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Relevant bibliographies by topics / Sample-sample

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Academic literature on the topic 'Sample-sample'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Sample-sample"

1

Muralidharan, K. "On Sample Size Determination." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 3, no. 1 (2014): 55–64. http://dx.doi.org/10.15415/mjis.2014.31005.

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Bréchon, Pierre. "Random Sample, Quota Sample." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 126, no. 1 (2015): 67–83. http://dx.doi.org/10.1177/0759106315572558.

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Zhang, Lingyun. "Sample Mean and Sample Variance." American Statistician 61, no. 2 (2007): 159–60. http://dx.doi.org/10.1198/000313007x188379.

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Araki, Sawa. "Sample Handling and Sample Pretreatment." Journal of Surface Analysis 25, no. 3 (2019): 202–8. http://dx.doi.org/10.1384/jsa.25.202.

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Gleibman, Andrew H. "SAMPLE." ACM SIGSAM Bulletin 26, no. 3 (1992): 21–29. http://dx.doi.org/10.1145/141897.141908.

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HALLER, HANS, and ANTHONY PAVLOPOULOS. "TO SAMPLE OR NOT TO SAMPLE." Manchester School 74, no. 1 (2006): 23–39. http://dx.doi.org/10.1111/j.1467-9957.2006.00480.x.

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Yurtay, Yuksel. "Sample application on dramatization in education." New Trends and Issues Proceedings on Humanities and Social Sciences 03, no. 07 (2017): 08–13. http://dx.doi.org/10.18844/prosoc.v2i7.1978.

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Öztürk, Ömer. "Two-sample inference based on one-sample ranked set sample sign statistics." Journal of Nonparametric Statistics 10, no. 2 (1999): 197–212. http://dx.doi.org/10.1080/10485259908832760.

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Krishnamoorthy, K., and Luis León-Novelo. "Small sample inference for gamma parameters: one-sample and two-sample problems." Environmetrics 25, no. 2 (2014): 107–26. http://dx.doi.org/10.1002/env.2261.

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Chia, Victor K. F., D. Kirk Veirs, and Gerd M. Rosenblatt. "Cryogenic sample manipulator for multipurpose sample analysis." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 7, no. 1 (1989): 108–9. http://dx.doi.org/10.1116/1.575755.

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Dissertations / Theses on the topic "Sample-sample"

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Hippert, Theresa M. "Hippert work sample." Online version, 2002. http://www.uwstout.edu/lib/thesis/2002/2002hippertt.pdf.

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Grossman, J. P. 1973. "Point sample rendering." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50063.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (p. 54-56).<br>We present an algorithm suitable for real-time, high quality rendering of complex objects. Objects are represented as a dense set of surface point samples which contain colour, depth and normal information. These point samples are obtained by sampling orthographic views on an equilateral triangle lattice. They are rendered directly and independently without any knowledge of surface topology. We introduce a novel solution to the problem of surface reconstruction using a hierarchy of Z-buffers to detect tears. The algorithm is fast, easily vectorizable, and requires only modest resources.<br>by J.P. Grossman.<br>S.M.

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Rattray, V. Robin (Vaughn Robin). "Sample preconcentration and analysis by direct sample insertion inductively coupled plasma spectrometry." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28892.

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Several approaches to sample preconcentration combined with sample introduction by Direct Sample Insertion (DSI) into the Inductively Coupled Plasma (ICP) for ultra trace elemental analysis have been developed. A microcolumn based flow injection (FI) manifold was used with ICP Mass Spectrometry (MS) but performance was adversely affected by high and variable blank levels.<br>Physical preconcentration by depositing the sample as an aerosol into an inductively heated graphite DSI probe yielded detection limit improvements of over two orders of magnitude for ICP Atomic Emission Spectrometry (AES). Instrumentation was developed to automate the aerosol deposition preconcentration process, and this apparatus was used in conjunction with ICP-MS. Several of the aerosol deposition, DSI, and ICP parameters that impact on the performance of the technique were studied. Detection limit improvements averaged two orders of magnitude, and analysis of a river water reference material for 10 elements gave good results even at the part per trillion (pg ml$ sp{-1}$) level.<br>Investigations into direct analysis of the analyte-laden chelating resin by DSI-ICP-AES were carried out. It was clearly demonstrated that the determination of volatile elements was adversely affected by the effect of the pyrolysis products of the resin on the plasma excitation conditions.

