Relevant bibliographies by topics / Likelihood ratio test (LRT)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Likelihood ratio test (LRT)'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Likelihood ratio test (LRT)"

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Zhao, Yueqin, Min Yi, and Ram C. Tiwari. "Extended likelihood ratio test-based methods for signal detection in a drug class with application to FDA’s adverse event reporting system database." Statistical Methods in Medical Research 27, no. 3 (May 2, 2016): 876–90. http://dx.doi.org/10.1177/0962280216646678.

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A likelihood ratio test, recently developed for the detection of signals of adverse events for a drug of interest in the FDA Adverse Events Reporting System database, is extended to detect signals of adverse events simultaneously for all the drugs in a drug class. The extended likelihood ratio test methods, based on Poisson model (Ext-LRT) and zero-inflated Poisson model (Ext-ZIP-LRT), are discussed and are analytically shown, like the likelihood ratio test method, to control the type-I error and false discovery rate. Simulation studies are performed to evaluate the performance characteristics of Ext-LRT and Ext-ZIP-LRT. The proposed methods are applied to the Gadolinium drug class in FAERS database. An in-house likelihood ratio test tool, incorporating the Ext-LRT methodology, is being developed in the Food and Drug Administration.
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Huang, Lan, Jyoti Zalkikar, and Ram Tiwari. "Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets." Computational and Mathematical Methods in Medicine 2019 (February 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/1526290.

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Pre- and postmarket drug safety evaluations usually include an integrated summary of results obtained using data from multiple studies related to a drug of interest. This paper proposes three approaches based on the likelihood ratio test (LRT), called the LRT methods, for drug safety signal detection from large observational databases with multiple studies, with focus on identifying signals of adverse events (AEs) from many AEs associated with a particular drug or inversely for signals of drugs associated with a particular AE. The methods discussed include simple pooled LRT method and its variations such as the weighted LRT that incorporates the total drug exposure information by study. The power and type-I error of the LRT methods are evaluated in a simulation study with varying heterogeneity across studies. For illustration purpose, these methods are applied to Proton Pump Inhibitors (PPIs) data with 6 studies for the effect of concomitant use of PPIs in treating patients with osteoporosis and to Lipiodol (a contrast agent) data with 13 studies for evaluating that drug’s safety profiles.
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Brum, Betania, Sidinei José Lopes, Daniel Furtado Ferreira, Lindolfo Storck, and Alberto Cargnelutti Filho. "Likelihood ratio test between two groups of castor oil plant traits." Ciência Rural 46, no. 7 (April 5, 2016): 1158–64. http://dx.doi.org/10.1590/0103-8478cr20151418.

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ABSTRACT: The likelihood ratio test (LRT), to the independence between two sets of variables, allows to identify whether there is a dependency relationship between them. The aim of this study was to calculate the type I error and power of the LRT for determining independence between two sets of variables under multivariate normal distributions in scenarios consisting of combinations of 16 sample sizes; 40 combinations of the number of variables of the two groups; and nine degrees of correlation between the variables (for the power). The rate of type I error and power were calculate at 640 and 5,760 scenarios, respectively. A performance evaluation of the LRT was conducted by computer simulation by the Monte Carlo method, using 2,000 simulations in each scenario. When the number of variables was large (24), the TRV controlled the rate of type I errors and showed high power in sizes greater than 100 samples. For small sample sizes (25, 30 and 50), the test showed good performance because the number of variables did not exceed 12.
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Chen, Huijun, and Tiefeng Jiang. "A study of two high-dimensional likelihood ratio tests under alternative hypotheses." Random Matrices: Theory and Applications 07, no. 01 (January 2018): 1750016. http://dx.doi.org/10.1142/s2010326317500162.

