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Journal articles on the topic 'Block Statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Zhang, Lei, Guo Qiang Lv, Fen Liu, and Xi Tiao Zhang. "Multi-Block Gray Statistics Local Dimming Algorithm." Applied Mechanics and Materials 321-324 (June 2013): 503–6. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.503.

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Multi-block gray statistics local dimming algorithm is proposed to automatically control the brightness of the backlight for reducing energy consumption and enhancing the image quality. The large blocks are used to determine the value of backlight matrix and small blocks are to determine the image block brightness. With the use of BMA (Blur-mask approach) to blur the backlight brightness matrix and compensate LCD control signal, local dimming function is realized.The simulation results show that: for pictures, the average backlight power consumption is decreased markedly by 19.9% and the contrast ratio is increased by 53%.

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2

Kunert, Joachim. "Optimality of block designs with variable block sizes and random block effects." Metrika 41, no. 1 (December 1994): 71–81. http://dx.doi.org/10.1007/bf01895306.

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3

Amarioarei, Alexandru, and Cristian Preda. "One Dimensional Discrete Scan Statistics for Dependent Models and Some Related Problems." Mathematics 8, no. 4 (April 13, 2020): 576. http://dx.doi.org/10.3390/math8040576.

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The one dimensional discrete scan statistic is considered over sequences of random variables generated by block factor dependence models. Viewed as a maximum of an 1-dependent stationary sequence, the scan statistics distribution is approximated with accuracy and sharp bounds are provided. The longest increasing run statistics is related to the scan statistics and its distribution is studied. The moving average process is a particular case of block factor and the distribution of the associated scan statistics is approximated. Numerical results are presented.

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4

Iglesias, Emma M. "The Block-Block Bootstrap for Time Series." Communications in Statistics - Theory and Methods 42, no. 14 (June 3, 2013): 2584–600. http://dx.doi.org/10.1080/03610926.2011.611605.

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5

Foldnes, Njål, and Steffen Grønneberg. "Approximating Test Statistics Using Eigenvalue Block Averaging." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 1 (October 20, 2017): 101–14. http://dx.doi.org/10.1080/10705511.2017.1373021.

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6

Lahiri, S. N. "ON THE JACKKNIFE-AFTER-BOOTSTRAP METHOD FOR DEPENDENT DATA AND ITS CONSISTENCY PROPERTIES." Econometric Theory 18, no. 1 (February 2002): 79–98. http://dx.doi.org/10.1017/s0266466602181059.

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Motivated by Efron (1992, Journal of the Royal Statistical Society, Series B 54, 83–111), this paper proposes a version of the moving block jackknife as a method of estimating standard errors of block-bootstrap estimators under dependence. As in the case of independent and identically distributed (i.i.d.) observations, the proposed method merely regroups the values of a statistic from different bootstrap replicates to produce an estimate of its standard error. Consistency of the resulting jackknife standard error estimator is proved for block-bootstrap estimators of the bias and the variance of a large class of statistics. Consistency of Efron's method is also established in similar problems for i.i.d. data.

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7

Gupta, V. K., A. Das, and A. Dey. "Universal optimality of block designs with unequal block sizes." Statistics & Probability Letters 11, no. 2 (February 1991): 177–80. http://dx.doi.org/10.1016/0167-7152(91)90139-i.

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8

Sinha, K., and B. Jones. "Further equireplicate balanced block designs with unequal block sizes." Statistics & Probability Letters 6, no. 4 (March 1988): 229–30. http://dx.doi.org/10.1016/0167-7152(88)90065-x.

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9

Xie, Junshan, and Gaoming Sun. "A test for block circular symmetric covariance structure with divergent dimension." ESAIM: Probability and Statistics 23 (2019): 672–96. http://dx.doi.org/10.1051/ps/2019020.

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The paper considers the likelihood ratio (LR) test on the block circular symmetric covariance structure of a multivariate Gaussian population with divergent dimension. When the sample size n, the dimension of each block p and the number of blocks u satisfy pu < n − 1 and p = p(n) → ∞ as n → ∞, the asymptotic distribution and the moderate deviation principle of the logarithmic LR test statistic under the null hypothesis are established. Some numerical simulations indicate that the proposed LR test method performs well in the divergent-dimensional block circular symmetric covariance structure test.