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Serra, Puertas Jorge. "Shrinkage corrections of sample linear estimators in the small sample size regime." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/404386.

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We are living in a data deluge era where the dimensionality of the data gathered by inexpensive sensors is growing at a fast pace, whereas the availability of independent samples of the observed data is limited. Thus, classical statistical inference methods relying on the assumption that the sample size is large, compared to the observation dimension, are suffering a severe performance degradation. Within this context, this thesis focus on a popular problem in signal processing, the estimation of a parameter, observed through a linear model. This inference is commonly based on a linear filtering of the data. For instance, beamforming in array signal processing, where a spatial filter steers the beampattern of the antenna array towards a direction to obtain the signal of interest (SOI). In signal processing the design of the optimal filters relies on the optimization of performance measures such as the Mean Square Error (MSE) and the Signal to Interference plus Noise Ratio (SINR). When the first two moments of the SOI are known, the optimization of the MSE leads to the Linear Minimum Mean Square Error (LMMSE). When such statistical information is not available one may force a no distortion constraint towards the SOI in the optimization of the MSE, which is equivalent to maximize the SINR. This leads to the Minimum Variance Distortionless Response (MVDR) method. The LMMSE and MVDR are optimal, though unrealizable in general, since they depend on the inverse of the data correlation, which is not known. The common approach to circumvent this problem is to substitute it for the inverse of the sample correlation matrix (SCM), leading to the sample LMMSE and sample MVDR. This approach is optimal when the number of available statistical samples tends to infinity for a fixed observation dimension. This large sample size scenario hardly holds in practice and the sample methods undergo large performance degradations in the small sample size regime, which may be due to short stationarity constraints or to a system with a high observation dimension. The aim of this thesis is to propose corrections of sample estimators, such as the sample LMMSE and MVDR, to circumvent their performance degradation in the small sample size regime. To this end, two powerful tools are used, shrinkage estimation and random matrix theory (RMT). Shrinkage estimation introduces a structure on the filters that forces some corrections in small sample size situations. They improve sample based estimators by optimizing a bias variance tradeoff. As direct optimization of these shrinkage methods leads to unrealizable estimators, then a consistent estimate of these optimal shrinkage estimators is obtained, within the general asymptotics where both the observation dimension and the sample size tend to infinity, but at a fixed rate. That is, RMT is used to obtain consistent estimates within an asymptotic regime that deals naturally with the small sample size. This RMT approach does not require any assumptions about the distribution of the observations. The proposed filters deal directly with the estimation of the SOI, which leads to performance gains compared to related work methods based on optimizing a metric related to the data covariance estimate or proposing rather ad-hoc regularizations of the SCM. Compared to related work methods which also treat directly the estimation of the SOI and which are based on a shrinkage of the SCM, the proposed filter structure is more general. It contemplates corrections of the inverse of the SCM and considers the related work methods as particular cases. This leads to performance gains which are notable when there is a mismatch in the signature vector of the SOI. This mismatch and the small sample size are the main sources of degradation of the sample LMMSE and MVDR. Thus, in the last part of this thesis, unlike the previous proposed filters and the related work, we propose a filter which treats directly both sources of degradation.