Relevant bibliographies by topics / Distributed Sequential Hypothesis Testing

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Distributed Sequential Hypothesis Testing'

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Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Distributed Sequential Hypothesis Testing"

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Li, Zishuo, Yilin Mo, and Fei Hao. "Distributed Sequential Hypothesis Testing With Byzantine Sensors." IEEE Transactions on Signal Processing 69 (2021): 3044–58. http://dx.doi.org/10.1109/tsp.2021.3075147.

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Li, Shang, and Xiaodong Wang. "Distributed Sequential Hypothesis Testing With Quantized Message-Exchange." IEEE Transactions on Information Theory 66, no. 1 (January 2020): 350–67. http://dx.doi.org/10.1109/tit.2019.2947494.

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Li, Shang, and Xiaodong Wang. "Fully Distributed Sequential Hypothesis Testing: Algorithms and Asymptotic Analyses." IEEE Transactions on Information Theory 64, no. 4 (April 2018): 2742–58. http://dx.doi.org/10.1109/tit.2018.2806961.

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Sahu, Anit Kumar, and Soummya Kar. "Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing." IEEE Transactions on Signal Processing 64, no. 1 (January 2016): 89–103. http://dx.doi.org/10.1109/tsp.2015.2478737.

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Lee, Ji-Woong, and Geir E. Dullerud. "Dynamic sequential team multi-hypothesis testing under uniformly distributed nonstationary observations." Systems & Control Letters 57, no. 12 (December 2008): 1030–36. http://dx.doi.org/10.1016/j.sysconle.2008.06.007.

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Pantula, Sastry G. "Testing for Unit Roots in Time Series Data." Econometric Theory 5, no. 2 (August 1989): 256–71. http://dx.doi.org/10.1017/s0266466600012421.

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Let Yt satisfy the stochastic difference equation for t = 1,2,…, where et are independent and identically distributed random variables with mean zero and variance σ2 and the initial conditions (Y−p+1,…, Y0) are fixed constants. It is assumed that the process is invertible and that the true, but unknown, roots m1,m2,…,mp of satisfy the hypothesis Hd: m1 = … = md = 1 and |mj| < 1 for j = d + 1,…,p. We present a reparameterization of the model for Yt that is convenient for testing the hypothesis Hd. We consider the asymptotic properties of (i) a likelihood ratio type “F-statistic” for testing the hypothesis Hd, (ii) a likelihood ratio type t-statistic for testing the hypothesis Hd against the alternative Hd−1. Using these asymptotic results, we obtain two sequential testing procedures that are asymptotically consistent.
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Tinochai, Khanittha, Katechan Jampachaisri, Yupaporn Areepong, and Saowanit Sukparungsee. "Empirical Bayes Prediction in a Sequential Sampling Plan Based on Loss Functions." Processes 7, no. 12 (December 11, 2019): 944. http://dx.doi.org/10.3390/pr7120944.

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The application of empirical Bayes for lot inspection in sequential sampling plans is usually conducted to estimate the proportion of defective items in the lot rather than for hypothesis testing of the variables’ process mean. In this paper, we propose the use of empirical Bayes in a sequential sampling plan variables’ process mean testing under a squared error loss function and precautionary loss function, for which the prediction is performed to estimate a sequence of the mean when the data are normally distributed in the case of a known mean and unknown variance. The proposed plans are compared with the sequential sampling plan. The proposed techniques yielded smaller average sample number (ASN) and provided higher probability of acceptance (Pa) than the sequential sampling plan.
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Zacks, Shelemyahu. "Two-stage and sequential sampling for estimation and testing with prescribed precision." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1650004. http://dx.doi.org/10.1142/s2425038416500048.