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10

Lu, Ta Te. "Significant Features Selection Resistant to Temporal Distortions." Applied Mechanics and Materials 284-287 (January 2013): 3154–58. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3154.

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Most patterns in continuous video sequences are similar. Temporal distortion, e.g. frames dropping, insertions, transposition, is a challenging issue for video reconstruction to find the actual missing positions in video sequences. The aim of this paper is to raise the detection accuracy and synchronize video frames back to original positions following temporal synchronization distortions. The successive video frames have similar statistics but the statistics in some local regions may differ from one another. Therefore, the block partition is partitioned into non-overlapping blocks by each frame, and then the local variance is calculated and taken as the block feature in each block. For most of the video frames, the pixels within the frame blocks are correlated and the maximum eigenvalue will be far from other eigenvalues. In this case, the maximum eigenvalue is set as the dominated block feature. For less correlated blocks, the values of the eigenvalues will be a little closer. In this case, the mean value of the eigenvalues represents the dominated block feature. Then, the sum of variance is regarded as the frame feature to calculate from these selective dominated blocks. Simulation results show the proposed methods are robust in evaluating the missing positions against temporal distortions.

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11

Aggarwal, M. L., Poonam Singh, and Nidhi Gupta. "Orthogonal block designs in two blocks for second degree K-model." Statistics & Probability Letters 66, no. 4 (March 2004): 423–34. http://dx.doi.org/10.1016/j.spl.2003.11.009.

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12

Gomes, D. Prata, and M. Manuela Neves. "Extremal index blocks estimator: the threshold and the block size choice." Journal of Applied Statistics 47, no. 13-15 (January 31, 2020): 2846–61. http://dx.doi.org/10.1080/02664763.2020.1720626.

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13

Li, Jixian, Wei Lu, Jian Weng, Yijun Mao, and Guoqiang Li. "Double JPEG compression detection based on block statistics." Multimedia Tools and Applications 77, no. 24 (June 13, 2018): 31895–910. http://dx.doi.org/10.1007/s11042-018-6175-2.

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14

Straubhaar, Julien, Philippe Renard, and Grégoire Mariethoz. "Conditioning multiple-point statistics simulations to block data." Spatial Statistics 16 (May 2016): 53–71. http://dx.doi.org/10.1016/j.spasta.2016.02.005.

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15

Shiraishi, Taka-Aki. "Studentized robust statistics in multivariate randomized block design." Journal of Nonparametric Statistics 10, no. 1 (January 1998): 95–110. http://dx.doi.org/10.1080/10485259808832755.

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16

Liò, Pietro, Antonio Politi, Marcello Buiatti, and Stefano Ruffo. "High Statistics Block Entropy Measures of DNA Sequences." Journal of Theoretical Biology 180, no. 2 (May 1996): 151–60. http://dx.doi.org/10.1006/jtbi.1996.0091.

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17

Chen, Joanna N. "Block decomposition and statistics arising from permutation tableaux." European Journal of Combinatorics 99 (January 2022): 103419. http://dx.doi.org/10.1016/j.ejc.2021.103419.

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18

Edmondson, Rodney N. "Multi-level Block Designs for Comparative Experiments." Journal of Agricultural, Biological and Environmental Statistics 25, no. 4 (October 8, 2020): 500–522. http://dx.doi.org/10.1007/s13253-020-00416-0.

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Abstract Complete replicate block designs are fully efficient for treatment effects and are the designs of choice for many agricultural field experiments. For experiments with a large number of treatments, however, they may not provide good control of variability over the whole experimental area. Nested incomplete block designs with a single level of nesting can then improve ‘within-block’ homogeneity for moderate sized experiments. For very large designs, however, a single level of nesting may not be adequate and this paper discusses multi-level nesting with hierarchies of nested blocks. Multi-level nested block designs provide a range of block sizes which can improve ‘within-block’ homogeneity over a range of scales of measurement. We discuss design and analysis of multi-level block designs for hierarchies of nested blocks including designs with crossed block factors. We describe an R language package for multi-level block design and we exemplify the design and analysis of multi-level block designs by a simulation study of block designs for cereal variety trials in the UK. Finally, we re-analyse a single large row-and-column field trial for 272 spring barley varieties in 16 rows and 34 columns assuming an additional set of multi-level nested column blocks superimposed on the existing design. For each example, a multi-level mixed blocks analysis is compared with a spatial analysis based on hierarchical generalized additive (HGAM) models. We discuss the combined analysis of random blocks and HGAM smoothers in the same model.