<br>Estamos viviendo en una era en la que la dimensión de los datos, recogidos por sensores de bajo precio, está creciendo a un ritmo elevado, pero la disponibilidad de muestras estadísticamente independientes de los datos es limitada. Así, los métodos clásicos de inferencia estadística sufren una degradación importante, ya que asumen un tamaño muestral grande comparado con la dimensión de los datos. En este contexto, esta tesis se centra en un problema popular en procesado de señal, la estimación lineal de un parámetro observado mediante un modelo lineal. Por ejemplo, la conformación de haz en procesado de agrupaciones de antenas, donde un filtro enfoca el haz hacia una dirección para obtener la señal asociada a una fuente de interés (SOI). El diseño de los filtros óptimos se basa en optimizar una medida de prestación como el error cuadrático medio (MSE) o la relación señal a ruido más interferente (SINR). Cuando hay información sobre los momentos de segundo orden de la SOI, la optimización del MSE lleva a obtener el estimador lineal de mínimo error cuadrático medio (LMMSE). Cuando esa información no está disponible, se puede forzar la restricción de no distorsión de la SOI en la optimización del MSE, que es equivalente a maximizar la SINR. Esto conduce al estimador de Capon (MVDR). El LMMSE y MVDR son óptimos, pero no son realizables, ya que dependen de la inversa de la matriz de correlación de los datos, que no es conocida. El procedimiento habitual para solventar este problema es sustituirla por la inversa de la correlación muestral (SCM), esto lleva al LMMSE y MVDR muestral. Este procedimiento es óptimo cuando el tamaño muestral tiende a infinito y la dimensión de los datos es fija. En la práctica este tamaño muestral elevado no suele producirse y los métodos LMMSE y MVDR muestrales sufren una degradación importante en este régimen de tamaño muestral pequeño. Éste se puede deber a periodos cortos de estacionariedad estadística o a sistemas cuya dimensión sea elevada. El objetivo de esta tesis es proponer correcciones de los estimadores LMMSE y MVDR muestrales que permitan combatir su degradación en el régimen de tamaño muestral pequeño. Para ello se usan dos herramientas potentes, la estimación shrinkage y la teoría de matrices aleatorias (RMT). La estimación shrinkage introduce una estructura de los estimadores que mejora los estimadores muestrales mediante la optimización del compromiso entre media y varianza del estimador. La optimización directa de los métodos shrinkage lleva a métodos no realizables. Por eso luego se propone obtener una estimación consistente de ellos en el régimen asintótico en el que tanto la dimensión de los datos como el tamaño muestral tienden a infinito, pero manteniendo un ratio constante. Es decir RMT se usa para obtener estimaciones consistentes en un régimen asintótico que trata naturalmente las situaciones de tamaño muestral pequeño. Esta metodología basada en RMT no requiere suposiciones sobre el tipo de distribución de los datos. Los filtros propuestos tratan directamente la estimación de la SOI, esto lleva a ganancias de prestaciones en comparación a otros métodos basados en optimizar una métrica relacionada con la estimación de la covarianza de los datos o regularizaciones ad hoc de la SCM. La estructura de filtro propuesta es más general que otros métodos que también tratan directamente la estimación de la SOI y que se basan en un shrinkage de la SCM. Contemplamos correcciones de la inversa de la SCM y los métodos del estado del arte son casos particulares. Esto lleva a ganancias de prestaciones que son notables cuando hay una incertidumbre en el vector de firma asociado a la SOI. Esa incertidumbre y el tamaño muestral pequeño son las fuentes de degradación de los LMMSE y MVDR muestrales. Así, en la última parte de la tesis, a diferencia de métodos propuestos previamente en la tesis y en la literatura, se propone un filtro que trata de forma directa ambas fuentes de degradación.

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Sima, Chao. "Small sample feature selection." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5796.

