Relevant bibliographies by topics / Closeness testing

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Academic literature on the topic 'Closeness testing'

Author: Grafiati
Published: 26 October 2024

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Journal articles on the topic "Closeness testing"

1

Batu, Tuğkan, Lance Fortnow, Ronitt Rubinfeld, Warren D. Smith, and Patrick White. "Testing Closeness of Discrete Distributions." Journal of the ACM 60, no. 1 (February 2013): 1–25. http://dx.doi.org/10.1145/2432622.2432626.

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Li, Qi. "Nonparametric testing of closeness between two unknown distribution functions." Econometric Reviews 15, no. 3 (January 1996): 261–74. http://dx.doi.org/10.1080/07474939608800355.

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Malinovsky, Yaakov, and Shelemyahu Zacks. "Proportional closeness estimation of probability of contamination under group testing." Sequential Analysis 37, no. 2 (April 3, 2018): 145–57. http://dx.doi.org/10.1080/07474946.2018.1466518.

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Rachman, Rosyidah, and Indri Ariyani. "PERSEPSI KUALITAS LAYANAN DAN KEDEKATAN EMOSIONAL TERHADAP KEPUASAN MASYARAKAT PADA PELAYANAN PUSKESMAS KECAMATAN MOYO HILIR." Samalewa: Jurnal Riset & Kajian Manajemen 4, no. 1 (July 16, 2024): 70–81. http://dx.doi.org/10.58406/samalewa.v4i1.1595.

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This study aims to examine perceptions of service quality and emotional closeness on community satisfaction at Moyo Hilir Health Centre services. The type of this study was associative study which aims to examine the effect of perceived service quality (X1) and emotional closeness (X2) on community satisfaction at Moyo Hilir Health Centre services (Y). products among students at Samawa University, Sumbawa Besar. The type of data used in this study was quantitative data obtained directly from research respondents using a questionnaire. The respondents in this study were 55 people who used the Moyo Hilir Health Center services in the period November 2022 who were determined using probability sampling techniques with the simple random sampling method. The data used in this study were processed with SPSS software for analysis using multiple linear regression analysis techniques, partial hypothesis testing (t test), simultaneous hypothesis testing (F test), and determinant coefficient testing (R2). The results of this study showed that service quality (X1) and emotional closeness (X2) had a positive and significant effect on community satisfaction at Moyo Hilir Health Centre services (Y), both partially and simultaneously. The effect of service quality and emotional closeness on community satisfaction at Moyo Hilir Health Center services was 75.6%, while the remaining of 24.4% was effected by other factors not included in this study, such as product quality, price or costs, and ease of obtaining products or services.
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Wang, Hua. "The Effects of School Climate, Parent–Child Closeness, and Peer Relations on the Problematic Internet Use of Chinese Adolescents: Testing the Mediating Role of Self-Esteem and Depression." International Journal of Environmental Research and Public Health 19, no. 13 (June 21, 2022): 7583. http://dx.doi.org/10.3390/ijerph19137583.

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Although previous research has investigated the associations among family factors, school factors, peer factors, and problematic Internet use, its causal direction has not been verified, particularly in the Chinese context. Using school-based data, this study aims to explore the possible causal direction among school climate, parent–child closeness, peer relations, and the problematic Internet use of Chinese adolescents. Nine hundred and sixty students in junior and senior high schools participated in a questionnaire survey. The results showed that parent–child closeness, school climate, and peer relations had a significantly direct effect on the problematic Internet use of Chinese adolescents. Meanwhile, the effects of parent–child closeness, school climate, and peer relations on problematic Internet use were mediated by self-esteem and depression. Implications are also discussed to prevent the problematic Internet use of adolescents.
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Carolus, Astrid, Jens F. Binder, Ricardo Muench, Catharina Schmidt, Florian Schneider, and Sarah L. Buglass. "Smartphones as digital companions: Characterizing the relationship between users and their phones." New Media & Society 21, no. 4 (December 12, 2018): 914–38. http://dx.doi.org/10.1177/1461444818817074.

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Based on the idea of computers constituting social agents and referring to core characteristics of human–human relationships, this study introduces the concept of a digital companionship between smartphone users and their devices. Constituting characteristics (closeness, trust, preoccupation) and outcomes (stress, coping with stress) of social relationships were adapted to yield a model of human–smartphone relationships for empirical testing. A cross-national sample of participants ( n = 1156) completed an online study, which included self-report measures as well as a newly developed instrument (Positioning Others and Devices [POD]) assessing the closeness to technical devices and social actors. Results showed the smartphone to be the closest device. Furthermore, structural equation modeling lent support for the theoretical model indicating that trust and preoccupation mediate the relationship between closeness to the smartphone and stress and coping. Findings support the concept of companionship as a fruitful approach to explain smartphone-related behaviors.
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Belovs, Aleksandrs, Arturo Castellanos, Francois Le Gall, Guillaume Malod, and Alexander A. Sherstov. "Quantum communication complexity of distribution testing." Quantum Information and Computation 21, no. 15&16 (November 2021): 1261–73. http://dx.doi.org/10.26421/qic21.15-16-1.