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Statistical data analysis includes several phases. First, there is the phase of data collection. Second, there is the phase of analysis and inference. The two phases are interconnected. There are two types of data analysis. One type is called parametric and the other type is nonparametric. In the present paper, we discuss parametric inference. In parametric inference, we model the results of a given experiment as realization of random variables having a particular distribution, which is specified by its parameters. A random sample is a sequence of independent and identically distributed (i.i.d.) random variables. Statistics are functions of the data in the sample, which do not involve unknown parameters. A statistical inference is based on statistics of a given sample. We discuss two kinds of parametric inference. Estimating the values of parameters, or testing hypotheses concerning the parameters in either kind of inference, we are concerned with the accuracy and precision of the results. In estimation of parameters, the results are precise if, with high probability, they belong to a specified neighborhoods of the parameters. In testing hypotheses, one has to decide which one of two or several hypotheses should be accepted. Hypotheses which are not accepted are rejected. We distinguish between two types of errors. Type I error is the one committed by rejecting a correct hypothesis. Type II is that of accepting a wrong hypothesis. It is desired that both types of errors will occur simultaneously with small probabilities. Both precision in estimation or small error probabilities in testing depend on the statistics used (estimators or test functions) and on the sample size. In this paper, we present sampling procedures that attain the desired objectives. In Sec. 2, we discuss estimation of the parameters of a binomial distribution. In Sec. 3, more general results about estimation of expected values are presented. In Sec. 4, we discuss the Wald Sequential Probability Ratio Test (SPRT), which has optimal properties for testing two simple hypotheses.
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Li, Cheng, and Guangping Zhu. "Underwater multi-sensor Bayesian distributed detection and data fusion." MATEC Web of Conferences 283 (2019): 07014. http://dx.doi.org/10.1051/matecconf/201928307014.

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The relationship of decision rule of sensor for each other is relevant to data fusion, so different topological network of sensors usually results in different performance. This paper considers the parallel and sequential topological data fusion in some detail and applies it to detect underwater signal with three sensors which respectively detects the energy, impulse width and frequency. In this paper, the signal detection model is specified for binary hypotheses testing problem. This paper compares the probabilities of error and Bayesian risk under both topologies corresponding to different value of priori probabilities of two hypotheses. Usually, the parallel architecture of detection and fusion with three sensors as specified in this paper needs to solve eleven nonlinear equations to determine the thresholds of three sensors and fusion rules, as to sequential architecture, five nonlinear equations need to be solved. So, this paper attempts to search numerical solutions for the parallel and sequential architecture of distributed detection and data fusion. Finally, this signal detection problem is simulated.
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Lepora, Nathan F., and Kevin N. Gurney. "The Basal Ganglia Optimize Decision Making over General Perceptual Hypotheses." Neural Computation 24, no. 11 (November 2012): 2924–45. http://dx.doi.org/10.1162/neco_a_00360.

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The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probability distributions, which raises the proposal to a practically realizable theory over generic perceptual hypotheses. We also show that the evidence in this model can represent either log likelihoods, log-likelihood ratios, or log odds, all leading proposals for the cortical processing of sensory data. For these reasons, we claim that the basal ganglia optimize decision making over general perceptual hypotheses represented in cortex. The relation of this theory to cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning is discussed.
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Dissertations / Theses on the topic "Distributed Sequential Hypothesis Testing"

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Wissinger, John W. (John Weakley). "Distributed nonparametric training algorithms for hypothesis testing networks." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12006.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 495-502).
by John W. Wissinger.
Ph.D.
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Durazo-Arvizu, Ramon Angel. "Bias-adjusted estimates of survival following group sequential hypothesis testing." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186830.