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High-throughput technologies for rapid measurement of vast numbers of biolog- ical variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for fea- ture selection, while at the same time making feature-selection algorithms less reliable. Feature selection is required to avoid overfitting, and the combinatorial nature of the problem demands a suboptimal feature-selection algorithm. In this dissertation, we have found that feature selection is problematic in small- sample settings via three different approaches. First we examined the feature-ranking performance of several kinds of error estimators for different classification rules, by considering all feature subsets and using 2 measures of performance. The results show that their ranking is strongly affected by inaccurate error estimation. Secondly, since enumerating all feature subsets is computationally impossible in practice, a suboptimal feature-selection algorithm is often employed to find from a large set of potential features a small subset with which to classify the samples. If error estimation is required for a feature-selection algorithm, then the impact of error estimation can be greater than the choice of algorithm. Lastly, we took a regression approach by comparing the classification errors for the optimal feature sets and the errors for the feature sets found by feature-selection algorithms. Our study shows that it is unlikely that feature selection will yield a feature set whose error is close to that of the optimal feature set, and the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist.

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Isheden, Gabriel. "Bayesian Hierarchic Sample Clustering." Thesis, KTH, Matematik (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168316.

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This report presents a novel algorithm for hierarchical clustering called Bayesian Sample Clustering (BSC). BSC is a single linkage algorithm that uses data samples to produce a predictive distribution for each sample. The predictive distributions are compared using the Chan-Darwiche distance, a metric for finite probability distributions, to produce a hierarchy of samples. The implemented version of BSC is found at https://github.com/Skjulet/Bayesian Sample Clustering.<br>Denna rapport presenterar en ny algoritm för hierarkisk klustring, Bayesian Sample Clustering (BSC). BSC är en single-linkage algoritm som använder stickprov av data för att skapa en prediktiv fördelning för varje stickprov. De prediktiva fördelningarna jämförs med Chan-Darwiche avståndet, en metrik över ändliga sannolikhetsfördelningar, vilket möjliggör skapandet av en hierarki av kluster. BSC finns i implementerad version på https://github.com/Skjulet/Bayesian Sample Clustering.

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Angelikaki, C. "An intelligent sample changer." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234786.

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Kathman, Steven Jay Jr. "Discrete Small Sample Asymptotics." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30101.

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Random variables defined on the natural numbers may often be approximated by Poisson variables. Just as normal approximations may be improved by saddlepoint methods, Poisson approximations may be substantially improved by tilting, expansion, and other related methods. This work will develop and examine the use of these methods, as well as present examples where such methods may be needed.<br>Ph. D.

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Ying, Lishi. "An automated direct sample insertion-inductively coupled plasma spectrometer for environmental sample analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq39610.pdf.

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Sing, Robert L. A. "Liquid and solid sample introduction into the inductively coupled plasma by direct sample insertion." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74023.

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Books on the topic "Sample-sample"

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Lorenz, Angela. Bologna sample. Angela Lorenz, 1992.

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Association, American Animal Hospital. Sample forms. AAHA, 1990.

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Sample surveys. Elsevier, 2009.

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Sample, Joe. Joe Sample. Warner Bros. Music, 1986.

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Field, Christopher. Small sample asymptotics. Institute of Mathematical Statistics, 1990.

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Ullah, Aman. Finite sample econometrics. Oxford University Press, 2004.

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Bosworth, Stefan. MCAT sample exams. 3rd ed. Macmillan, 1998.

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Society of Management Accountants of Canada. Sample entrance examinations. Society of Management Accountants of Canada, 1992.

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Daniel, Douglas, and American Association of School Administrators., eds. A sample contract. American Association of School Administrators, 1990.

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Hagen, Jörg von. Proteomics sample preparation. Wiley-VCH, 2008.

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Book chapters on the topic "Sample-sample"

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Brenner, Barbara. "Sample." In Management Control in Central and Eastern European Subsidiaries. Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230201743_6.

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Weik, Martin H. "sample." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16569.

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Gooch, Jan W. "Sample." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15360.

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Heppner, John B., David B. Richman, Steven E. Naranjo, et al. "Sample." In Encyclopedia of Entomology. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_4010.

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Bährle-Rapp, Marina. "sample." In Springer Lexikon Kosmetik und Körperpflege. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71095-0_9094.

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Jensen, Lindsay G., Loren K. Mell, Christin A. Knowlton, et al. "Sample." In Encyclopedia of Radiation Oncology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_652.

