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Journal articles on the topic 'Hidden statistics'

Author: Grafiati

Published: 30 June 2021

Last updated: 14 February 2022

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1

Flajolet, Philippe, Wojciech Szpankowski, and Brigitte Vallée. "Hidden word statistics." Journal of the ACM 53, no. 1 (2006): 147–83. http://dx.doi.org/10.1145/1120582.1120586.

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2

Zak, Michail. "Hidden statistics of Schrödinger equation." Physics Essays 22, no. 2 (2009): 173–78. http://dx.doi.org/10.4006/1.3123664.

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3

Teague, Michael. "Statistics may ignore hidden crime." Probation Journal 52, no. 1 (2005): 76–77. http://dx.doi.org/10.1177/0264550505050627.

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4

Mevel, L., and L. Finesso. "Asymptotical Statistics of Misspecified Hidden Markov Models." IEEE Transactions on Automatic Control 49, no. 7 (2004): 1123–32. http://dx.doi.org/10.1109/tac.2004.831156.

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5

Ge, Y. F. "Hidden topological Zn symmetry and fractional statistics." Physics Letters A 166, no. 3-4 (1992): 185–87. http://dx.doi.org/10.1016/0375-9601(92)90359-t.

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6

Farcomeni, Alessio. "Hidden Markov partition models." Statistics & Probability Letters 81, no. 12 (2011): 1766–70. http://dx.doi.org/10.1016/j.spl.2011.07.012.

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7

Sebastianelli, Rose, and Susan Trussler. "International Content as Hidden Curriculum in Business Statistics." Journal of Teaching in International Business 18, no. 1 (2006): 73–87. http://dx.doi.org/10.1300/j066v18n01_05.

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8

BAŞÇI, Sıdıka. "Using Numbers to Persuade: Hidden Rhetoric of Statistics." International Econometric Review 12, no. 1 (2020): 75–97. http://dx.doi.org/10.33818/ier.747554.

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9

Silveira, Fernando, and Edmundo de Souza e Silva. "Predicting packet loss statistics with hidden Markov models." ACM SIGMETRICS Performance Evaluation Review 35, no. 3 (2007): 19–21. http://dx.doi.org/10.1145/1328690.1328698.

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10

Morse, Peter K., and Eric I. Corwin. "Hidden symmetries in jammed systems." Journal of Statistical Mechanics: Theory and Experiment 2016, no. 7 (2016): 074009. http://dx.doi.org/10.1088/1742-5468/2016/07/074009.

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11

Elliott, Robert, Nikolaos Limnios, and Anatoliy Swishchuk. "Filtering hidden semi-Markov chains." Statistics & Probability Letters 83, no. 9 (2013): 2007–14. http://dx.doi.org/10.1016/j.spl.2013.05.007.

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12

Rynkiewicz, J. "Asymptotic statistics for multilayer perceptron with ReLU hidden units." Neurocomputing 342 (May 2019): 16–23. http://dx.doi.org/10.1016/j.neucom.2018.11.097.

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13

Pimentel, C. "Derivation of Burst Statistics for Hidden Markov Channel Models." Journal of Communication and Information Systems 12, no. 2 (1997): 108–13. http://dx.doi.org/10.14209/jcis.1997.12.

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14

Rosenius, Marie. "Hidden Ecclesiology: How Statistics Shaped the Church of Sweden." Studia Liturgica 45, no. 1 (2015): 16–28. http://dx.doi.org/10.1177/003932071504500103.

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15

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (2005): 632–42. http://dx.doi.org/10.1239/jap/1127322017.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.

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16

Scott, Steven L., Gareth M. James, and Catherine A. Sugar. "Hidden Markov Models for Longitudinal Comparisons." Journal of the American Statistical Association 100, no. 470 (2005): 359–69. http://dx.doi.org/10.1198/016214504000001592.

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17

Rue, Havard, Ingelin Steinsland, and Sveinung Erland. "Approximating hidden Gaussian Markov random fields." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 4 (2004): 877–92. http://dx.doi.org/10.1111/j.1467-9868.2004.b5590.x.

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18

Bogdanov, I. I. "Hidden matrix semirings." Journal of Mathematical Sciences 135, no. 5 (2006): 3276–80. http://dx.doi.org/10.1007/s10958-006-0157-z.

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19

Bordes, Laurent, and Pierre Vandekerkhove. "Statistical Inference for Partially Hidden Markov Models." Communications in Statistics - Theory and Methods 34, no. 5 (2005): 1081–104. http://dx.doi.org/10.1081/sta-200056845.

