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Journal articles on the topic 'Hidden statistics'

Author: Grafiati

Published: 30 June 2021

Last updated: 29 July 2025

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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

Carminati, Rémi. "Hidden statistics in speckle patterns." Photoniques, no. 110 (October 2021): 32–35. http://dx.doi.org/10.1051/photon/202111032.

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Scattering of coherent light from a disordered material produces a complex distribution of intensity known as a speckle pattern. Speckle patterns are not as random as they appear at first glance. Their statistical properties exhibit universal features, as already recognized in the first paper on laser speckle. The existence of short-range and long-range spatial correlations in speckles has been shown to be fundamental in wave physics. It has also led to the emergence of novel approaches for imaging through scattering media.

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3

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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4

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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5

Kulumbetova, D. B., G. K. Uashov, and E. G. Tokareva. "Kazakhstan’s Labor Market Peculiarities: Beyond Official Statistics." Central Asian Economic Review, no. 1 (May 20, 2025): 62–75. https://doi.org/10.52821/2789-4401-2025-1-62-75.

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The aim of the study is to consider the complex situation of the Kazakhstani labor market, characterized by hidden unemployment, gender inequality and analysis of the factors contributing to this phenomenon, emphasizing the need for a broader understanding of the dynamics of the labor market and the development of recommendations for activating this hidden labor force.The research methods used in this article are based on the collection and analysis of economic information, inductive methods, as well as statistical observations and comparison of the results obtained.Originality / Scientific va

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6

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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7

Tokmachev, Andrey M. "Hidden scales in statistics of citation indicators." Journal of Informetrics 17, no. 1 (2023): 101356. http://dx.doi.org/10.1016/j.joi.2022.101356.

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8

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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9

Constantino G. Medilo, Jr. and Ariel B. Lunzaga. "Analysis of the Current International Migration Using Fractal Statistics." Journal of Educational and Human Resource Development (JEHRD) 4 (December 10, 2016): 38–49. http://dx.doi.org/10.61569/p0zfh946.

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Now that human civilization has entered into the age of information, it is not clear if there are hidden push and pull factors that caused the current global distribution of migration. This study focused on finding the hidden dimensions of recent international migration using an emerging statistical tool called fractal statistical inference, which is a method that stemmed out from the theory of Benoit Mandelbrot. Utilizing the descriptive-correlational design to describe the current pattern of international migration to look for hidden dimensions, this study found out that the 2013 global dist

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10

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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11

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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12

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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13

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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14

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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15

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-

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16

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 ha

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17

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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18

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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19

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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20

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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21

Taranto, Philip, Thomas J. Elliott, and Simon Milz. "Hidden Quantum Memory: Is Memory There When Somebody Looks?" Quantum 7 (April 27, 2023): 991. http://dx.doi.org/10.22331/q-2023-04-27-991.

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In classical physics, memoryless dynamics and Markovian statistics are one and the same. This is not true for quantum dynamics, first and foremost because quantum measurements are invasive. Going beyond measurement invasiveness, here we derive a novel distinction between classical and quantum processes, namely the possibility of hidden quantum memory. While Markovian statistics of classical processes can always be reproduced by a memoryless dynamical model, our main result shows that this is not true in quantum mechanics: We first provide an example of quantum non-Markovianity whose manifestat

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22

Lee, Sora, Woojin Kang, and Jodi Oakman. "Unveiling the Hidden Workers in Australia: Who Are the Hidden Workers and What Makes Them Hidden?" Social Sciences 14, no. 7 (2025): 446. https://doi.org/10.3390/socsci14070446.

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The pattern of labour underutilisation is complex and multifaceted, but research has been focused on unemployment. To explore socio-economic demographics of other forms of labour underutilisation, this study investigates the concept of ‘hidden workers’ using the latest data from the Household, Income and Labour Dynamics in Australia (HILDA) study. Hidden workers are composed of three categories, namely those who are unemployed but actively seeking employment; working part-time but willing and able to work full-time; and not working but are willing and able to work under the right conditions. A

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23

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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24

Fadel, Matteo, and Manuel Gessner. "Entanglement of Local Hidden States." Quantum 6 (February 15, 2022): 651. http://dx.doi.org/10.22331/q-2022-02-15-651.

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Steering criteria are conditions whose violation excludes the possibility of describing the observed measurement statistics with local hidden state (LHS) models. When the available data do not allow to exclude arbitrary LHS models, it may still be possible to exclude LHS models with a specific separability structure. Here, we derive experimentally feasible criteria that put quantitative bounds on the multipartite entanglement of LHS. Our results reveal that separable states may contain hidden entanglement that can be unlocked by measurements on another system, even if no steering between the t

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25

Raja, Valarmathi, and Arulprakash Gowrisankar. "On the Variable Order Fractional Calculus Characterization for the Hidden Variable Fractal Interpolation Function." Fractal and Fractional 7, no. 1 (2022): 34. http://dx.doi.org/10.3390/fractalfract7010034.

