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Relevant bibliographies by topics / Statistical inference

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Academic literature on the topic 'Statistical inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 April 2025

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Journal articles on the topic "Statistical inference"

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Rennolls, Keith, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 3 (1996): 622. http://dx.doi.org/10.2307/2983341.

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Crowder, Martin, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Statistician 45, no. 3 (1996): 386. http://dx.doi.org/10.2307/2988478.

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Brunson, Barry W., and Vijay K. Rohatgi. "Statistical Inference." American Mathematical Monthly 94, no. 2 (February 1987): 210. http://dx.doi.org/10.2307/2322441.

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Lindley, D. V., and Vijay K. Rohatgi. "Statistical Inference." Mathematical Gazette 69, no. 447 (March 1985): 63. http://dx.doi.org/10.2307/3616474.

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Rohatgi, V. K. "Statistical Inference." Biometrics 41, no. 4 (December 1985): 1102. http://dx.doi.org/10.2307/2530991.

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Ghosh, Malay, George Casella, and Roger L. Berger. "Statistical Inference." Journal of the American Statistical Association 89, no. 426 (June 1994): 712. http://dx.doi.org/10.2307/2290879.

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Casella, G., and R. L. Berger. "Statistical Inference." Biometrics 49, no. 1 (March 1993): 320. http://dx.doi.org/10.2307/2532634.

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Ziegel, Eric R. "Statistical Inference." Technometrics 44, no. 4 (November 2002): 407–8. http://dx.doi.org/10.1198/tech.2002.s94.

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Angus, John E. "Statistical Inference." Technometrics 33, no. 4 (November 1991): 493. http://dx.doi.org/10.1080/00401706.1991.10484898.

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Randles, Ronald H., and Vijay K. Rohatgi. "Statistical Inference." Journal of the American Statistical Association 81, no. 393 (March 1986): 258. http://dx.doi.org/10.2307/2288010.

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Dissertations / Theses on the topic "Statistical inference"

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Thabane, Lehana. "Contributions to Bayesian statistical inference." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq31133.pdf.

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Yang, Liqiang. "Statistical Inference for Gap Data." NCSU, 2000. http://www.lib.ncsu.edu/theses/available/etd-20001110-173900.

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This thesis research is motivated by a special type of missing data - Gap Data, which was first encountered in a cardiology study conducted at Duke Medical School. This type of data include multiple observations of certain event time (in this medical study the event is the reopenning of a certain artery), some of them may have one or more missing periods called ``gaps'' before observing the``first'' event. Therefore, for those observations, the observed first event may not be the true first event because the true first event might have happened in one of the missing gaps. Due to this kind of missing information, estimating the survival function of the true first event becomes very difficult. No research nor discussion has been done on this type of data by now. In this thesis, the auther introduces a new nonparametric estimating method to solve this problem. This new method is currently called Imputed Empirical Estimating (IEE) method. According to the simulation studies, the IEE method provide a very good estimate of the survival function of the true first event. It significantly outperforms all the existing estimating approaches in our simulation studies. Besides the new IEE method, this thesis also explores the Maximum Likelihood Estimate in thegap data case. The gap data is introduced as a special type of interval censored data for thefirst time. The dependence between the censoring interval (in the gap data case is the observedfirst event time point) and the event (in the gap data case is the true first event) makes the gap data different from the well studied regular interval censored data. This thesis points of theonly difference between the gap data and the regular interval censored data, and provides a MLEof the gap data under certain assumptions.The third estimating method discussed in this thesis is the Weighted Estimating Equation (WEE)method. The WEE estimate is a very popular nonparametric approach currently used in many survivalanalysis studies. In this thesis the consistency and asymptotic properties of the WEE estimateused in the gap data are discussed. Finally, in the gap data case, the WEE estimate is showed to be equivalent to the Kaplan-Meier estimate. Numerical examples are provied in this thesis toillustrate the algorithm of the IEE and the MLE approaches. The auther also provides an IEE estimate of the survival function based on the real-life data from Duke Medical School. A series of simulation studies are conducted to assess the goodness-of-fit of the new IEE estimate. Plots and tables of the results of the simulation studies are presentedin the second chapter of this thesis.

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Sun, Xiaohai. "Causal inference from statistical data /." Berlin : Logos-Verl, 2008. http://d-nb.info/988947331/04.

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Czogiel, Irina. "Statistical inference for molecular shapes." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12217/.