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Let [Formula: see text] be a [Formula: see text]-dimensional normal distribution. Testing [Formula: see text] equal to a given matrix or [Formula: see text] equal to a given pair through the likelihood ratio test (LRT) is a classical problem in the multivariate analysis. When the population dimension [Formula: see text] is fixed, it is known that the LRT statistics go to [Formula: see text]-distributions. When [Formula: see text] is large, simulation shows that the approximations are far from accurate. For the two LRT statistics, in the high-dimensional cases, we obtain their central limit theorems under a big class of alternative hypotheses. In particular, the alternative hypotheses are not local ones. We do not need the assumption that [Formula: see text] and [Formula: see text] are proportional to each other. The condition [Formula: see text] suffices in our results.
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Erickson, Stephen. "A Likelihood-Ratio Test of Twin Zygosity Using Molecular Genetic Markers." Twin Research and Human Genetics 11, no. 1 (February 1, 2008): 41–43. http://dx.doi.org/10.1375/twin.11.1.41.

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AbstractThe importance of using multiple polymorphic genetic markers to determine unambiguously whether a twin pair is monozygotic (MZ) or dizygotic (DZ) has long been recognized. Concordance among a set of markers is used as evidence of monozygosity, as it would be improbable for DZ twins to be concordant at a large number of polymorphic loci. Several sources give a formula for the probability of two DZ twins sharing the same genotype at a locus, assuming knowledge of allele frequencies but not of either twin's genotype; this probability can be used to determine whether a set of markers will reliably distinguish between MZ and DZ status in a randomly selected twin pair. If the shared genotype is known, however, the likelihood-ratio test (LRT) of the null hypothesis of dizygosity against the alternative hypothesis of monozygosity takes into account the observed genotype and, by the Neyman-Pearson lemma, is the most powerful test of its size. The LRT is equivalent to conditioning on the genotype of one of the twins, and computing the probability, assuming DZ status, of the other twin sharing that genotype. The resultingpvalues are frequently lower than those produced by the unconditional probability, especially if rare alleles are observed. The unconditional probability can be recapitulated from conditional probabilities by averaging across all of the conditioned sibling's possible genotypes. To illustrate properties of the LRT applied to multiple markers, the probability distribution of the LRTpvalue is computed from allele frequencies of twelve unlinked markers published in Elbaz et al. (2006) and compared with thepvalue computed from unconditional probabilities.
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Lee, Sunbok. "Detecting Differential Item Functioning Using the Logistic Regression Procedure in Small Samples." Applied Psychological Measurement 41, no. 1 (September 24, 2016): 30–43. http://dx.doi.org/10.1177/0146621616668015.

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The logistic regression (LR) procedure for testing differential item functioning (DIF) typically depends on the asymptotic sampling distributions. The likelihood ratio test (LRT) usually relies on the asymptotic chi-square distribution. Also, the Wald test is typically based on the asymptotic normality of the maximum likelihood (ML) estimation, and the Wald statistic is tested using the asymptotic chi-square distribution. However, in small samples, the asymptotic assumptions may not work well. The penalized maximum likelihood (PML) estimation removes the first-order finite sample bias from the ML estimation, and the bootstrap method constructs the empirical sampling distribution. This study compares the performances of the LR procedures based on the LRT, Wald test, penalized likelihood ratio test (PLRT), and bootstrap likelihood ratio test (BLRT) in terms of the statistical power and type I error for testing uniform and non-uniform DIF. The result of the simulation study shows that the LRT with the asymptotic chi-square distribution works well even in small samples.
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Zhang, Yangsong, Li Dong, Rui Zhang, Dezhong Yao, Yu Zhang, and Peng Xu. "An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/908719.

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An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.
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Kawasaki, Tamae, and Takashi Seo. "Modified Likelihood Ratio Test for Sub-mean Vectors with Two-step Monotone Missing Data in Two-sample Problem." Austrian Journal of Statistics 50, no. 1 (February 3, 2021): 88–104. http://dx.doi.org/10.17713/ajs.v50i1.928.

