A reproducible robust likelihood approach to inference about marginal characteristics of binary data in paired settings

We introduce a robust likelihood approach to inference about marginal distributional characteristics for paired data without modeling correlation/joint probabilities. This method is reproducible in that it is applicable to paired settings with various sizes. The virtue of the new strategy is elucidated via testing marginal homogeneity in paired triplet scenario. We use simulations and real data analysis to demonstrate the merit of our robust likelihood methodology.

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A robust likelihood approach to inference about the kappa coefficient for correlated binary data

Statistical Methods in Medical Research ◽

10.1177/0962280217751519 ◽

2018 ◽

Vol 28 (4) ◽

pp. 1188-1202 ◽

Cited By ~ 1

Author(s):

Tsung-Shan Tsou

Keyword(s):

Data Analysis ◽

Binary Data ◽

Likelihood Function ◽

Score Test ◽

Real Data ◽

Kappa Coefficient ◽

Correlated Data ◽

Ratio Test ◽

Likelihood Approach ◽

Robust Likelihood

We construct a legitimate likelihood function for the agreement kappa coefficient for correlated data without specifically modelling all levels of correlation. This makes available the likelihood ratio test, the score test and other tools without the knowledge of the underlying distributions. This parametric robust likelihood approach applies to general clustered data scenarios. We provide simulations and real data analysis to demonstrate the advantage of the robust procedure.

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Robust likelihood inference for diagnostic accuracy measures for paired organs

Statistical Methods in Medical Research ◽

10.1177/0962280218798170 ◽

2018 ◽

Vol 28 (10-11) ◽

pp. 3163-3175 ◽

Cited By ~ 1

Author(s):

Tsung-Shan Tsou

Keyword(s):

Diagnostic Accuracy ◽

Likelihood Inference ◽

Paired Data ◽

Data Set ◽

Likelihood Approach ◽

The Common ◽

Accuracy Measures ◽

Robust Likelihood ◽

Fellow Eyes ◽

Joint Probabilities

Paired data arise naturally in Ophthalmology where pairs of eyes undergo diagnostic tests to predict the presence of certain diseases. The common correlation model is popular for modeling the joint probabilities of responses from fellow eyes for inference about accuracy measures. One of the assumptions underlying the model is exchangeability of fellow eyes that stipulates the accuracy measures such as sensitivities/specificities of fellow eyes be equal. We propose a parametric robust likelihood approach to testing the equality of accuracy measures of fellow eyes without modeling correlation. The robust likelihood procedure is applicable for inference about diagnostic accuracy measures in general paired designs. We provide simulations and analyses of a data set in Ophthalmology to demonstrate the effectiveness of the parametric robust procedure.

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A robust likelihood approach to inference about the difference between two multinomial distributions in paired designs

Statistical Methods in Medical Research ◽

10.1177/0962280217691558 ◽

2017 ◽

Vol 27 (10) ◽

pp. 3077-3091 ◽

Cited By ~ 1

Author(s):

Tsung-Shan Tsou

Keyword(s):

Binary Data ◽

Pair Correlation ◽

Real Data ◽

Model Specification ◽

Score Statistic ◽

Parallel Design ◽

Probability Structure ◽

Likelihood Approach ◽

The Difference ◽

Robust Likelihood

Pairing serves as a way of lessening heterogeneity but pays the price of introducing more parameters to the model. This complicates the probability structure and makes inference more intricate. We employ the simpler structure of the parallel design to develop a robust score statistic for testing the equality of two multinomial distributions in paired designs. This test incorporates the within-pair correlation in a data-driven manner without a full model specification. In the paired binary data scenario, the robust score statistic turns out to be the McNemar’s test. We provide simulations and real data analysis to demonstrate the advantage of the robust procedure.

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Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2019-0007 ◽

2020 ◽

Vol 19 (3) ◽

Author(s):

Saheb Foroutaifar

Keyword(s):

Data Analysis ◽

Bayesian Methods ◽

Milk Fat ◽

Prediction Accuracy ◽

Real Data ◽

Fat Percentage ◽

Real Data Analysis ◽

Wide Range ◽

High Heritability ◽

Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

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Empirical Likelihood Testing for Longitudinal Varying Coefficient Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.518.356 ◽

2014 ◽

Vol 518 ◽

pp. 356-360

Author(s):

Chang Qing Liu

Keyword(s):

Data Analysis ◽

Empirical Likelihood ◽

Real Data ◽

Likelihood Method ◽

Varying Coefficient Models ◽

Empirical Likelihood Method ◽

Real Data Analysis ◽

Varying Coefficient ◽

Testing Method

By using the empirical likelihood method, a testing method is proposed for longitudinal varying coefficient models. Some simulations and a real data analysis are undertaken to investigate the power of the empirical likelihood based testing method.

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A hidden Markov model for modelling long-term persistence in multi-site rainfall time series. 2. Real data analysis

Journal of Hydrology ◽

10.1016/s0022-1694(02)00411-0 ◽

2003 ◽

Vol 275 (1-2) ◽

pp. 27-48 ◽

Cited By ~ 37

Author(s):

Mark Thyer ◽

George Kuczera

Keyword(s):

Time Series ◽

Data Analysis ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Real Data ◽

Rainfall Time Series ◽

Real Data Analysis

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Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽

10.3390/math6070124 ◽

2018 ◽

Vol 6 (7) ◽

pp. 124 ◽

Cited By ~ 1

Author(s):

Elena Barton ◽

Basad Al-Sarray ◽

Stéphane Chrétien ◽

Kavya Jagan

Keyword(s):

Time Series ◽

Signal Processing ◽

Data Analysis ◽

Real Data ◽

Real Data Analysis ◽

Convex Optimisation ◽

Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

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