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This thesis is concerned with developing statistical methods for evaluating and comparing molecular shapes. Techniques from statistical shape analysis serve as a basis for our methods. However, as molecules are fuzzy objects of electron clouds which constantly undergo vibrational motions and conformational changes, these techniques should be modified to be more suitable for the distinctive features of molecular shape. The first part of this thesis is concerned with the continuous nature of molecules. Based on molecular properties which have been measured at the atom positions, a continuous field--based representation of a molecule is obtained using methods from spatial statistics. Within the framework of reproducing kernel Hilbert spaces, a similarity index for two molecular shapes is proposed which can then be used for the pairwise alignment of molecules. The alignment is carried out using Markov chain Monte Carlo methods and posterior inference. In the Bayesian setting, it is also possible to introduce additional parameters (mask vectors) which allow for the fact that only part of the molecules may be similar. We apply our methods to a dataset of 31 steroid molecules which fall into three activity classes with respect to the binding activity to a common receptor protein. To investigate which molecular features distinguish the activity classes, we also propose a generalisation of the pairwise method to the simultaneous alignment of several molecules. The second part of this thesis is concerned with the dynamic aspect of molecular shapes. Here, we consider a dataset containing time series of DNA configurations which have been obtained using molecular dynamic simulations. For each considered DNA duplex, both a damaged and an undamaged version are available, and the objective is to investigate whether or not the damage induces a significant difference to the the mean shape of the molecule. To do so, we consider bootstrap hypothesis tests for the equality of mean shapes. In particular, we investigate the use of a computationally inexpensive algorithm which is based on the Procrustes tangent space. Two versions of this algorithm are proposed. The first version is designed for independent configuration matrices while the second version is specifically designed to accommodate temporal dependence of the configurations within each group and is hence more suitable for the DNA data.

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方以德 and Yee-tak Daniel Fong. "Statistical inference on biomedical models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31210788.

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Liu, Fei, and 劉飛. "Statistical inference for banding data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41508701.

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Junklewitz, Henrik. "Statistical inference in radio astronomy." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-177457.

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This thesis unifies several studies, which all are dedicated to the subject of statistical data analysis in radio astronomy and radio astrophysics. Radio astronomy, like astronomy as a whole, has undergone a remarkable development in the past twenty years in introducing new instruments and technologies. New telescopes like the upgraded VLA, LOFAR, or the SKA and its pathfinder missions offer unprecedented sensitivities, previously uncharted frequency domains and unmatched survey capabilities. Many of these have the potential to significantly advance the science of radio astrophysics and cosmology on all scales, from solar and stellar physics, Galactic astrophysics and cosmic magnetic fields, to Galaxy cluster astrophysics and signals from the epoch of reionization. Since then, radio data analysis, calibration and imaging techniques have entered a similar phase of new development to push the boundaries and adapt the field to the new instruments and scientific opportunities. This thesis contributes to these greater developments in two specific subjects, radio interferometric imaging and cosmic magnetic field statistics. Throughout this study, different data analysis techniques are presented and employed in various settings, but all can be summarized under the broad term of statistical infer- ence. This subject encompasses a huge variety of statistical techniques, developed to solve problems in which deductions have to be made from incomplete knowledge, data or measurements. This study focuses especially on Bayesian inference methods that make use of a subjective definition of probabilities, allowing for the expression of probabilities and statistical knowledge prior to an actual measurement. The thesis contains two different sets of application for such techniques. First, situations where a complicated, and generally ill-posed measurement problem can be approached by assuming a statistical signal model prior to infer the desired measured variable. Such a problem very often is met should the measurement device take less data then needed to constrain all degrees of freedom of the problem. The principal case investigated in this thesis is the measurement problem of a radio interferometer, which takes incomplete samples of the Fourier transformed intensity of the radio emission in the sky, such that it is impossible to exactly recover the signal. The new imaging algorithm RESOLVE is presented, optimal for extended radio sources. A first showcase demonstrates the performance of the new technique on real data. Further, a new Bayesian approach to multi-frequency radio interferometric imaging is presented and integrated into RESOLVE. The second field of application are astrophysical problems, in which the inherent stochas- tic nature of a physical process demands a description, where properties of physical quanti- ties can only be statistically estimated. Astrophysical plasmas for instance are very often in a turbulent state, and thus governed by statistical hydrodynamical laws. Two studies are presented that show how properties of turbulent plasma magnetic fields can be inferred from radio observations.

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Bell, Paul W. "Statistical inference for multidimensional scaling." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327197.

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Covarrubias, Carlos Cuevas. "Statistical inference for ROC curves." Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399489.

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Oe, Bianca Madoka Shimizu. "Statistical inference in complex networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28032017-095426/.

