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Relevant bibliographies by topics / Fisher scoring

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Academic literature on the topic 'Fisher scoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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Journal articles on the topic "Fisher scoring"

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Purba, Switamy Angnitha. "The Application of Fisher Scoring Algorithm on Parameter Estimation of Normal Distributed Data." Journal of Research in Mathematics Trends and Technology 3, no. 1 (2021): 20–24. http://dx.doi.org/10.32734/jormtt.v3i1.8345.

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In statistics, parameter estimation is the estimation of a population using sample data. A population data certainly has a certain distribution. Fisher Scoring is a form of Newton's method which is commonly used in solving the maximum likelihood equation. The focus of this research is to estimate distributed data using the fisher scoring algorithm

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Takai, Keiji. "Incomplete-data Fisher scoring method with steplength adjustment." Statistics and Computing 30, no. 4 (2020): 871–86. http://dx.doi.org/10.1007/s11222-020-09923-z.

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ZAKI, NAZAR M., SAFAAI DERIS, and ROSLI M. ILLIAS. "FEATURES EXTRACTION FOR PROTEIN HOMOLOGY DETECTION USING HIDDEN MARKOV MODELS COMBINING SCORES." International Journal of Computational Intelligence and Applications 04, no. 01 (2004): 1–12. http://dx.doi.org/10.1142/s1469026804001161.

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Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discriminative approach, and in particular support vector machines for protein homologies detection. However, in place of the Fisher scoring method, we present a new Hidden Markov Model Combining Scores approach. Six scoring algorithms are combined as a way of extracting features from a protein sequence. Experiments show that our method, improves on previous methods for homologies detection of protein domains.

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Ouakasse, Abdelhamid, and Guy Mélard. "On-line estimation of ARMA models using Fisher-scoring." Systems Science & Control Engineering 2, no. 1 (2014): 406–32. http://dx.doi.org/10.1080/21642583.2014.912572.

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Toscas, Peter J., and Malcolm J. Faddy. "Using Fisher Scoring to Fit Extended Poisson Process Models." Computational Statistics 19, no. 3 (2004): 425–43. http://dx.doi.org/10.1007/bf03372105.

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Hudson, Malcolm, and Jun Ma. "On asymptotic convergence of the block-iterative Fisher scoring algorithm." Statistics & Probability Letters 80, no. 11-12 (2010): 922–25. http://dx.doi.org/10.1016/j.spl.2010.01.028.

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Kwagyan, John, Victor Apprey, George E. Bonney, and Zudi Lu. "Gram–Schmidt–Fisher scoring algorithm for parameter orthogonalization in MLE." Cogent Mathematics 3, no. 1 (2016): 1159847. http://dx.doi.org/10.1080/23311835.2016.1159847.

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Messelodi, Stefano, and Carla Maria Modena. "Boosting Fisher vector based scoring functions for person re-identification." Image and Vision Computing 44 (December 2015): 44–58. http://dx.doi.org/10.1016/j.imavis.2015.09.008.

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Wang, Yong. "Fisher scoring: An interpolation family and its Monte Carlo implementations." Computational Statistics & Data Analysis 54, no. 7 (2010): 1744–55. http://dx.doi.org/10.1016/j.csda.2010.02.006.

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Hong, Ju young, Je Sung You, Min Joung Kim, et al. "Development and external validation of new nomograms by adding ECG changes (ST depression or tall T wave) and age to conventional scoring systems to improve the predictive capacity in patients with subarachnoid haemorrhage: a retrospective, observational study in Korea." BMJ Open 9, no. 2 (2019): e024007. http://dx.doi.org/10.1136/bmjopen-2018-024007.

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ObjectivesTo develop new nomograms by adding ECG changes (ST depression or tall T wave) and age to three conventional scoring systems, namely, World Federation of Neurosurgical Societies (WFNS) scale, Hunt and Hess (HH) system and Fisher scale, that can predict prognosis in patients with subarachnoid haemorrhage (SAH) using our preliminary research results and to perform external validation of the three new nomograms.DesignRetrospective, observational studySettingEmergency departments (ED) of two university-affiliated tertiary hospital between January 2009 and March 2015.ParticipantsAdult patients with SAH were enrolled. Exclusion criteria were age <19 years, no baseline ECG, cardiac arrest on arrival, traumatic SAH, referral from other hospital and referral to other hospitals from the ED.Primary outcome measuresThe 6 month prognosis was assessed using the Glasgow Outcome Scale (GOS). We defined a poor outcome as a GOS score of 1, 2 or 3.ResultsA total of 202 patients were included for analysis. From the preliminary study, age, ECG changes (ST depression or tall T wave), and three conventional scoring systems were selected to predict prognosis in patients with SAH using multi-variable logistic regression. We developed simplified nomograms using these variables. Discrimination of the developed nomograms including WFNS scale, HH system and Fisher scale was superior to those of WFNS scale, HH system and Fisher scale (0.912 vs 0.813; p<0.001, 0.913 vs 0.826; p<0.001, and 0.885 vs 0.746; p<0.001, respectively). The calibration plots showed excellent agreement. In the external validation, the discrimination of the newly developed nomograms incorporating the three scoring systems was also good, with an area under the receiver-operating characteristic curve value of 0.809, 0.812 and 0.772, respectively.ConclusionsWe developed and externally validated new nomograms using only three independent variables. Our new nomograms were superior to the WFNS scale, HH systems, and Fisher scale in predicting prognosis and are readily available.