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It is known that repeated use of fixed sample hypothesis testing causes inflation of the type one statistical error. Since in a group sequential design a study is stopped preferentially when extreme data have been observed, we expect that the usual estimators are biased toward the extremes. We demonstrate through simulations that the fixed sample survival estimators are biased if computed after stopping a group sequential trial in which stopping is based on survival statistics. The problem is first studied under no censored data and then with the more realistic setting of censoring. In particular, we studied the two sample problem, that is, the estimation of survival following repeatedly testing the equality of two survival distributions. We first investigated some potential measures of bias. Bias-adjusted estimators of survival of the two distributions were suggested first based on the Whitehead (1986, (33), (34)) bias-adjusted estimator. This was done for the proportional hazards model. We found that this approach is not well behaved for the unconditional measures of bias, whereas it behaves well for the conditional measures, provided that the power of the test to detect the true value of the parameter was not too large (< 94%). However, for large powers, it has a tendency to overestimate the absolute difference of the two distributions. A generalized Whitehead bias-adjusted semiparametric estimator was suggested. This is not only applicable to the proportional hazards model, but to other survival data models. The conditional semiparametric estimator was found to behave well for all the data models and powers considered. The unconditional semiparametric estimator produced adjustments of survival in the right direction for the proportional hazards model, but not for the others. It was also seen to be applicable to the two main group sequential designs, namely the Pocock and O'Brien-Fleming designs. Finally, the findings were applied to a leukemia clinical trial. The control treatment (Daunorubicin) was compared to the new treatment (Idarubicin) based on a 0.05-level O'Brien-Fleming group sequential design with a maximum of 4 analyses and equal number of observations per group.
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Wang, Yan. "Asymptotic theory for decentralized sequential hypothesis testing problems and sequential minimum energy design algorithm." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41082.

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The dissertation investigates asymptotic theory of decentralized sequential hypothesis testing problems as well as asymptotic behaviors of the Sequential Minimum Energy Design (SMED). The main results are summarized as follows. 1.We develop the first-order asymptotic optimality theory for decentralized sequential multi-hypothesis testing under a Bayes framework. Asymptotically optimal tests are obtained from the class of "two-stage" procedures and the optimal local quantizers are shown to be the "maximin" quantizers that are characterized as a randomization of at most M-1 Unambiguous Likelihood Quantizers (ULQ) when testing M >= 2 hypotheses. 2. We generalize the classical Kullback-Leibler inequality to investigate the quantization effects on the second-order and other general-order moments of log-likelihood ratios. It is shown that a quantization may increase these quantities, but such an increase is bounded by a universal constant that depends on the order of the moment. This result provides a simpler sufficient condition for asymptotic theory of decentralized sequential detection. 3. We propose a class of multi-stage tests for decentralized sequential multi-hypothesis testing problems, and show that with suitably chosen thresholds at different stages, it can hold the second-order asymptotic optimality properties when the hypotheses testing problem is "asymmetric." 4. We characterize the asymptotic behaviors of SMED algorithm, particularly the denseness and distributions of the design points. In addition, we propose a simplified version of SMED that is computationally more efficient.
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Escamilla, Pierre. "On cooperative and concurrent detection in distributed hypothesis testing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2019. http://www.theses.fr/2019IPPAT007.