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The complex network theory has been extensively used to understand various natural and artificial phenomena made of interconnected parts. This representation enables the study of dynamical processes running on complex systems, such as epidemics and rumor spreading. The evolution of these dynamical processes is influenced by the organization of the network. The size of some real world networks makes it prohibitive to analyse the whole network computationally. Thus it is necessary to represent it by a set of topological measures or to reduce its size by means of sampling. In addition, most networks are samples of a larger networks whose structure may not be captured and thus, need to be inferred from samples. In this work, we study both problems: the influence of the structure of the network in spreading processes and the effects of sampling in the structure of the network. Our results suggest that it is possible to predict the final fraction of infected individuals and the final fraction of individuals that came across a rumor by modeling them with a beta regression model and using topological measures as regressors. The most influential measure in both cases is the average search information, that quantifies the ease or difficulty to navigate through a network. We have also shown that the structure of a sampled network differs from the original network and that the type of change depends on the sampling method. Finally, we apply four sampling methods to study the behaviour of the epidemic threshold of a network when sampled with different sampling rates and found out that the breadth-first search sampling is most appropriate method to estimate the epidemic threshold among the studied ones.
Vários fenômenos naturais e artificiais compostos de partes interconectadas vem sendo estudados pela teoria de redes complexas. Tal representação permite o estudo de processos dinâmicos que ocorrem em redes complexas, tais como propagação de epidemias e rumores. A evolução destes processos é influenciada pela organização das conexões da rede. O tamanho das redes do mundo real torna a análise da rede inteira computacionalmente proibitiva. Portanto, torna-se necessário representá-la com medidas topológicas ou amostrá-la para reduzir seu tamanho. Além disso, muitas redes são amostras de redes maiores cuja estrutura é difícil de ser capturada e deve ser inferida de amostras. Neste trabalho, ambos os problemas são estudados: a influência da estrutura da rede em processos de propagação e os efeitos da amostragem na estrutura da rede. Os resultados obtidos sugerem que é possível predizer o tamanho da epidemia ou do rumor com base em um modelo de regressão beta com dispersão variável, usando medidas topológicas como regressores. A medida mais influente em ambas as dinâmicas é a informação de busca média, que quantifica a facilidade com que se navega em uma rede. Também é mostrado que a estrutura de uma rede amostrada difere da original e que o tipo de mudança depende do método de amostragem utilizado. Por fim, quatro métodos de amostragem foram aplicados para estudar o comportamento do limiar epidêmico de uma rede quando amostrada com diferentes taxas de amostragem. Os resultados sugerem que a amostragem por busca em largura é a mais adequada para estimar o limiar epidêmico entre os métodos comparados.

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Books on the topic "Statistical inference"

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Bromek, Tadeusz, and Elżbieta Pleszczyńska, eds. Statistical Inference. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.

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Panik, Michael J. Statistical Inference. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118309773.

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L, Berger Roger, ed. Statistical inference. Pacific Grove, Calif: Brooks/Cole Pub. Co., 1990.

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Silvey, S. D. Statistical inference. London: Chapman and Hall, 1988.

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Garthwaite, Paul H. Statistical inference. London: Prentice Hall, 1995.

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Garthwaite, Paul H. Statistical inference. 2nd ed. Oxford: Oxford University Press, 2002.

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Oakes, Michael W. Statistical inference. Chestnut Hill, MA: Epidemiology Resources Inc., 1990.

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L, Berger Roger, ed. Statistical Inference. 2nd ed. Australia: Thomson Learning, 2002.

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T, Jolliffe I., and Jones Byron 1951-, eds. Statistical inference. Oxford: Oxford University Press, 2002.

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Deshmukh, Shailaja, and Madhuri Kulkarni. Asymptotic Statistical Inference. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9003-0.

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Book chapters on the topic "Statistical inference"

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Lynch, Scott M. "Statistical Inference." In Using Statistics in Social Research, 83–105. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8573-5_6.

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Kroese, Dirk P., and Joshua C. C. Chan. "Statistical Inference." In Statistical Modeling and Computation, 121–59. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_5.

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Rahlf, Thomas. "Statistical Inference." In Handbook of Cliometrics, 471–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40406-1_16.

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Gooch, Jan W. "Statistical Inference." In Encyclopedic Dictionary of Polymers, 998. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15388.

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Helland, Inge S. "Statistical Inference." In Epistemic Processes, 21–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95068-6_2.

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Verma, J. P. "Statistical Inference." In Statistics and Research Methods in Psychology with Excel, 365–438. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3429-0_10.

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Haenni, Rolf, Jan-Willem Romeijn, Gregory Wheeler, and Jon Williamson. "Statistical Inference." In Probabilistic Logics and Probabilistic Networks, 49–61. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-94-007-0008-6_5.