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Dissertations / Theses on the topic "Fisher scoring"

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Whipps, Gene Thomas. "Contributions to Distributed Detection and Estimation over Sensor Networks." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502970194073045.

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Schoonbee, Willem Lodewyk. "The qualitative and quantitative description of growth and condition of silver kob, A. inodorus." Thesis, Link to the online version, 2006. http://hdl.handle.net/10019/1193.

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Books on the topic "Fisher scoring"

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Johnston, T. L. (Terry L. ). A numeric scoring matrix for use in the rapid assessment of extinction vulnerability of marine fish species in the NAFO regions 4VWX, 5Zc. Fisheries and Oceans Canada, 2004.

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Book chapters on the topic "Fisher scoring"

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Longford, Nicholas T. "FISHER SCORING ALGORITHM FOR VARIANCE COMPONENT ANALYSIS OF DATA WITH MULTILEVEL STRUCTURE." In Multilevel Analysis of Educational Data. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-12-108840-8.50019-6.

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Medellin-Castillo, Nahum Andrés, Hilda Guadalupe Cisneros-Ontiveros, Candy Carranza-Álvarez, Cesar Arturo Ilizaliturri-Hernandez, Leticia Guadalupe Yánez-Estrada, and Andrea Guadalupe Rodríguez-López. "Evaluation of the Fish Invasiveness Scoring Kit (FISK v2) for pleco fish or devil fish." In Bacterial Fish Diseases. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-85624-9.00021-x.

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"Landscape Influences on Stream Habitats and Biological Assemblages." In Landscape Influences on Stream Habitats and Biological Assemblages, edited by Nathaniel P. Hitt and Paul L. Angermeier. American Fisheries Society, 2006. http://dx.doi.org/10.47886/9781888569766.ch4.

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<em>Abstract.</em>—A key challenge in stream fish ecology and biomonitoring is to partition local and regional influences on assemblage structure. Numerous studies have identified local determinants of species composition (i.e., competition, predation, habitat availability), but regional influences remain poorly understood. Here, we test the hypotheses that (1) fish dispersal from adjacent streams influences local fish assemblage structure, and (2) the effects of interstream dispersal are mediated by local environmental conditions. We evaluated fish and physical habitat data from the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program in western Virginia streams (<em>n </em>= 55). We found significant effects of adjacent stream size on local species richness, mean reproductive age, and riverine species richness. Large adjacent streams (greater than third-order) were associated with increased species richness in second-order sites. Fourth-order sites showed increased riverine species richness and decreased mean reproductive age in the presence of large adjacent streams. The nonrandom effects of adjacent stream size among sites of various stream orders suggests that local environmental conditions mediate the effects of dispersal from adjacent streams. Measures of channel shape (i.e., depth, width, and sinuosity) and microhabitat complexity (i.e., mean substrate size and woody debris) were associated with local assemblage structure in some cases, but did not account for significant variation in fish metrics explained by adjacent stream size. These results indicate that the ability of fish biomonitoring metrics to detect anthropogenic impacts may be improved by calibrating scoring criteria based on the size of adjacent streams.

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Conference papers on the topic "Fisher scoring"

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Ma, Jun, and Malcolm Hudson. "BLOCK-ITERATIVE FISHER SCORING FOR EMISSION TOMOGRAPHY." In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356811.

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Kang, Jeon-Hyung, and Kristina Lerman. "Scalable mining of social data using stochastic gradient fisher scoring." In the 2103 workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2513577.2513582.

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Sheng-Wang Li, Li-Wei Guo, and Chun-Hua Li. "Fabric defects detecting and rank scoring based on Fisher criterion discrimination." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212106.

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Neumeister, VM, SS Yan, JA McGuire, DE Carvajal, ML Prasad, and DL Rimm. "Abstract P4-09-22: Quantitative immuno-fluorescent evaluation of Her2 expression levels in a prospectively collected cohort of breast cancer cases: Comparison to conventional IHC scoring and FISH." In Abstracts: Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium; December 8-12, 2015; San Antonio, TX. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.sabcs15-p4-09-22.

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Reports on the topic "Fisher scoring"

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Marchetti, F. Sperm Scoring Using Multi-Spectral Flow Imaging and FISH-IS. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/918427.

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Marchetti, F., and P. J. Morrissey. Sperm Scoring Using Multi-Spectral Flow Imaging and FISH-IS Final Report CRADA No. TC02088.0. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1399741.

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