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20

Golinelli, O., and K. Mallick. "Hidden symmetries in the asymmetric exclusion process." Journal of Statistical Mechanics: Theory and Experiment 2004, no. 12 (2004): P12001. http://dx.doi.org/10.1088/1742-5468/2004/12/p12001.

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21

Yungelson, L. R., and A. V. Tutukov. "Statistics of Wolf-Rayet Binaries." Symposium - International Astronomical Union 143 (1991): 459–64. http://dx.doi.org/10.1017/s0074180900045629.

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A theoretical model of the ensemble of galactic Wolf-Rayet stars is constructed, assuming that all of them are members of either close or wide binaries. The model provides a reasonable explanation of the observed number of WR stars, their distribution over masses, mass ratios of components in binary systems, and spatial velocities. It predicts that up to 10 % of the apparently single WR stars have relativistic companions hidden inside thick stellar winds.

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22

Altman, Douglas G., and J. Patrick Royston. "The hidden effect of time." Statistics in Medicine 7, no. 6 (1988): 629–37. http://dx.doi.org/10.1002/sim.4780070602.

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23

Chu, Clara, Woollcott Smith, and Andrew Solow. "A hidden species-area curve." Environmental and Ecological Statistics 21, no. 1 (2013): 113–24. http://dx.doi.org/10.1007/s10651-013-0247-2.

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24

Saber Raza, Mahdi, and Mark Broom. "Survival analysis modeling with hidden censoring." Journal of Statistical Theory and Practice 10, no. 2 (2016): 375–88. http://dx.doi.org/10.1080/15598608.2016.1152205.

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25

Mongillo, Gianluigi, and Sophie Deneve. "Online Learning with Hidden Markov Models." Neural Computation 20, no. 7 (2008): 1706–16. http://dx.doi.org/10.1162/neco.2008.10-06-351.

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We present an online version of the expectation-maximization (EM) algorithm for hidden Markov models (HMMs). The sufficient statistics required for parameters estimation is computed recursively with time, that is, in an online way instead of using the batch forward-backward procedure. This computational scheme is generalized to the case where the model parameters can change with time by introducing a discount factor into the recurrence relations. The resulting algorithm is equivalent to the batch EM algorithm, for appropriate discount factor and scheduling of parameters update. On the other hand, the online algorithm is able to deal with dynamic environments, i.e., when the statistics of the observed data is changing with time. The implications of the online algorithm for probabilistic modeling in neuroscience are briefly discussed.

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26

Lagun, A. "USAGE OF THE STEGANOGRAPHIC ALGORITHMS FOR TEXT INFORMATION HIDING." Bulletin of Lviv State University of Life Safety, no. 18 (December 31, 2018): 49–56. http://dx.doi.org/10.32447/20784643.18.2018.04.

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Today cryptographic and steganographic systems provide the best information security of society. Cryptography transforms information into the incomprehensible form with using the cryptographic keys and algorithms. Steganography hides the secret information in unknown place of object. The steganographic algorithms, which hide message in text container, are researched in the article. For process of hiding are used the text file-container properties. The hide message converts to the binary numbers system. User puts ones or zeros into the defined places of text file-container. These places have special characteristics. There may be two types of hiding: insertion and replacement. In case of insertion the hiding message adds to file-container with using invisible characters in viewing mode of text file. Then the size of full container with hided message is bigger than size of empty container. If used the replacing method then the characters of file-container replace to other characters that are almost the same as the first ones. For example, anyone is possible replacement of characters that have the same appearance in different languages. In this case the sizes of the empty and filled container remain the same. One of the simplest hiding methods is insertion the variable quantity of the space characters between words of text file. Suppose, that zero of hidden message is coded by one space character and one - is coded by two space characters. Therefore, depending on hidden message one or two space characters are located in different places of the text. Also, the author considers another hiding type, which uses the same view of some characters of different languages. If you look at the characters in Ukrainian and English, than the 18 characters in the each language is the same – 'a','c','e','i','o','p','x','A','B','C','E','H','I','K','O','P','T','X'. When hiding for the values of zeros in hidden message the file-container remains the same, and for the values of ones in hidden message the characters of language file-container replace to the same characters of another language (Ukrainian-English).The results of the algorithm work show us, that when using characters from different languages in the hiding process, the full file-container is much smaller than when encoding the space characters. The last algorithm which is considered in work uses tail space characters. It forms a filled container with enlarged text strings depending on the number of space characters which the hidden message determines. One character of hidden message is written in two file-container text strings. In particular the binary representation of each character is divided into two parts with four bits, and at the end of each text string is written no more than 15 space characters. The number of space characters corresponds to the decimal value of each part. To ensure hiding of secret message full container has the form aligned to the left edge of the text. Considered algorithms of hiding message in text container are used for the confidential information defense. Algorithms, which use insertion of invisible characters, allow hiding the amount of information that corresponds to the number of space characters with certain characteristics. The most of replacement algorithms hide more information than insertion algorithms. Also replacement algorithms do not change file-container size. For example, algorithm, which replace characters of different alphabets, hides such amount of information, which depends on the statistics of used languages. The most problem of using text containers is providing its steganographic defense. In particular, if user enables the unprintable character view in a text editor, then could see the some statistic of location invisible symbols added by the insertion methods. Therefore decoding of hidden message is simplified. The hidden message with using replacement algorithms is more defensible, but using of compression algorithms to the full container deletes the hidden information.