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This article deals with the problem of testing for two normal sub-mean vectors when the data set have two-step monotone missing observations. Under the assumptions that the population covariance matrices are equal, we obtain the likelihood ratio test (LRT) statistic. Furthermore, an asymptotic expansion for the null distribution of the LRT statistic is derived under the two-step monotone missing data by the perturbation method. Using the result, we propose two improved statistics with good chi-squared approximation. One is the modified LRT statistic by Bartlett correction,and the other is the modified LRT statistic using the modification coefficient by linear interpolation. The accuracy of the approximations are investigated by using a Monte Carlo simulation. The proposed methods are illustrated using an example.
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Kim, Seon Man. "Auditory Device Voice Activity Detection Based on Statistical Likelihood-Ratio Order Statistics." Applied Sciences 10, no. 15 (July 22, 2020): 5026. http://dx.doi.org/10.3390/app10155026.

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This paper proposes a technique for improving statistical-model-based voice activity detection (VAD) in noisy environments to be applied in an auditory hearing aid. The proposed method is implemented for a uniform polyphase discrete Fourier transform filter bank satisfying an auditory device time latency of 8 ms. The proposed VAD technique provides an online unified framework to overcome the frequent false rejection of the statistical-model-based likelihood-ratio test (LRT) in noisy environments. The method is based on the observation that the sparseness of speech and background noise cause high false-rejection error rates in statistical LRT-based VAD—the false rejection rate increases as the sparseness increases. We demonstrate that the false-rejection error rate can be reduced by incorporating likelihood-ratio order statistics into a conventional LRT VAD. We confirm experimentally that the proposed method relatively reduces the average detection error rate by 15.8% compared to a conventional VAD with only minimal change in the false acceptance probability for three different noise conditions whose signal-to-noise ratio ranges from 0 to 20 dB.
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Zhao, Hongbo, Lei Chen, Wenquan Feng, and Chuan Lei. "A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/8535486.

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Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.
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Dissertations / Theses on the topic "Likelihood ratio test (LRT)"

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Stoorhöök, Li, and Sara Artursson. "Hur påverkar avrundningar tillförlitligheten hos parameterskattningar i en linjär blandad modell?" Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279039.

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Tidigare studier visar på att blodtrycket hos gravida sjunker under andra trimestern och sedanökar i ett senare skede av graviditeten. Högt blodtryck hos gravida kan medföra hälsorisker, vilket gör mätningar av blodtryck relevanta. Dock uppstår det osäkerhet då olika personer inom vården hanterar blodtrycksmätningarna på olika sätt. Delar av vårdpersonalen avrundarmätvärden och andra gör det inte, vilket kan leda till svårigheter att tolkablodtrycksutvecklingen. I uppsatsen behandlas ett dataset innehållandes blodtrycksvärden hos gravida genom att skatta nio olika linjära regressionsmodeller med blandade effekter. Därefter genomförs en simuleringsstudie med syfte att undersöka hur mätproblem orsakat av avrundningar påverkar parameterskattningar och modellval i en linjär blandad modell. Slutsatsen är att blodtrycksavrundningarna inte påverkar typ 1-felet men påverkar styrkan. Dock innebär inte detta något problem vid fortsatt analys av blodtrycksvärdena i det verkliga datasetet.
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Barton, William H. "COMPARISON OF TWO SAMPLES BY A NONPARAMETRIC LIKELIHOOD-RATIO TEST." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/99.

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In this dissertation we present a novel computational method, as well as its software implementation, to compare two samples by a nonparametric likelihood-ratio test. The basis of the comparison is a mean-type hypothesis. The software is written in the R-language [4]. The two samples are assumed to be independent. Their distributions, which are assumed to be unknown, may be discrete or continuous. The samples may be uncensored, right-censored, left-censored, or doubly-censored. Two software programs are offered. The first program covers the case of a single mean-type hypothesis. The second program covers the case of multiple mean-type hypotheses. For the first program, an approximate p-value for the single hypothesis is calculated, based on the premise that -2log-likelihood-ratio is asymptotically distributed as ­­χ2(1). For the second program, an approximate p-value for the p hypotheses is calculated, based on the premise that -2log-likelihood-ratio is asymptotically distributed as ­χ2(p). In addition we present a proof relating to use of a hazard-type hypothesis as the basis of comparison. We show that -2log-likelihood-ratio is asymptotically distributed as ­­χ2(1) for this hypothesis. The R programs we have developed can be downloaded free-of-charge on the internet at the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org, package name emplik2. The R-language itself is also available free-of-charge at the same site.
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Dai, Xiaogang. "Score Test and Likelihood Ratio Test for Zero-Inflated Binomial Distribution and Geometric Distribution." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/2447.