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19

Julian R. Abel, R., G. Ge, Malcolm Greig, and L. Zhu. "Resolvable balanced incomplete block designs with block size 5." Journal of Statistical Planning and Inference 95, no. 1-2 (May 2001): 49–65. http://dx.doi.org/10.1016/s0378-3758(00)00277-9.

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20

Nordman, Daniel J., and Soumendra N. Lahiri. "Convergence rates of empirical block length selectors for block bootstrap." Bernoulli 20, no. 2 (May 2014): 958–78. http://dx.doi.org/10.3150/13-bej511.

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21

Engel, Konrad, and Sylke Gierer. "Optimal designs for models with block-block resp. treatment-treatment correlations." Metrika 40, no. 1 (December 1993): 349–59. http://dx.doi.org/10.1007/bf02613699.

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22

Simic, Dusica, Irena Simic, Marija Stevic, Nevena Jovicic, Maja Mitrovic, Ivana Budic, Miodrag Milenovic, Vesna Marjanovic, and Biljana Milicic. "The role of regional anesthesia in the postoperative analgesia in pediatric patients." Srpski arhiv za celokupno lekarstvo 147, no. 5-6 (2019): 316–20. http://dx.doi.org/10.2298/sarh180116067s.

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Introduction/Objective. Pain is a disturbing experience associated with existing or potential tissue damage, with a sensory, emotional, cognitive, and social component. The aim of this study was to show the efficiency of regional anesthetic techniques in postoperative pain in children. Methods. The retrospective cohort study was conducted on a group of 564 pediatric patients during the period from 2013 to 2016. Types of regional anesthesia were classified into the following six groups: caudal, epidural, spinal block, upper limb blocks, lower limb blocks, and truncal nerve block. From statistical methods, we used descriptive statistical methods of absolute and relative numbers, measurements of variability, central tendencies for numerical features, and methods of inferential statistics. We used the ?2 test for the attributive features of observations. Results. In relation to the postoperative time when an analgesic was required, a statistically significant difference was observed in the age of children (p = 0.000), disease diagnosis (p = 0.000), type of block (p = 0.000), type of local anesthetic (p = 0.000), and type of anesthesia or sedation preoperatively (p = 0.005). Conclusion. Postoperative analgesia was most needed by older children and children who were awake during surgery. Children with injuries and tumors need postoperative analgesia the earliest. The longest postoperative analgesia was recorded in patients who received caudal block. The longest postoperative analgesia can be seen in patients who received levobupivacaine, bupivacaine or levobupivacaine combined with lidocaine to perform the block.

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23

Bhaumik, Dulal K., and Dexter C. Whittinghill. "Optimality and Robustness to the Unavailability of Blocks in Block Designs." Journal of the Royal Statistical Society: Series B (Methodological) 53, no. 2 (January 1991): 399–407. http://dx.doi.org/10.1111/j.2517-6161.1991.tb01832.x.

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24

Morgan, J. P. "Optimal Incomplete Block Designs." Journal of the American Statistical Association 102, no. 478 (June 2007): 655–63. http://dx.doi.org/10.1198/016214506000001329.

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25

Majumdar, Dibyen, and Aloke Dey. "Theory of Block Designs." Journal of the American Statistical Association 83, no. 401 (March 1988): 275. http://dx.doi.org/10.2307/2288964.

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26

Preece, D. A., and A. Dey. "Theory of Block Designs." Applied Statistics 38, no. 3 (1989): 523. http://dx.doi.org/10.2307/2347742.

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27

Yandell, Brian S. "Block diagonal smoothing splines." Statistics & Probability Letters 6, no. 5 (April 1988): 331–34. http://dx.doi.org/10.1016/0167-7152(88)90009-0.

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28

Jones, B. "Searching for Optimal Block Designs when Block Effects are Random." Biometrical Journal 28, no. 8 (1986): 909–18. http://dx.doi.org/10.1002/bimj.4710280803.