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The classical communication complexity of testing closeness of discrete distributions has recently been studied by Andoni, Malkin and Nosatzki (ICALP'19). In this problem, two players each receive $t$ samples from one distribution over $[n]$, and the goal is to decide whether their two distributions are equal, or are $\epsilon$-far apart in the $l_1$-distance. In the present paper we show that the quantum communication complexity of this problem is $\tilde{O}(n/(t\epsilon^2))$ qubits when the distributions have low $l_2$-norm, which gives a quadratic improvement over the classical communication complexity obtained by Andoni, Malkin and Nosatzki. We also obtain a matching lower bound by using the pattern matrix method. Let us stress that the samples received by each of the parties are classical, and it is only communication between them that is quantum. Our results thus give one setting where quantum protocols overcome classical protocols for a testing problem with purely classical samples.
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Teacher Jiham Salman Allawy and Teacher Rouaa Ali AbdAlsadah. "The Role of Internal Ranking in Job Satisfaction: An Exploratory Research in the General Company for Electrical and Electronic Industries in Baghdad." Economic and Administrative Studies Journal 2, no. 2 (May 27, 2023): 75–96. http://dx.doi.org/10.58564/easj/2.2.2023.5.

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this reached aim to measuring the role of layout to achieve a job satisfaction in the General Company for Electrical Industries the data of this study was collected through a questionnaire, distributed to (88) respondents from department and divisional officials and a number of employees with technical and engineering specializations. The descriptive analytical method was used to reach results, the SPSS v 23 programs was used to analyzing the data of the responses for this reached sample and testing the two main hypotheses using a number of statistical indicators. The most prominent of conclusions of study is the existence of a correlation and influence relationship between layout and job satisfaction, and that the closeness between departments is one of the most influential dimensions of internal layout in achieving job satisfaction, which confirms the need for great attention to this dimension. The research also reached the most important recommendations, including increasing the interest of the company's management in the dimensions of the internal arrangement represented in( flow clarity , closeness among depts , vocational safety , spaces use) Especially after closeness among depts because of their positive and significant role in achieving job satisfaction for workers and thus improving productivity.
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Pesaran, M. Hashem. "Global and Partial Non-Nested Hypotheses and Asymptotic Local Power." Econometric Theory 3, no. 1 (February 1987): 69–97. http://dx.doi.org/10.1017/s0266466600004138.

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This paper addresses two related issues in the literature of non-nested hypotheses testing. Firstly, by means of a measure of “closeness” of probability density functions, it shows how any two hypotheses can be placed into the nested and the non-nested categories with the latter category being subdivided further into “globally” and “partially” non-nested hypotheses. Secondly, by emphasizing the distinction between a “local null” and a “local alternative,” the paper shows that only in the case of partially non-nested hypotheses is it possible to specify local alternatives. In this case the paper derives the asymptotic distribution of the Cox test statistic under local alternatives and shows that it is distributed as a normal variate with a mean which is directly related to the measure of “closeness” of the alternative to the null hypothesis.
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Mazmanian, R. O. "NONCORRALATED DATA ORDERED SAMPLES AS A SINGLE-ELEMENT MULTICHANNEL CONVERTER." Tekhnichna Elektrodynamika 2021, no. 3 (April 19, 2021): 74–82. http://dx.doi.org/10.15407/techned2021.03.074.

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The sufficient closeness of the medians of the ordered samples of random data to the normal distribution is used in computer systems for control, monitoring and diagnosing electric power equipment. However, it remains what other probability density function (pdf) of elements (sample statistics) have such similarities. This paper presents various methods for statistical testing hypotheses for pdf-converter channels as statistics of given sizes odd numbered and ordered samples of bounded and uniformly distributed random numbers. The use of various different criteria and the results of estimates studied under the same conditions showed a sufficient conformity of the results of tests for three statistical criteria. It made possible to draw a reasonable conclusion about the preferable use of the adapted chi-square test for assessing the congruence of analytical pdf channels of the converter with normal distribution. We also suggested using the "statistical closeness window" to define those channels of the converter that do not significantly differ from the normal distribution. In addition, we presented an empirical formula determining the dependence of the size of the window of the statistical closeness window on the sample size. The results of the research are summarized in a statistical model of a multichannel uncorrelated data converter. References 27, figures 7.
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Dissertations / Theses on the topic "Closeness testing"

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Pornsakulvanich, Vikanda. "TESTING A USES AND GRATIFICATIONS MODEL OF ONLINE RELATIONSHIPS." Kent State University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=kent1120752984.