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The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate the likelihood of type I error and type II error by changing the values of parameters. In the first chapter, we discuss the motivation behind the work presented in this thesis. Also, we introduce the definitions used throughout the paper. In the second chapter, we derive test statistics for the likelihood ratio test and the score test for the geometric distribution. For the score test, we consider the score test using both the observed information matrix and the expected information matrix, and obtain the score test statistic zO and zI . Chapter 3 discusses the likelihood ratio test and the score test for the inflated binomial distribution. The main parameter of interest is w, so p is a nuisance parameter in this case. We derive the likelihood ratio test statistics and the score test statistics to test w. In both tests, the nuisance parameter p is estimated using maximum likelihood estimator pˆ. We also consider the score test using both the observed and the expected information matrices. Chapter 4 focuses on the score test in the inflated binomial distribution. We generate data to follow the zero inflated binomial distribution by using the package R. We plot the graph of the ratio of the two score test statistics for the sample data, zI /zO , in terms of different values of n0, the number of zero values in the sample. In chapter 5, we discuss and compare the use of the score test using two types of information matrices. We perform a simulation study to estimate the two types of errors when applying the test to the geometric distribution and the inflated binomial distribution. We plot the percentage of the two errors by fixing different parameters, such as the probability p and the number of trials m. Finally, we conclude by briefly summarizing the results in chapter 6.
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Liang, Yi. "Likelihood ratio test for the presence of cured individuals : a simulation study /." Internet access available to MUN users only, 2002. http://collections.mun.ca/u?/theses,157472.

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Emberson, E. A. "The asymptotic distribution and robustness of the likelihood ratio and score test statistics." Thesis, University of St Andrews, 1995. http://hdl.handle.net/10023/13738.

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Cordeiro & Ferrari (1991) use the asymptotic expansion of Harris (1985) for the moment generating function of the score statistic to produce a generalization of Bartlett adjustment for application to the score statistic. It is shown here that Harris's expansion is not invariant under reparameterization and an invariant expansion is derived using a method based on the expected likelihood yoke. A necessary and sufficient condition for the existence of a generalized Bartlett adjustment for an arbitrary statistic is given in terms of its moment generating function. Generalized Bartlett adjustments to the likelihood ratio and score test statistics are derived in the case where the interest parameter is one-dimensional under the assumption of a mis-specified model, where the true distribution is not assumed to be that under the null hypothesis.
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Yu, Yuan. "Tests of Independence in a Single 2x2 Contingency Table with Random Margins." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/625.

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In analysis of the contingency tables, the Fisher's exact test is a very important statistical significant test that is commonly used to test independence between the two variables. However, the Fisher' s exact test is based upon the assumption of the fixed margins. That is, the Fisher's exact test uses information beyond the table so that it is conservative. To solve this problem, we allow the margins to be random. This means that instead of fitting the count data to the hypergeometric distribution as in the Fisher's exact test, we model the margins and one cell using multinomial distribution, and then we use the likelihood ratio to test the hypothesis of independence. Furthermore, using Bayesian inference, we consider the Bayes factor as another test statistic. In order to judge the test performance, we compare the power of the likelihood ratio test, the Bayes factor test and the Fisher's exact test. In addition, we use our methodology to analyse data gathered from the Worcester Heart Attack Study to assess gender difference in the therapeutic management of patients with acute myocardial infarction (AMI) by selected demographic and clinical characteristics.
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Shen, Paul. "Empirical Likelihood Tests For Constant Variance In The Two-Sample Problem." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1544187568883762.