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27

Nakagawa, Seiichi, and Kazumasa Yamamoto. "Speech recognition using hidden Markov models based on segmental statistics." Systems and Computers in Japan 28, no. 7 (1997): 31–38. http://dx.doi.org/10.1002/(sici)1520-684x(19970630)28:7<31::aid-scj4>3.0.co;2-k.

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28

Visser, Ingmar, Maartje E. J. Raijmakers, and Peter C. M. Molenaar. "Fitting Hidden Markov Models to Psychological Data." Scientific Programming 10, no. 3 (2002): 185–99. http://dx.doi.org/10.1155/2002/874560.

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Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

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29

Salakhutdinov, Ruslan, and Geoffrey Hinton. "An Efficient Learning Procedure for Deep Boltzmann Machines." Neural Computation 24, no. 8 (2012): 1967–2006. http://dx.doi.org/10.1162/neco_a_00311.

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We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.

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30

Turner, T. Rolf, Murray A. Cameron, and Peter J. Thomson. "Hidden Markov chains in generalized linear models." Canadian Journal of Statistics 26, no. 1 (1998): 107–25. http://dx.doi.org/10.2307/3315677.

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31

Martino, Andrea, Giuseppina Guatteri, and Anna Maria Paganoni. "Hidden Markov Models for multivariate functional data." Statistics & Probability Letters 167 (December 2020): 108917. http://dx.doi.org/10.1016/j.spl.2020.108917.

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32

Ridall, P. G., and A. N. Pettitt. "BAYESIAN HIDDEN MARKOV MODELS FOR LONGITUDINAL COUNTS." Australian New Zealand Journal of Statistics 47, no. 2 (2005): 129–45. http://dx.doi.org/10.1111/j.1467-842x.2005.00379.x.

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33

Aggarwal, M. L., and Renu Kaul. "Hidden projection properties of some optimal designs." Statistics & Probability Letters 43, no. 1 (1999): 87–92. http://dx.doi.org/10.1016/s0167-7152(98)00249-1.

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34

ARRIBAS-GIL, ANA, ELISABETH GASSIAT, and CATHERINE MATIAS. "Parameter Estimation in Pair-hidden Markov Models." Scandinavian Journal of Statistics 33, no. 4 (2006): 651–71. http://dx.doi.org/10.1111/j.1467-9469.2006.00513.x.

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35

Fuh, Cheng-Der. "SPRT and CUSUM in hidden Markov models." Annals of Statistics 31, no. 3 (2003): 942–77. http://dx.doi.org/10.1214/aos/1056562468.

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36

Fiecas, Mark, Jürgen Franke, Rainer von Sachs, and Joseph Tadjuidje Kamgaing. "Shrinkage Estimation for Multivariate Hidden Markov Models." Journal of the American Statistical Association 112, no. 517 (2017): 424–35. http://dx.doi.org/10.1080/01621459.2016.1148608.

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37

Lakner, Peter, and Halina Frydman. "Maximum likelihood estimation of hidden Markov processes." Annals of Applied Probability 13, no. 4 (2003): 1296–312. http://dx.doi.org/10.1214/aoap/1069786500.

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38

Rydén, Tobias. "Estimating the Order of Hidden Markov Models." Statistics 26, no. 4 (1995): 345–54. http://dx.doi.org/10.1080/02331889508802501.

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39

Spezia, Luigi, Nial Friel, and Alessandro Gimona. "Spatial hidden Markov models and species distributions." Journal of Applied Statistics 45, no. 9 (2017): 1595–615. http://dx.doi.org/10.1080/02664763.2017.1386771.