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Fermanian, Jean-Baptiste. "High dimensional multiple means estimation and testing with applications to machine learning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM035.

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Nous étudions dans cette thèse l'influence de la grande dimension dans des problèmes de test et d'estimation. Notre analyse porte sur la dépendance en la dimension de la vitesse de séparation d'un test de proximité et du risque quadratique de l'estimation multiples de vecteurs. Nous complétons les résultats existants en étudiant ces dépendances dans le cas de distributions non isotropes. Pour de telles distributions, le rôle de la dimension est alors joué par des notions de dimension effective définies à partir de la covariance des distributions. Ce cadre permet d'englober des données de dimension infinie comme le kernel mean embedding, outil de machine learning que nous chercherons à estimer. A l'aide de cette analyse, nous construisons des méthodes d'estimation simultanée de vecteurs moyennes de différentes distributions à partir d'échantillons indépendants de chacune. Ces estimateurs ont de meilleures performances théorique et pratique relativement aux moyennes empiriques, en particulier dans des situations défavorables où la dimension (effective) est grande. Ces méthodes utilisent explicitement ou implicitement la relative facilité du test par rapport à l'estimation. Elles reposent sur la construction d'estimateurs de distances et de moments de la covariance pour lesquels nous fournissons des bornes de concentration non asymptotiques. Un intérêt particulier est porté à l'étude de données bornées pour lesquels une analyse spécifique est nécessaire. Nos méthodes sont accompagnées d'une analyse minimax justifiant leur optimalité. Dans une dernière partie, nous proposons une interprétation du mécanisme d'attention utilisé dans les réseaux de neurones Transformers comme un problème d'estimation multiple de vecteurs. Dans un cadre simplifié, ce mécanisme partage des idées similaires avec nos approches et nous mettons en évidence son effet de débruitage en grande dimension
In this thesis, we study the influence of high dimension in testing and estimation problems. We analyze the dimension dependence of the separation rate of a closeness test and of the quadratic risk of multiple vector estimation. We complement existing results by studying these dependencies in the case of non-isotropic distributions. For such distributions, the role of dimension is played by notions of effective dimension defined from the covariance of the distributions. This framework covers infinite-dimensional data such as kernel mean embedding, a machine learning tool we will be seeking to estimate. Using this analysis, we construct methods for simultaneously estimating mean vectors of different distributions from independent samples of each. These estimators perform better theoretically and practically than the empirical mean in unfavorable situations where the (effective) dimension is large. These methods make explicit or implicit use of the relative ease of testing compared with estimation. They are based on the construction of estimators of distances and moments of covariance, for which we provide non-asymptotic concentration bounds. Particular interest is given to the study of bounded data, for which a specific analysis is required. Our methods are accompanied by a minimax analysis justifying their optimality. In a final section, we propose an interpretation of the attention mechanism used in Transformer neural networks as a multiple vector estimation problem. In a simplified framework, this mechanism shares similar ideas with our approaches, and we highlight its denoising effect in high dimension
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Conference papers on the topic "Closeness testing"

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Chan, Siu-On, Ilias Diakonikolas, Paul Valiant, and Gregory Valiant. "Optimal Algorithms for Testing Closeness of Discrete Distributions." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.88.

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Diakonikolas, Ilias, Daniel M. Kane, and Sihan Liu. "Testing Closeness of Multivariate Distributions via Ramsey Theory." In STOC '24: 56th Annual ACM Symposium on Theory of Computing. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3618260.3649657.

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Acharya, Jayadev, Ashkan Jafarpour, Alon Orlitsky, and Ananda Theertha Suresh. "Sublinear algorithms for outlier detection and generalized closeness testing." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6875425.

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Diakonikolas, Ilias, Daniel M. Kane, and Vladimir Nikishkin. "Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions." In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2015. http://dx.doi.org/10.1109/focs.2015.76.

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Johnson, William R., Michael J. Leamy, Washington DeLima, and Massimo Ruzzene. "Pulse Shaping in 1D Elastic Waveguides for Shock Testing." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23280.