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Ngunkeng, Grace. "Statistical Analysis of Skew Normal Distribution and its Applications." Bowling Green State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1370958073.

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Yumin, Xiao. "Robustness of the Likelihood Ratio Test for Periodicity in Short Time Series and Application to Gene Expression Data." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175807.

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Lynch, O'Neil. "Mixture distributions with application to microarray data analysis." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/2075.

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The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. In this dissertation we proposed two methods to determine differentially expressed genes. For the penalized normal mixture model (PMMM) to determine genes that are differentially expressed, we penalized both the variance and the mixing proportion parameters simultaneously. The variance parameter was penalized so that the log-likelihood will be bounded, while the mixing proportion parameter was penalized so that its estimates are not on the boundary of its parametric space. The null distribution of the likelihood ratio test statistic (LRTS) was simulated so that we could perform a hypothesis test for the number of components of the penalized normal mixture model. In addition to simulating the null distribution of the LRTS for the penalized normal mixture model, we showed that the maximum likelihood estimates were asymptotically normal, which is a first step that is necessary to prove the asymptotic null distribution of the LRTS. This result is a significant contribution to field of normal mixture model. The modified p-value approach for detecting differentially expressed genes was also discussed in this dissertation. The modified p-value approach was implemented so that a hypothesis test for the number of components can be conducted by using the modified likelihood ratio test. In the modified p-value approach we penalized the mixing proportion so that the estimates of the mixing proportion are not on the boundary of its parametric space. The null distribution of the (LRTS) was simulated so that the number of components of the uniform beta mixture model can be determined. Finally, for both modified methods, the penalized normal mixture model and the modified p-value approach were applied to simulated and real data.
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Books on the topic "Likelihood ratio test (LRT)"

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Kunst, Robert M. A Likelihood-Ratio Test for Seasonal Unit Roots. Wien: Inst.fur Hohere Studien, 1988.

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Srivastava, M. S. Saddlepoint method for obtaining tail probability of Wilk's likelihood ratio test. Toronto: University of Toronto, Dept. of Statistics, 1988.

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Alexander, Peter D. G., and Malachy O. Columb. Presentation and handling of data, descriptive and inferential statistics. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0028.

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The need for any doctor to comprehend, assimilate, analyse, and form an opinion on data cannot be overestimated. This chapter examines the presentation and handling of such data and its subsequent statistical analysis. It covers the organization and description of data, measures of central tendency such as mean, median, and mode, measures of dispersion (standard deviation), and the problems of missing data. Theoretical distributions, such as the Gaussian distribution, are examined and the possibility of data transformation discussed. Inferential statistics are used as a means of comparing groups, and the rationale and use of parametric and non-parametric tests and confidence intervals is outlined. The analysis of categorical variables using the chi-squared test and assessing the value of diagnostic tests using sensitivity, specificity, positive and negative predictive values, and a likelihood ratio are discussed. Measures of association are covered, namely linear regression, as is time-to-event analysis using the Kaplan–Meier method. Finally, the chapter discusses the statistical analysis used when comparing clinical measurements—the Bland and Altman method. Illustrative examples, relevant to the practice of anaesthesia, are used throughout and it is hoped that this will provide the reader with an outline of the methodologies employed and encourage further reading where necessary.
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Book chapters on the topic "Likelihood ratio test (LRT)"

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Sahu, Pradip Kumar, Santi Ranjan Pal, and Ajit Kumar Das. "Likelihood Ratio Test." In Estimation and Inferential Statistics, 103–29. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2514-0_4.

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Gasperoni, Francesca, Francesca Ieva, and Anna Maria Paganoni. "Likelihood Ratio Test." In Eserciziario di Statistica Inferenziale, 63–81. Milano: Springer Milan, 2020. http://dx.doi.org/10.1007/978-88-470-3995-7_5.

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Frery, Alejandro C. "Log-Likelihood Ratio Test." In Encyclopedia of Mathematical Geosciences, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_313-1.

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Gupta, A. K., and D. K. Nagar. "Likelihood Ratio Test for Multisample Sphericity." In Advances in Multivariate Statistical Analysis, 111–39. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-0653-7_7.