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29

Khattree, Ravindra. "On construction of constant block-sum partially balanced incomplete block designs." Communications in Statistics - Theory and Methods 49, no. 11 (March 12, 2019): 2585–606. http://dx.doi.org/10.1080/03610926.2019.1576895.

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30

Lahiri, S. N. "Effects of block lengths on the validity of block resampling methods." Probability Theory and Related Fields 121, no. 1 (September 2001): 73–97. http://dx.doi.org/10.1007/pl00008798.

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31

Raghavarao, D., W. T. Federer, and S. J. Schwager. "Characteristics for distinguishing among balanced incomplete block designs with repeated blocks." Journal of Statistical Planning and Inference 13 (January 1986): 151–63. http://dx.doi.org/10.1016/0378-3758(86)90129-1.

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32

Jakubowski, Adam, and Joanna Karłowska-Pik. "Processes with Block-Associated Increments." Journal of Applied Probability 48, no. 02 (June 2011): 514–26. http://dx.doi.org/10.1017/s0021900200008019.

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This paper is motivated by relations between association and independence of random variables. It is well known that, for real random variables, independence implies association in the sense of Esary, Proschan and Walkup (1967), while, for random vectors, this simple relationship breaks. We modify the notion of association in such a way that any vector-valued process with independent increments also has associated increments in the new sense - association between blocks. The new notion is quite natural and admits nice characterization for some classes of processes. In particular, using the covariance interpolation formula due to Houdré, Pérez-Abreu and Surgailis (1998), we show that within the class of multidimensional Gaussian processes, block association of increments is equivalent to supermodularity (in time) of the covariance functions. We also define corresponding versions of weak association, positive association, and negative association. It turns out that the central limit theorem for weakly associated random vectors due to Burton, Dabrowski and Dehling (1986) remains valid, if the weak association is relaxed to the weak association between blocks.

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33

Jakubowski, Adam, and Joanna Karłowska-Pik. "Processes with Block-Associated Increments." Journal of Applied Probability 48, no. 2 (June 2011): 514–26. http://dx.doi.org/10.1239/jap/1308662641.

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This paper is motivated by relations between association and independence of random variables. It is well known that, for real random variables, independence implies association in the sense of Esary, Proschan and Walkup (1967), while, for random vectors, this simple relationship breaks. We modify the notion of association in such a way that any vector-valued process with independent increments also has associated increments in the new sense - association between blocks. The new notion is quite natural and admits nice characterization for some classes of processes. In particular, using the covariance interpolation formula due to Houdré, Pérez-Abreu and Surgailis (1998), we show that within the class of multidimensional Gaussian processes, block association of increments is equivalent to supermodularity (in time) of the covariance functions. We also define corresponding versions of weak association, positive association, and negative association. It turns out that the central limit theorem for weakly associated random vectors due to Burton, Dabrowski and Dehling (1986) remains valid, if the weak association is relaxed to the weak association between blocks.

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34

Johnson, Oliver. "A Central Limit Theorem for Non-Overlapping Return Times." Journal of Applied Probability 43, no. 1 (March 2006): 32–47. http://dx.doi.org/10.1239/jap/1143936241.

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Define the non-overlapping return time of a block of a random process to be the number of blocks that pass by before the block in question reappears. We prove a central limit theorem based on these return times. This result has applications to entropy estimation, and to the problem of determining if digits have come from an independent, equidistributed sequence. In the case of an equidistributed sequence, we use an argument based on negative association to prove convergence under conditions weaker than those required in the general case.

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35

Brzeskwiniewicz, Henryk. "On the E-Optimality of Block Designs with Unequal Block Sizes." Biometrical Journal 31, no. 5 (1989): 631–35. http://dx.doi.org/10.1002/bimj.4710310520.

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36

Alfano, Giuseppa, Carla-Fabiana Chiasserini, Alessandro Nordio, and Siyuan Zhou. "Closed-Form Output Statistics of MIMO Block-Fading Channels." IEEE Transactions on Information Theory 60, no. 12 (December 2014): 7782–97. http://dx.doi.org/10.1109/tit.2014.2363838.

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37

Provata, A. "Non-extensive block entropy statistics of Cantor fractal sets." Physica A: Statistical Mechanics and its Applications 381 (July 2007): 148–54. http://dx.doi.org/10.1016/j.physa.2007.03.055.