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Abstract Mechanical shock events experienced by electronics systems can be reproduced in the laboratory using Hopkinson bar tests. In these tests a projectile strikes a bar, creating a pulse which travels through the bar into the system. The quality of these tests depends on the closeness of the shape of the incident pulse to the shape specified for the test. This paper introduces a new way to control the shape of the incoming pulse, through the use of elastic metamaterial concepts. Two dispersion-modifying material concepts, phononic crystals, and local resonators, are examined for their wave shaping capabilities in 1D elastic waveguides. They are then evaluated using a transfer matrix method to determine the output wave shape in the time domain. The concepts are then optimized for various pulse shapes, showing that they are most effective when they are tuned to introduce dispersion near the fundamental frequency of the incident wave.
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Abbasi, Jassem, and Pål Østebø Andersen. "Improved Initialization of Non-Linear Solvers in Numerical Simulation of Flow in Porous Media with a Deep Learning Approach." In SPE EuropEC - Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209667-ms.

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Abstract Partial Differential Equations (PDEs) have a wide list of applications in modeling complex processes including flow in porous materials. Solution of these equations that are mostly highly non-linear is generally possible using numerical algorithms that are carried out by iterative approaches like Newton's method, where the calculations to find the solution at a new time step are started using an initial guess of the unknown variables. The computational efficiency of the calculations is highly dependent on the closeness of these initial guesses to the exact values. As a routine, solvers pick the solutions at the previous timestep as the kickoff point for Newton's method. Improvement of this starting point at each time step can reduce the time-to-solution of the solver. This study focuses on using a Deep Learning (DL) algorithm for optimization of a PDE solver and improvement of the computational efficiency of simulation of flow in porous media by providing more efficient initial guesses for the unknown variables. In this work, a 1D gravity-capillary driven two-phase flow problem with a fully implicit Newton's solver was hired as the base numerical model. A fully connected neural network (NN) was initialized and added to the numerical solver, at the point before starting each timestep. The data from the most important features and the target properties were collected from a series of simulation cases and the DL model was trained with the Adam optimizer. After training and testing, the default initialization approach (i.e., solution at previous timestep) was replaced by the hybrid DL-based approach that provides an initial guess for cells with high saturation gradients. Saturation gradients at the previous timestep, location, and mobility ratio of both phases are selected as the input features. The results showed that applying the developed algorithm to numerical simulation problems reduced the run-time in the range of 15-35% in different cases, while the required processing time of the DL model was only around 1-3% of the whole simulation. The model performed acceptably when the effective parameters are like porosity, permeability and capillary pressure deviated from the range of the training data in order of 100%. The model performance declined when this deviation increased. This hybrid initialization approach showed the possibility of applying DL methodologies for the improvement of the numerical simulation processes.
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Abbasi, Jassem, and Pål Østebø Andersen. "Improved Initialization of Non-Linear Solvers in Numerical Simulation of Flow in Porous Media with a Deep Learning Approach." In SPE EuropEC - Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209667-ms.

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Abstract Partial Differential Equations (PDEs) have a wide list of applications in modeling complex processes including flow in porous materials. Solution of these equations that are mostly highly non-linear is generally possible using numerical algorithms that are carried out by iterative approaches like Newton's method, where the calculations to find the solution at a new time step are started using an initial guess of the unknown variables. The computational efficiency of the calculations is highly dependent on the closeness of these initial guesses to the exact values. As a routine, solvers pick the solutions at the previous timestep as the kickoff point for Newton's method. Improvement of this starting point at each time step can reduce the time-to-solution of the solver. This study focuses on using a Deep Learning (DL) algorithm for optimization of a PDE solver and improvement of the computational efficiency of simulation of flow in porous media by providing more efficient initial guesses for the unknown variables. In this work, a 1D gravity-capillary driven two-phase flow problem with a fully implicit Newton's solver was hired as the base numerical model. A fully connected neural network (NN) was initialized and added to the numerical solver, at the point before starting each timestep. The data from the most important features and the target properties were collected from a series of simulation cases and the DL model was trained with the Adam optimizer. After training and testing, the default initialization approach (i.e., solution at previous timestep) was replaced by the hybrid DL-based approach that provides an initial guess for cells with high saturation gradients. Saturation gradients at the previous timestep, location, and mobility ratio of both phases are selected as the input features. The results showed that applying the developed algorithm to numerical simulation problems reduced the run-time in the range of 15-35% in different cases, while the required processing time of the DL model was only around 1-3% of the whole simulation. The model performed acceptably when the effective parameters are like porosity, permeability and capillary pressure deviated from the range of the training data in order of 100%. The model performance declined when this deviation increased. This hybrid initialization approach showed the possibility of applying DL methodologies for the improvement of the numerical simulation processes.
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Reports on the topic "Closeness testing"

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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