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L’inférence statistique prend une place prépondérante dans le développement des nouvelles technologies et inspire un grand nombre d’algorithmes dédiés à des tâches de détection, d’identification et d’estimation. Cependant il n’existe pas de garantie théorique pour les performances de ces algorithmes. Dans cette thèse, nous considérons un réseau simplifié de capteurs communicant sous contraintes pour tenter de comprendre comment des détecteurs peuvent se partager au mieux les informations à leur disposition pour détecter un même événement ou des événements distincts. Nous investiguons différents aspects de la coopération entre détecteurs et comment des besoins contradictoires peuvent être satisfaits au mieux dans le cas de tâches de détection. Plus spécifiquement nous étudions un problème de test d’hypothèse où chaque détecteur doit maximiser l’exposant de décroissance de l’erreur de Type II sous une contrainte d’erreur de Type I donnée. Comme il y a plusieurs détecteurs intéressés par des informations distinctes, un compromis entre les vitesses de décroissance atteignables va apparaître. Notre but est de caractériser la région des compromis possibles entre exposants d’erreurs de Type II. Dans le cadre des réseaux de capteurs massifs, la quantité d’information est souvent soumise à des limitations pour des raisons de consommation d’énergie et de risques de saturation du réseau. Nous étudions donc, en particulier, le cas du régime de communication à taux de compression nul (i.e. le nombre de bits des messages croit de façon sous-linéaire avec le nombre d’observations). Dans ce cas, nous caractérisons complètement la région des exposants d’erreurs de Type II dans les configurations où les détecteurs peuvent avoir des buts différents. Nous étudierons aussi le cas d’un réseau avec des taux de compressions positifs (i.e. le nombre de bits des messages augmente de façon linéaire avec le nombre d’observations). Dans ce cas, nous présentons des sous-parties de la région des exposants d’erreur de Type II. Enfin, nous proposons dans le cas d’un problème point à point avec un taux de compression positif une caractérisation complète de l’exposant de l’erreur de Type II optimal pour une famille de tests gaussiens
Statistical inference plays a major role in the development of new technologies and inspires a large number of algorithms dedicated to detection, identification and estimation tasks. However, there is no theoretical guarantee for the performance of these algorithms. In this thesis we try to understand how sensors can best share their information in a network with communication constraints to detect the same or distinct events. We investigate different aspects of detector cooperation and how conflicting needs can best be met in the case of detection tasks. More specifically we study a hypothesis testing problem where each detector must maximize the decay exponent of the Type II error under a given Type I error constraint. As the detectors are interested in different information, a compromise between the achievable decay exponents of the Type II error appears. Our goal is to characterize the region of possible trade-offs between Type II error decay exponents. In massive sensor networks, the amount of information is often limited due to energy consumption and network saturation risks. We are therefore studying the case of the zero rate compression communication regime (i.e. the messages size increases sub-linearly with the number of observations). In this case we fully characterize the region of Type II error decay exponent. In configurations where the detectors have or do not have the same purposes. We also study the case of a network with positive compression rates (i.e. the messages size increases linearly with the number of observations). In this case we present subparts of the region of Type II error decay exponent. Finally, in the case of a single sensor single detector scenario with a positive compression rate, we propose a complete characterization of the optimal Type II error decay exponent for a family of Gaussian hypothesis testing problems
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Krantz, Elizabeth. "Sharpening the Boundaries of the Sequential Probability Ratio Test." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1169.

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In this thesis, we present an introduction to Wald’s Sequential Probability Ratio Test (SPRT) for binary outcomes. Previous researchers have investigated ways to modify the stopping boundaries that reduce the expected sample size for the test. In this research, we investigate ways to further improve these boundaries. For a given maximum allowable sample size, we develop a method intended to generate all possible sets of boundaries. We then find the one set of boundaries that minimizes the maximum expected sample size while still preserving the nominal error rates. Once the satisfying boundaries have been created, we present the results of simulation studies conducted on these boundaries as a means for analyzing both the expected number of observations and the amount of variability in the sample size required to make a decision in the test.
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Pereira, Pratap 1969. "Digitizing Technique with Sequential Hypothesis Testing For Reverse Engineering Using Coordinate Measuring Machines /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487934589976702.

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Ramdas, Aaditya Kumar. "Computational and Statistical Advances in Testing and Learning." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/790.