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38

Xiong, Li, and Zuoxiang Peng. "Heavy tail index estimation based on block order statistics." Journal of Statistical Computation and Simulation 90, no. 12 (May 19, 2020): 2198–208. http://dx.doi.org/10.1080/00949655.2020.1769622.

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39

Zorzi, M., and R. R. Rao. "On the statistics of block errors in bursty channels." IEEE Transactions on Communications 45, no. 6 (June 1997): 660–67. http://dx.doi.org/10.1109/26.592604.

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40

ANDJELKOVICH, DRAGANA A., REGINA B. RICHARDSON, PHILIP E. ENTERLINE, and RICHARD J. LEVINE. "ASSIGNING RACE TO OCCUPATIONAL COHORTS USING CENSUS BLOCK STATISTICS." American Journal of Epidemiology 131, no. 5 (May 1990): 928–34. http://dx.doi.org/10.1093/oxfordjournals.aje.a115582.

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41

Yue, Chentao, Mahyar Shirvanimoghaddam, Giyoon Park, Ok-Sun Park, Branka Vucetic, and Yonghui Li. "Probability-Based Ordered-Statistics Decoding for Short Block Codes." IEEE Communications Letters 25, no. 6 (June 2021): 1791–95. http://dx.doi.org/10.1109/lcomm.2021.3058978.

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42

Bolla, Marianna, and Ahmed Elbanna. "Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm." Journal of Probability and Statistics 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/657965.

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We introduce a semiparametric block model for graphs, where the within- and between-cluster edge probabilities are not constants within the blocks but are described by logistic type models, reminiscent of the 50-year-old Rasch model and the newly introducedα-βmodels. Our purpose is to give a partition of the vertices of an observed graph so that the induced subgraphs and bipartite graphs obey these models, where their strongly interlaced parameters give multiscale evaluation of the vertices at the same time. In this way, a profoundly heterogeneous version of the stochastic block model is built via mixtures of the above submodels, while the parameters are estimated with a special EM iteration.

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43

Tjur, Tue. "Block Designs and Electrical Networks." Annals of Statistics 19, no. 2 (June 1991): 1010–27. http://dx.doi.org/10.1214/aos/1176348134.

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44

Sanders, Jaron, Alexandre Proutière, and Se-Young Yun. "Clustering in Block Markov Chains." Annals of Statistics 48, no. 6 (December 2020): 3488–512. http://dx.doi.org/10.1214/19-aos1939.

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45

Kala, Radosław. "On nested block designs geometry." Statistical Papers 50, no. 4 (August 2009): 805–15. http://dx.doi.org/10.1007/s00362-009-0260-6.

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46

Dey, Aloke, and Ashish Das. "On someE-optimal block designs." Metrika 36, no. 1 (December 1989): 269–78. http://dx.doi.org/10.1007/bf02614100.

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47

Block, Henry W., Naftali A. Langberg, and Thomas H. Savits. "Maintenance comparisons: block policies." Journal of Applied Probability 27, no. 3 (September 1990): 649–57. http://dx.doi.org/10.2307/3214548.

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Complete repair and minimal repair models with a block maintenance policy are considered. Each of these models gives rise to a counting process, and these processes are compared stochastically. This contrasts with most previous work on maintenance policies where only univariate marginal comparisons were made. Also a more general block schedule is considered than is customary.

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48

Block, Henry W., Naftali A. Langberg, and Thomas H. Savits. "Maintenance comparisons: block policies." Journal of Applied Probability 27, no. 03 (September 1990): 649–57. http://dx.doi.org/10.1017/s0021900200039188.

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Complete repair and minimal repair models with a block maintenance policy are considered. Each of these models gives rise to a counting process, and these processes are compared stochastically. This contrasts with most previous work on maintenance policies where only univariate marginal comparisons were made. Also a more general block schedule is considered than is customary.

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49

Singh, Rajeshwar, and V. K. Gupta. "Resistance of block designs." Journal of Statistical Planning and Inference 27, no. 2 (February 1991): 263–69. http://dx.doi.org/10.1016/0378-3758(91)90021-6.

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50

Hering, Franz, and Stanislaw Mejza. "Incomplete Split-Block Designs." Biometrical Journal 39, no. 2 (1997): 227–38. http://dx.doi.org/10.1002/bimj.4710390210.

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