Generalized Likelihood Ratio Test and Cox's<i>F</i>-Test Based on Fuzzy Lifetime Data

Shafiq, M. Zubair; Atif, Muhammad; Viertl, Reinhard Karl Wolfgang

doi:10.1002/int.21825

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…Another issue is the use of high-cardinality discrete covariates, necessitating some form of dimension embedding. Such properties are becoming typical in medical records [41,42,43] and are also present in this study.…”

Section: Addressed Limitations Of Typical Survival Modelssupporting

confidence: 61%

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Variational Bayes survival analysis for unemployment modelling

Boškoski,

Perne,

Rameša

et al. 2021

Preprint

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Mathematical modelling of unemployment dynamics attempts to predict the probability of a job seeker finding a job as a function of time. This is typically achieved by using information in unemployment records. These records are right censored, making survival analysis a suitable approach for parameter estimation. The proposed model uses a deep artificial neural network (ANN) as a non-linear hazard function. Through embedding, high-cardinality categorical features are analysed efficiently. The posterior distribution of the ANN parameters are estimated using a variational Bayes method. The model is evaluated on a time-to-employment data set spanning from 2011 to 2020 provided by the Slovenian public employment service. It is used to determine the employment probability over time for each individual on the record. Similar models could be applied to other questions with multi-dimensional, high-cardinality categorical data including censored records. Such data is often encountered in personal records, for example in medical records.

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Section: Addressed Limitations Of Typical Survival Modelssupporting

confidence: 61%

“…All these properties come to use when building survival models using personal records, for instance medical, PES data, or similar. The records tend to be multi-dimensional and usually have high-cardinality categorical data [41,42,43]. Such data are an ideal candidate for harnessing the capability of the VB-based survival method.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayes survival analysis for unemployment modelling

Boškoski,

Perne,

Rameša

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Subsequently, in addition to classical statistical methodology, fuzzy models are additionally necessary to analyze realistic lifetime data. Realizing the importance of fuzziness research has been conducted from the last couple of decades, [20][21][22][23][24][25][26][27][28][29][30][31][32][33] but still most of the times fuzziness of the individual observations is ignored, which leads to non-representative estimates. For threeparameter log-normal distribution the parameter estimates based on fuzzy life times are presented in Shafiq et al; 34 therefore, in this article, generalized (fuzzy) estimators for three-parameter lifetime distributions, that is, Weibull, Pareto, and Gamma are proposed.…”

Section: Lifetime Analysismentioning

confidence: 99%

Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times

Shafiq

Atif

Viertl

2017

Advances in Mechanical Engineering

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Besides errors and stochastic variation, the observed data always have more or less imprecision. In contrast, classical statistical procedures are based on precise measurements and do not allow imprecision of the individual observations. Therefore, to integrate imprecision of the observations, classical statistical techniques are required to generalize for fuzzy numbers. Since lifetime measurements are also more or less fuzzy, therefore, this study was aimed to generalize parameters and reliability functions estimation for the three-parameter lifetime distributions in such manners to integrate fuzziness of the lifetime data. The obtained estimators assimilate fuzziness and stochastic variation of lifetime observations, whereas classical techniques are only based on stochastic variation. This propagation of fuzziness makes the proposed estimators more realistic and suitable for lifetime analysis. Therefore, in order to model lifetime data, the suggested techniques are far better alternative.

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“…In addition, the assumptions of crisp data and crisp hypotheses (on which the sign test is also founded) are often inaccurate as most observations are more or less imprecise (see Shafiq et al 2 ) or there is uncertainty regarding a hypothesized quantile value. For these reasons, some authors have considered approaches of fuzzy statistics to deal appropriately with fuzziness in data and hypotheses formulation.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19

Chukhrova

Johannssen

2021

Int J Intell Syst

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The sign test is one of the most popular nonparametric tests for location problems and allows testing for any quantile of a population. However, the common sign test has serious drawbacks such as loss of information by considering solely signs of observations but not their magnitudes, various problems related to handling of ties in the data, and the lack of embedding uncertainty regarding the fraction of underlying quantile. To address these issues, we present an extended sign test based on fuzzy categories and fuzzy formulated hypotheses that improves the generality, versatility, and practicability of the common sign test. This generalized test procedure is neat in theory and practice and avoids disadvantages that are often associated with fuzzy tests (e.g., a considerably higher complexity of the underlying model, a fuzzy test decision, and a possibilistic instead of a probabilistic interpretation of test results). In addition, we perform a comprehensive case study on COVID‐19 in HIV‐infected individuals with a focus on human body temperature and related measurement problems. The results of the study clearly indicate that fuzzy categories and fuzzy hypotheses improve the performance of the sign test.

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Generalized Likelihood Ratio Test and Cox'sF-Test Based on Fuzzy Lifetime Data

Cited by 8 publications

References 24 publications

Variational Bayes survival analysis for unemployment modelling

Variational Bayes survival analysis for unemployment modelling

Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times

Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19

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