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This thesis makes fundamental computational and statistical advances in testing and estimation, making critical progress in theory and application of classical statistical methods like classification, regression and hypothesis testing, and understanding the relationships between them. Our work connects multiple fields in often counter-intuitive and surprising ways, leading to new theory, new algorithms, and new insights, and ultimately to a cross-fertilization of varied fields like optimization, statistics and machine learning. The first of three thrusts has to do with active learning, a form of sequential learning from feedback-driven queries that often has a provable statistical advantage over passive learning. We unify concepts from two seemingly different areas—active learning and stochastic firstorder optimization. We use this unified view to develop new lower bounds for stochastic optimization using tools from active learning and new algorithms for active learning using ideas from optimization. We also study the effect of feature noise, or errors-in-variables, on the ability to actively learn. The second thrust deals with the development and analysis of new convex optimization algorithms for classification and regression problems. We provide geometrical and convex analytical insights into the role of the margin in margin-based classification, and develop new greedy primal-dual algorithms for non-linear classification. We also develop a unified proof for convergence rates of randomized algorithms for the ordinary least squares and ridge regression problems in a variety of settings, with the purpose of investigating which algorithm should be utilized in different settings. Lastly, we develop fast state-of-the-art numerically stable algorithms for an important univariate regression problem called trend filtering with a wide variety of practical extensions. The last thrust involves a series of practical and theoretical advances in nonparametric hypothesis testing. We show that a smoothedWasserstein distance allows us to connect many vast families of univariate and multivariate two sample tests. We clearly demonstrate the decreasing power of the families of kernel-based and distance-based two-sample tests and independence tests with increasing dimensionality, challenging existing folklore that they work well in high dimensions. Surprisingly, we show that these tests are automatically adaptive to simple alternatives and achieve the same power as other direct tests for detecting mean differences. We discover a computation-statistics tradeoff, where computationally more expensive two-sample tests have a provable statistical advantage over cheaper tests. We also demonstrate the practical advantage of using Stein shrinkage for kernel independence testing at small sample sizes. Lastly, we develop a novel algorithmic scheme for performing sequential multivariate nonparametric hypothesis testing using the martingale law of the iterated logarithm to near-optimally control both type-1 and type-2 errors. One perspective connecting everything in this thesis involves the closely related and fundamental problems of linear regression and classification. Every contribution in this thesis, from active learning to optimization algorithms, to the role of the margin, to nonparametric testing fits in this picture. An underlying theme that repeats itself in this thesis, is the computational and/or statistical advantages of sequential schemes with feedback. This arises in our work through comparing active with passive learning, through iterative algorithms for solving linear systems instead of direct matrix inversions, and through comparing the power of sequential and batch hypothesis tests.
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Atta-Asiamah, Ernest. "Distributed Inference for Degenerate U-Statistics with Application to One and Two Sample Test." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31777.

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In many hypothesis testing problems such as one-sample and two-sample test problems, the test statistics are degenerate U-statistics. One of the challenges in practice is the computation of U-statistics for a large sample size. Besides, for degenerate U-statistics, the limiting distribution is a mixture of weighted chi-squares, involving the eigenvalues of the kernel of the U-statistics. As a result, it’s not straightforward to construct the rejection region based on this asymptotic distribution. In this research, we aim to reduce the computation complexity of degenerate U-statistics and propose an easy-to-calibrate test statistic by using the divide-and-conquer method. Specifically, we randomly partition the full n data points into kn even disjoint groups, and compute U-statistics on each group and combine them by averaging to get a statistic Tn. We proved that the statistic Tn has the standard normal distribution as the limiting distribution. In this way, the running time is reduced from O(n^m) to O( n^m/km_n), where m is the order of the one sample U-statistics. Besides, for a given significance level , it’s easy to construct the rejection region. We apply our method to the goodness of fit test and two-sample test. The simulation and real data analysis show that the proposed test can achieve high power and fast running time for both one and two-sample tests.
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Tout, Karim. "Automatic Vision System for Surface Inspection and Monitoring : Application to Wheel Inspection." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0008.