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Bosgiraud, Jacques. "Nonstandard likelihood ratio test in exponential families." In The Strength of Nonstandard Analysis, 145–69. Vienna: Springer Vienna, 2007. http://dx.doi.org/10.1007/978-3-211-49905-4_10.

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Kirsteins, Ivars P. "A Reduced-Rank Generalized Likelihood-Ratio Test." In Acoustic Signal Processing for Ocean Exploration, 219–24. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1604-6_21.

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Falkenhagen, Undine, Wolfgang Kössler, and Hans-J. Lenz. "A Likelihood Ratio Test for Inlier Detection." In Springer Proceedings in Mathematics & Statistics, 351–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28665-1_26.

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Sul, Jae Hoon, Buhm Han, and Eleazar Eskin. "Increasing Power of Groupwise Association Test with Likelihood Ratio Test." In Lecture Notes in Computer Science, 452–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20036-6_41.

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Yi, Wei. "Automatic Aircraft Recognition Using Maximum Likelihood Ratio Test." In Active Media Technology, 321–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45336-9_37.

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Ferguson, Thomas S. "Asymptotic Distribution of the Likelihood Ratio Test Statistic." In A Course in Large Sample Theory, 144–50. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-4549-5_22.

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Conference papers on the topic "Likelihood ratio test (LRT)"

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Todros, Koby, and Alfred O. Hero. "Measure-transformed quasi likelihood ratio test." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472480.

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An, Taehun, Hwang-Ki Min, Seungwon Lee, and Iickho Song. "Likelihood ratio test for wideband spectrum sensing." In 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, 2013. http://dx.doi.org/10.1109/pacrim.2013.6625488.

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Rekavandi, Aref Miri, Abd-Krim Seghouane, and Robin J. Evans. "Robust Likelihood Ratio Test Using α−Divergence." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053881.

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Newey, Michael, Gerald Benitz, and Stephen Kogon. "A generalized likelihood ratio test for SAR CCD." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489328.

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Liu, Yingxi, and Ahmed Tewfik. "Empirical likelihood ratio test with density function constraints." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638886.

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Moschitta, Antonio, Paolo Carbone, and Carlo Muscas. "Generalized Likelihood Ratio Test for voltage dip detection." In 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings. IEEE, 2010. http://dx.doi.org/10.1109/imtc.2010.5488097.

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Cohen, Andrea, and Christopher Zach. "The Likelihood-Ratio Test and Efficient Robust Estimation." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.263.

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Chung, Pei-Jung, and Kon Max Wong. "A full generalized likelihood ratio test for source detection." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288410.

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Shams, I., and K. Shahanaghi. "Analysis of nonhomogeneous input data using likelihood ratio test." In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5373166.

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Sharifi, Abbas Ali, Morteza Sharifi, and Javad Musevi Niya. "Reputation-based Likelihood Ratio Test with anchor nodes assistance." In 2016 8th International Symposium on Telecommunications (IST). IEEE, 2016. http://dx.doi.org/10.1109/istel.2016.7881781.

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Reports on the topic "Likelihood ratio test (LRT)"

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Swope, Gerald W. Likelihood Ratio Test for the Equivalence of Two Autoregressive Moving-Average Time Series. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada370599.

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De Maio, Antonio, Alfonso Farina, and Michael Wicks. Knowledge-Based Generalized Likelihood Ratio Test (KB-GLRT): Exploiting Knowledge of the Clutter Ridge in Airborne Radar. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada430052.

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Chernoff, Herman, and Eric Lander. Asymptotic Distribution of the Likelihood Ratio Test That a Mixture of Two Binomials is a Single Binomial. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada236714.

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Gray, Gregory C., Jeffrey P. Struewing, Kenneth C. Hyams, Joel Escamilla, and Alan K. Tupponce. Interpreting a Single Antistreptolysin O Test: A Comparison of the Upper Limit of Normal and Likelihood Ratio Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada279323.

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