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In this study, the variable order fractional calculus of the hidden variable fractal interpolation function is explored. It extends the constant order fractional calculus to the case of variable order. The Riemann–Liouville and the Weyl–Marchaud variable order fractional calculus are investigated for hidden variable fractal interpolation function. Moreover, the conditions for the variable fractional order μ on a specified range are also derived. It is observed that, under certain conditions, the Riemann–Liouville and the Weyl–Marchaud variable order fractional calculus of the hidden variable f

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26

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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27

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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28

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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29

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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30

Kim, Jaesoo, and Dongsoo Shin. "Vertical Relationships with Hidden Interactions." Games 14, no. 6 (2023): 69. http://dx.doi.org/10.3390/g14060069.

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In an agency model with adverse selection, we study how hidden interactions between agents affect the optimal contract. The principal employs two agents who learn their task environments through their involvement. The principal cannot observe the task environments. It is important to note that hidden interactions, such as acts of sabotage or help between the agents, have the potential to alter each other’s task environments. Our analysis encompasses two distinct organizational structures: competition and cooperation. Without hidden interactions, the competitive structure is optimal because the

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31

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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32

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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33

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 an

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34

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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35

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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36

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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37

Takakura, Ryo, Kei Morisue, Issei Watanabe, and Gen Kimura. "Trade-off relations between measurement dependence and hiddenness for separable hidden variable models." Quantum 9 (March 14, 2025): 1662. https://doi.org/10.22331/q-2025-03-14-1662.

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The Bell theorem is explored in terms of a trade-off relation between underlying assumptions within the hidden variable model framework. In this paper, recognizing the incorporation of hidden variables as one of the fundamental assumptions, we propose a measure termed `hidden information&apos; taking account of their distribution. This measure quantifies the number of hidden variables that essentially contribute to the empirical statistics. For factorizable models, hidden variable models that satisfy `locality&apos; without adhering to the measurement independence criterion, we derive

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38

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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39

Zavatone-Veth, Jacob A., Abdulkadir Canatar, Benjamin S. Ruben, and Cengiz Pehlevan. "Asymptotics of representation learning in finite Bayesian neural networks*." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 11 (2022): 114008. http://dx.doi.org/10.1088/1742-5468/ac98a6.

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Abstract Recent works have suggested that finite Bayesian neural networks may sometimes outperform their infinite cousins because finite networks can flexibly adapt their internal representations. However, our theoretical understanding of how the learned hidden layer representations of finite networks differ from the fixed representations of infinite networks remains incomplete. Perturbative finite-width corrections to the network prior and posterior have been studied, but the asymptotics of learned features have not been fully characterized. Here, we argue that the leading finite-width correc

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40

Sen, Indrajit, and Antony Valentini. "Superdeterministic hidden-variables models II: conspiracy." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2243 (2020): 20200214. http://dx.doi.org/10.1098/rspa.2020.0214.

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We prove that superdeterministic models of quantum mechanics are conspiratorial in a mathematically well-defined sense, by further development of the ideas presented in a previous article A . We consider a Bell scenario where, in each run and at each wing, the experimenter chooses one of N devices to determine the local measurement setting. We prove, without assuming any features of quantum statistics, that superdeterministic models of this scenario must have a finely tuned distribution of hidden variables. Specifically, fine-tuning is required so that the measurement statistics depend on the

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41

Tukurah, Haruna A., Prof Rosemary Mbogo, and Dr Francisca Ngala. "The Impact of Hidden Curriculum in Theological Seminaries: A Case Study of JOS-ECWA Theological Seminary." Journal of Education 2, no. 3 (2022): 31–55. http://dx.doi.org/10.70619/vol2iss3pp31-55.

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The impact of hidden curriculum activity within the formal learning sessions of courses is real among theological school learners. In this paper, the study focused on Jos Evangelical Church Winning All (ECWA) Theological Seminary commonly known as JETS as a case study. The questionnaire was served to lecturers, non-academic staff, students, alumni, and immediate community members of the seminary to uncover what could be viewed as their understanding of hidden curriculum. The findings were presented in a statistical method using descriptive statistics, correlation, t-tests, and chi-square analy

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42

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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43

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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44

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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45

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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46

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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47

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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48

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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49

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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50

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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