with special consideration of the needs of Special Education teachers. In particular, the following topics will be discussed: goals and organisation; descriptive statistics; inferential statistics; specific hypothesis testing procedures; experimental and quasi-experimental designs; survey research and sampling techniques; qualitative data collection techniques; and reliability and validity. The paper ends with a discussion of what special education teachers need to know concerning meta-analysis and single-subject designs and with some miscellaneous comments.
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Reports on the topic "Hypothesis sampling"

1

Bhatt, Nikita. Hypothesis testing and population sampling. BJUI Knowledge, January 2021. http://dx.doi.org/10.18591/bjuik.0743.

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2

Lafrancois, Toben, Mark Hove, and Jay Glase. Zebra mussel (Dreissena polymorpha) distribution in Apostle Islands National Lakeshore: SCUBA-based search and removal efforts: 2019–2020. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293376.

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Invasive zebra mussels (Dreissena polymorpha) were first observed in situ at Apostle Islands National Lakeshore (APIS) in 2015. This report builds on 2018 SCUBA surveys and Environmental Protection Agency (EPA) veliger sampling to: 1) determine whether shoals on APIS borders act as sentinel sites to corroborate veliger drift hypotheses about invasion pathways, 2) evaluate ongoing hand-removal of zebra mussels from easily identified structures, and 3) continue efforts to assess native unionid mussel populations, particularly where zebra mussels are also present. Standard catch per unit effort survey methods by SCUBA teams were used to determine the distribution and relative abundance of zebra or quagga mussels (dreissenids) and native mussels (unionids). Zebra mussels were present at densities between 3 and 42 n/diver/hr (number of mussels per diver per hour), while native unionids were present at densities between 5 and 72 n/diver/hr. Shoal surveys (Eagle Island shoal, Sand Island shoal, York Island shoal, Bear Island shoal, Oak Island shoal, and Gull Island shoal) showed zebra mussels were more abundant on the west side of APIS and absent on the easternmost shoal (Gull Island), corroborating veliger work by the EPA that suggested drift from the Twin Ports of Duluth, Minnesota, and Superior, Wisconsin, is one pathway of invasion. Our results support the use of shallow shoals along the periphery of the park as sentinel sites gauging zebra mussel immigration and population dynamics. Zebra mussel densities in the central islands showed no obvious spatial pattern, and this survey cannot determine whether currents or human transport (or both) are invasion vectors. Given the mussels’ continued presence at heavily used mooring areas and docks where there are no zebra mussels on nearby natural features (e.g., Rocky Island dock, Stockton Island mooring areas), our findings are consistent with multiple invasion pathways (drift from the Twin Ports and anthropogenic sources at mooring areas). SCUBA search and removal of zebra mussels from docks was confirmed to be an effective method for significantly lowering the risk of zebra mussels reproducing and dispersing from these locations. We caution that this work is being done on what look like initial invasions at low densities. Repeated removal of zebra mussels by divers reduced numbers to zero at some sites after one year (South Twin docks, Stockton Island NPS docks, and the Ottawa wreck) or decreased numbers by an order of magnitude (Rocky Island docks). Dreissenid densities were more persistent on the Sevona wreck and longer-term work is required to evaluate removal versus recruitment (local and/or veliger drift). Given the size of the wreck, we have tracked detailed survey maps to guide future efforts. Zebra mussels were again observed attached to native mussels near Stockton Island and South Twin Island. Their continued presence on sensitive native species is of concern. Native unionid mussels were more widely distributed in the park than previously known, with new beds found near Oak and Basswood Islands. The work reported here will form the basis for continued efforts to determine the optimal frequency of zebra mussel removal for effective control, as well as evaluate impacts on native species.
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