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Turner, J. Rick. "Statistical Inference." In Encyclopedia of Behavioral Medicine, 2136. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1047.

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Turner, J. Rick. "Statistical Inference." In Encyclopedia of Behavioral Medicine, 1878. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1047.

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Barnard, G. A., J. C. Kiefer, L. M. LeCam, and L. J. Savage. "Statistical Inference." In Collected Papers, 601–22. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-8505-9_38.

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Conference papers on the topic "Statistical inference"

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Singh, Pawan Kumar, and Pawan Kumar Upadhyay. "Informatics for dementia detection using statistical inference." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE), 446–51. IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911793.

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Kunisky, Dmitriy, Cristopher Moore, and Alexander S. Wein. "Tensor Cumulants for Statistical Inference on Invariant Distributions." In 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), 1007–26. IEEE, 2024. http://dx.doi.org/10.1109/focs61266.2024.00067.

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Borovcnik, Manfred. "Informal inference – approaches towards statistical inference." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19101.

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The development of methods suitable to tackle the problem of inductive logic – how to justify arguments that generalise findings from data – has been signified by great controversies in the foundations and – later – also in statistics education. There have been several attempts to reconcile the various approaches or to simplify statistical inference: EDA, Non-parametric statistics, and the Bootstrap. EDA focuses on a strong connection between data and context, non parametrics reduces the complexity of the model, and Bootstrap rests solely on the data. Informal inference subsumes two different areas of didactic endeavour: teaching strategies to simplify the full complexity of inference by analogies, simulations, or visualisations on the one hand, and reduce the complexity of inference by a novel approach of Bootstrap and re-randomisation. The considerations about statistical inference will remain important in the era of Big Data. In this paper, the various approaches are compared for their merits and drawbacks.

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Mu, Weiyan, and Xiaona Yuan. "Statistical inference for ANOVA under heteroscedasticity: Statistical inference." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201745.

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BITYUKOV, S. I., V. V. SMIRNOVA, N. V. KRASNIKOV, and V. A. TAPERECHKINA. "STATISTICALLY DUAL DISTRIBUTIONS IN STATISTICAL INFERENCE." In Proceedings of PHYSTAT05. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2006. http://dx.doi.org/10.1142/9781860948985_0023.

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Mézard, Marc. "Statistical physics and statistical inference." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3465420.

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Jones, Peter, Kay Lipson, and Brian Phillips. "A role for computer intensive methods in introducing statistical inference." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93311.

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Introductory statistics courses have become increasingly prevalent in a wide variety of undergraduate and graduate programs over the past few years. This has resulted in the study of inferential statistics becoming the norm rather than exception. Whilst statistics was once a course chosen by the more mathematically able student, many current students of statistics have little mathematical aptitude or expertise (Tanis, 1992 for example). As a result, many introductory statistic courses have moved away from including much of the statistical theory that underpins inference to become basically technique oriented "recipe book" courses. the internet danger of producing students capable of performing various complex statistical tests without really knowing what they are doing is obvious, particularly if the house includes the use of a sophisticated statistical computer package.

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Masucci, Antonia, Oyvind Ryan, Sheng Yang, and Merouane Debbah. "Finite dimensional statistical inference." In 2009 International Conference on Ultra Modern Telecommunications & Workshops. ICUMT 2009. IEEE, 2009. http://dx.doi.org/10.1109/icumt.2009.5345347.

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Vesely, Sara, Leonardo Vesely, and Alessandro Vesely. "Nanotechnology and statistical inference." In NANOINNOVATION 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4997131.

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du Pin Calmon, Flavio, and Nadia Fawaz. "Privacy against statistical inference." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483382.

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Reports on the topic "Statistical inference"

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Carroll, Raymond J. Research in Statistical Inference. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada252928.

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Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, January 2019. http://dx.doi.org/10.1920/wp.cem.2019.06.

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Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, January 2019. http://dx.doi.org/10.1920/wp.cem.2019.0619.

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Karr, Alan F. Statistical Inference for Stochastic Processes. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada190491.

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Masry, Elias. Statistical Inference from Sampled Data. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada342544.

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Gimpel, K., and D. Rudoy. Statistical Inference in Graphical Models. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada482530.

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Batchelder, William H. Statistical Inference for Cultural Consensus Theory. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada605989.

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Hill, Bruce M. Bayesian Nonparametric Prediction and Statistical Inference. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada218473.

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Zhao, Hongwei, and David Oakes. Statistical Inference for Quality-Adjusted Survival Time. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada437896.

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Zhao, Hongwei. Statistical Inference for Quality-Adjusted Survival Time. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada456901.

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