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Benedetti, Roberto, Federica Piersimoni, and Paolo Postiglione. "Sample Size and Sample Allocation." In Advances in Spatial Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46008-5_8.

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Gibbons, Jean Dickinson, and Subhabrata Chakraborti. "One-Sample and Paired-Sample Procedures." In Nonparametric Statistical Inference. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315110479-5.

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Alvo, Mayer, and Philip L. H. Yu. "One-Sample and Two-Sample Problems." In A Parametric Approach to Nonparametric Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94153-0_5.

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Friedman, Lawrence M., Curt D. Furberg, and David L. DeMets. "Sample Size." In Fundamentals of Clinical Trials. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1586-3_8.

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Conference papers on the topic "Sample-sample"

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Doe, Jane, and John Smith. "AIAA Crossmark Sample Paper." In Crossmark Sample. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.crossmarktest.

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Doe, Jane, and John Smith. "Correction: AIAA Crossmark Sample Paper." In Crossmark Sample. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.crossmarktest.c1.

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Doe, Jane, and John Smith. "Second Correction: AIAA Crossmark Sample Paper." In Crossmark Sample. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.crossmarktest.c2.

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Logsdon, Morgan. "Sample Management Tools for the Irradiation Test Area (ITA)." In Sample Management Tools for the Irradiation Test Area (ITA). US DOE, 2020. http://dx.doi.org/10.2172/1648539.

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Shao, Peng. "Sample selection." In the 46th Annual Southeast Regional Conference. ACM Press, 2008. http://dx.doi.org/10.1145/1593105.1593248.

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Ding, Bolin, Silu Huang, Surajit Chaudhuri, Kaushik Chakrabarti, and Chi Wang. "Sample + Seek." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2915249.

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"Review Sample." In 2021 IEEE International Conference on Health, Instrumentation & Measurement, and Natural Sciences (InHeNce). IEEE, 2021. http://dx.doi.org/10.1109/inhence52833.2021.9537228.

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Mumm, Erik, Tom Kennedy, Lee Carlson, Dustyn Roberts, Michael Rutberg, and Jerri Ji. "Sample Manipulation System for Sample Analysis at Mars." In AIAA SPACE 2008 Conference & Exposition. American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-7736.

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Younse, P., T. de Alwis, P. Backes, and A. Trebi-Ollennu. "Sample sealing approaches for Mars Sample Return caching." In 2012 IEEE Aerospace Conference. IEEE, 2012. http://dx.doi.org/10.1109/aero.2012.6187048.

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Adams, Douglas, James Leary, Stergios Papadakis, et al. "Comet surface sample return: Sample chain system overview." In 2017 IEEE Aerospace Conference. IEEE, 2017. http://dx.doi.org/10.1109/aero.2017.7943721.

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Reports on the topic "Sample-sample"

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Dallimore, S. R., and D. Gillespie. Sample Handling. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/132216.

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Finsterle, S. ITOUGH2 sample problems. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/334371.

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Crane, T. W. Liquid sample shuffler. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/6124373.

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Finsterle, Stefan. iTOUGH2 Sample Problems. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/799535.

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Blanchard, R. J. Improving sample recovery. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/274937.

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Fritz, Brad G., David G. Abrecht, James C. Hayes, and Donaldo P. Mendoza. Soil Gas Sample Handling: Evaluation of Water Removal and Sample Ganging. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1405062.

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Kouzes, Richard T., Mark H. Engelhard, and Zihua Zhu. Germanium-76 Sample Analysis. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1013937.

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Alcaraz, A., H. Gregg, and C. Koester. Sample Proficiency Test exercise. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/900123.

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Angrist, Joshua, and Alan Krueger. Split Sample Instrumental Variables. National Bureau of Economic Research, 1995. http://dx.doi.org/10.3386/t0150.

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Leon, Ryan Francis. Personal Drawing Portfolio Sample. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1253516.

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