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L'inspection visuelle des produits industriels a toujours été l'une des applications les plus reconnues du contrôle de qualité. Cette inspection reste en grande partie un processus manuel mené par des opérateurs et ceci rend l’opération peu fiable. Par conséquent, il est nécessaire d'automatiser cette inspection pour une meilleure efficacité. L'objectif principal de cette thèse est de concevoir un système d'inspection visuelle automatique pour l'inspection et la surveillance de la surface du produit. L'application spécifique de l'inspection de roues est considérée pour étudier la conception et l'installation du système d'imagerie. Ensuite, deux méthodes d'inspection sont développées : une méthode de détection des défauts à la surface du produit et une méthode de détection d’un changement brusque dans les paramètres du processus d’inspection non stationnaire. Parce que dans un contexte industriel, il est nécessaire de contrôler le taux de fausses alarmes, les deux méthodes proposées s’inscrivent dans le cadre de la théorie de la décision statistique. Un modèle paramétrique des observations est développé. Les paramètres du modèle sont estimés afin de concevoir un test statistique dont les performances sont analytiquement connues. Enfin, l'impact de la dégradation de l'éclairage sur la performance de détection des défauts est étudié afin de prédire les besoins de maintenance du système d'imagerie. Des résultats numériques sur un grand nombre d'images réelles mettent en évidence la pertinence de l'approche proposée
Visual inspection of finished products has always been one of the basic and most recognized applications of quality control in any industry. This inspection remains largely a manual process conducted by operators, and thus faces considerable limitations that make it unreliable. Therefore, it is necessary to automatize this inspection for better efficiency. The main goal of this thesis is to design an automatic visual inspection system for surface inspection and monitoring. The specific application of wheel inspection is considered to study the design and installation setup of the imaging system. Then, two inspection methods are developed: a defect detection method on the product’s surface and a change-point detection method in the parameters of the non-stationary inspection process. Because in an industrial context it is necessary to control the false alarm rate, the two proposed methods are cast into the framework of hypothesis testing theory. A parametric approach is proposed to model the non-anomalous part of the observations. The model parameters are estimated to design a statistical test whose performances are analytically known. Finally, the impact of illumination degradation on the defect detection performance is studied in order to predict the maintenance needs of the imaging system. Numerical results on a large set of real images highlight the relevance of the proposed approach
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Kang, Shin-jae. "Korea's export performance : three empirical essays." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/767.

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Books on the topic "Distributed Sequential Hypothesis Testing"

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Gül, Gökhan. Robust and Distributed Hypothesis Testing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49286-5.

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Li, Shang. Cooperative Sequential Hypothesis Testing in Multi-Agent Systems. [New York, N.Y.?]: [publisher not identified], 2017.

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Sequential tests. Leipzig, Germany: B.G. Teubner, 1985.

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Siegmund, David. Sequential analysis: Tests and confidence intervals. New York: Springer-Verlag, 1985.

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

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Sequential analysis: Tests and confidence intervals. New York: Springer-Verlag, 1985.

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Lemeshko, Boris, and Irina Veretel'nikova. Criteria for testing hypotheses about randomness and the absence of a trend. Application Guide. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1587437.

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The monograph discusses the application of statistical criteria aimed at testing hypotheses about the absence of a trend in the analyzed samples. The rejection of such a hypothesis gives grounds to consider the analyzed data as samples of independent equally distributed random variables. We consider a set of special criteria aimed at testing such hypotheses, as well as a set of criteria for the uniformity of laws, the uniformity of averages and the uniformity of variances, which can also be used for these purposes. The disadvantages and advantages of various criteria are emphasized, the application of criteria in conditions of violation of standard assumptions is considered. Estimates of the power of the criteria are given, which allows you to navigate when choosing the most preferred criteria. Following the recommendations will ensure the correctness and increase the validity of statistical conclusions when analyzing data. It is intended for specialists who are interested in the application of statistical methods for the analysis of various aspects and trends of the surrounding reality and who are in contact with the processing of experimental results, the need for data analysis in their activities. It will be useful for engineers, researchers, specialists of various profiles (doctors, biologists, sociologists, economists, etc.) who face the need for statistical analysis of experimental results in their activities. It will also be useful for university teachers, graduate students and students.
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Tartakovsky, Alexander. Sequential Change Detection and Hypothesis Testing. Taylor & Francis Group, 2021.

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Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer, 2018.

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Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer International Publishing AG, 2017.

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Book chapters on the topic "Distributed Sequential Hypothesis Testing"

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Lavigna, Anthony, Armand M. Makowski, and John S. Baras. "A Continuous—Time Distributed Version of Wald’s Sequential Hypothesis Testing Problem." In Analysis and Optimization of Systems, 533–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0007587.

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Young, Linda J., and Jerry H. Young. "Sequential Hypothesis Testing." In Statistical Ecology, 153–90. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2829-3_5.