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40

Mossel, Elchanan, and Sébastien Roch. "Learning nonsingular phylogenies and hidden Markov models." Annals of Applied Probability 16, no. 2 (2006): 583–614. http://dx.doi.org/10.1214/105051606000000024.

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41

Macdonald, Iain L., and David Raubenheimer. "Hidden Markov Models and Animal Behaviour." Biometrical Journal 37, no. 6 (1995): 701–12. http://dx.doi.org/10.1002/bimj.4710370606.

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42

French, O. E., K. I. Hopcraft, E. Jakeman, and T. J. Shepherd. "Intrinsic and measured statistics of discrete stochastic populations." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2099 (2008): 2929–48. http://dx.doi.org/10.1098/rspa.2008.0110.

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The notion that the nature of a measurement is critical to its outcome is usually associated with quantum phenomena. In this paper, we show that the observed statistical properties are also a function of the measurement technique in the case of simple classical populations. In particular, the measured and intrinsic statistics of a single population may be different, while correlation and transfer of individuals between two populations may be hidden from the observer.

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43

Khasminskii, Rafail Z., and Yury A. Kutoyants. "On parameter estimation of hidden telegraph process." Bernoulli 24, no. 3 (2018): 2064–90. http://dx.doi.org/10.3150/16-bej920.

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44

Geman, Hélyette, Dilip B. Madan, and Marc Yor. "Stochastic volatility, jumps and hidden time changes." Finance and Stochastics 6, no. 1 (2002): 63–90. http://dx.doi.org/10.1007/s780-002-8401-3.

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45

Cohen, P. L., M. A. Olson, and C. B. Fogarty. "Multivariate one-sided testing in matched observational studies as an adversarial game." Biometrika 107, no. 4 (2020): 809–25. http://dx.doi.org/10.1093/biomet/asaa024.

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Summary We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics to combat nature’s presentation of the worst-case pattern of hidden bias. The corresponding optimization problem is convex, and can be solved efficiently even for reasonably sized observational studies. Asymptotically, the test statistic converges to a chi-bar-squared distribution under the null, a common distribution in order-restricted statistical inference. The test attains the largest possible design sensitivity over a class of coherent test statistics, and facilitates one-sided sensitivity analyses for individual outcome variables while maintaining familywise error control through its incorporation into closed testing procedures.

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46

Sisson, Scott. "Hidden Markov Models for Bioinformatics." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 1 (2004): 194–95. http://dx.doi.org/10.1111/j.1467-985x.2004.298_13.x.

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47

Udomboso, Christopher Godwin. "On the Level of Precision of a Heterogeneous Transfer Function in a Statistical Neural Network Model." Journal of Modern Applied Statistical Methods 19, no. 1 (2021): 2–16. http://dx.doi.org/10.22237/jmasm/1608553560.

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A heterogeneous function of the statistical neural network is presented from two transfer functions: symmetric saturated linear and hyperbolic tangent sigmoid. The precision of the derived heterogeneous model over their respective homogeneous forms are established, both at increased sample sizes hidden neurons. Results further show the sensitivity of the heterogeneous model to increase in hidden neurons.

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48

VASCONCELOS, RITA. "THE RELEVANCE OF SPATIAL STATISTICS ON THE STATISTICAL MODEL BUILDING FOR CORONARY HEART DISEASE." Journal of Biological Systems 03, no. 03 (1995): 661–75. http://dx.doi.org/10.1142/s0218339095000617.

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For a long time, it has been widely acknowledged that putting data on a map underlines important features, and helps in the understanding and interpretation of the real world. Recent and extensive developments of spatial statistics and of geostatistics show the growing importance of this field. Our aim was to help physicians to interpret a very large database on heart diseases (acute myocardial infarction and angina pectoris) on the Madeira Islands. Besides standard techniques, such as loglinear models fitting, we decided to explore the spatial aspect of the question, and to bring in to the analysis recent advances in exploratory and robust data analysis. We show the relevance of spatial statistics on the detection of "hidden" variables.

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49

Mastromatteo, Iacopo. "Apparent impact: the hidden cost of one-shot trades." Journal of Statistical Mechanics: Theory and Experiment 2015, no. 6 (2015): P06022. http://dx.doi.org/10.1088/1742-5468/2015/06/p06022.

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50

Mackay, Rachel J. "Estimating the order of a hidden markov model." Canadian Journal of Statistics 30, no. 4 (2002): 573–89. http://dx.doi.org/10.2307/3316097.

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