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Ahlswede, Rudolf. "Hypothesis Testing Under Communication Constraints." In Probabilistic Methods and Distributed Information, 509–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00312-8_22.

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Shieh, J. S., and Y. L. Tong. "Sequential Multi-Hypothesis Testing in Software Reliability." In Lifetime Data: Models in Reliability and Survival Analysis, 291–98. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5654-8_38.

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Varshney, Pramod K. "Information Theory and Distributed Hypothesis Testing." In Distributed Detection and Data Fusion, 233–50. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_7.

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Almudevar, Anthony. "Applications of Sequential Methods in Multiple Hypothesis Testing." In Statistical Modeling for Biological Systems, 97–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34675-1_6.

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Chraim, Fabien, and Kristofer Pister. "Smart Fence: Decentralized Sequential Hypothesis Testing for Perimeter Security." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 65–78. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-04166-7_5.

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PS, Chandrashekhara Thejaswi, and Ranjeet Kumar Patro. "Distributed Multiple Hypothesis Testing in Sensor Networks Under Bandwidth Constraint." In Distributed Computing and Internet Technology, 184–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11604655_22.

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Altes, Richard A. "The Line Segment Transform and Sequential Hypothesis Testing in Dolphin Echolocation." In Marine Mammal Sensory Systems, 317–55. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3406-8_23.

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Papastavrou, Jason, and Michael Athans. "A Distributed Hypothesis-Testing Team Decision Problem with Communications Cost." In System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, 99–130. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3929-5_3.

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Conference papers on the topic "Distributed Sequential Hypothesis Testing"

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Jithin, K. S., and Vinod Sharma. "Novel algorithms for distributed sequential hypothesis testing." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120349.

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Zhang, Shan, Prashant Khanduri, and Pramod K. Varshney. "Distributed Sequential Hypothesis Testing with Dependent Sensor Observations." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048804.

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Salehkalaibar, Sadaf, and Vincent Y. F. Tan. "Distributed Sequential Hypothesis Testing With Zero-Rate Compression." In 2021 IEEE Information Theory Workshop (ITW). IEEE, 2021. http://dx.doi.org/10.1109/itw48936.2021.9611441.

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Sahu, Anit Kumar, and Soummya Kar. "Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094543.

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Ji-Woong Lee and G. E. Dullerud. "A dynamic decentralized sequential multi-hypothesis testing problem under uniformly distributed nonstationary observations." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1430338.

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Tartakovsky, A., and X. Rong Li. "Sequential testing of multiple hypotheses in distributed systems." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.859897.

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Leonard, Mark R., and Abdelhak M. Zoubir. "Robust distributed sequential hypothesis testing for detecting a random signal in non-Gaussian noise." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081190.

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Leonard, Mark R., Maximilian Stiefel, Michael Faub, and Abdelhak M. Zoubir. "Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461895.

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Pattanayak, Kunal, Vikram Krishnamurthy, and Erik Blasch. "Inverse Sequential Hypothesis Testing." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190339.

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Peng, Guanze, and Quanyan Zhu. "Sequential Hypothesis Testing Game." In 2020 54th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2020. http://dx.doi.org/10.1109/ciss48834.2020.1570617162.

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Reports on the topic "Distributed Sequential Hypothesis Testing"

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LaVigna, Anthony, Armand M. Makowski, and John S. Baras. A Continuous-Time Distributed Version of Wald's Sequential Hypothesis Testing Problem. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada453211.

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Al-Ibrahim, Mohammad M., and Pramod K. Varshney. On Disturbed Sequential Hypothesis Testing. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada238691.

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Chair, Zelneddine, and Pramod K. Varshney. On Hypothesis Testing in Distributed Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada195910.

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Sigeti, David E., and Robert A. Pelak. Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1050516.

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Nuzman, Dwayne W. An Accumulate-Toward-the-Mode Approach to Confidence Intervals and Hypothesis Testing With Applications to Binomially Distributed Data. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada514638.

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