continuous data
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2022 ◽  
Vol 191 ◽  
pp. 116302
Author(s):  
Akshata K. Naik ◽  
Venkatanareshbabu Kuppili

Author(s):  
Linh H. Nghiem ◽  
Francis K. C. Hui ◽  
Samuel Müller ◽  
A. H. Welsh

2022 ◽  
Author(s):  
Miron Bartosz Kursa

Abstract Kendall transformation is a conversion of an ordered feature into a vector of pairwise order relations between individual values. This way, it preserves ranking of observations and represents it in a categorical form. Such transformation allows for generalisation of methods requiring strictly categorical input, especially in the limit of small number of observations, when discretisation becomes problematic.In particular, many approaches of information theory can be directly applied to Kendall-transformed continuous data without relying on differential entropy or any additional parameters. Moreover, by filtering information to this contained in ranking, Kendall transformation leads to a better robustness at a reasonable cost of dropping sophisticated interactions which are anyhow unlikely to be correctly estimated. In bivariate analysis, Kendall transformation can be related to popular non-parametric methods, showing the soundness of the approach.The paper also demonstrates its efficiency in multivariate problems, as well as provides an example analysis of a real-world data.


F1000Research ◽  
2022 ◽  
Vol 11 ◽  
pp. 4
Author(s):  
Soichi Osozawa

Background: In Japan, more than 1,000 participants died shortly after receiving the coronavirus disease 2019 (COVID-19) vaccine, but the causal relation between the injection and death remains uncertain. Methods: Applying long-term personal vital care data for 28 months for an elderly patient, I investigated and evidenced adverse reactions after the first dose of the COVID-19 Pfizer vaccination. Results: The precise, detailed, and continuous data statistically clarified the long-term fevers associated with no meals or drinks. Interrupted time series analysis showed significant and fluctuating increases of body temperatures, pressures, and pulses, although solely long-term plots showed an abrupt and timely increase in these vital data after the vaccine. Conclusions: Anorexia was fatal, and newly reported in the present care records since the patient received the first dose of the COVID-19 vaccine.


2022 ◽  
Vol 16 ◽  
pp. 263235242110705
Author(s):  
Carol Chunfeng Wang ◽  
Ellen Yichun Han ◽  
Mark Jenkins ◽  
Xuepei Hong ◽  
Shuqin Pang ◽  
...  

Introduction: This study aimed to synthesise the best available evidence on the safety and efficacy of using moxibustion and/or acupuncture to manage cancer-related insomnia (CRI). Methods: The PRISMA framework guided the review. Nine databases were searched from its inception to July 2020, published in English or Chinese. Randomised clinical trials (RCTs) of moxibustion and or acupuncture for the treatment of CRI were selected for inclusion. Methodological quality was assessed using the method suggested by the Cochrane collaboration. The Cochrane Review Manager was used to conduct a meta-analysis. Results: Fourteen RCTs met the eligibility criteria. Twelve RCTs used the Pittsburgh Sleep Quality Index (PSQI) score as continuous data and a meta-analysis showed positive effects of moxibustion and or acupuncture ( n = 997, mean difference (MD) = −1.84, 95% confidence interval (CI) = −2.75 to −0.94, p < 0.01). Five RCTs using continuous data and a meta-analysis in these studies also showed significant difference between two groups ( n = 358, risk ratio (RR) = 0.45, 95% CI = 0.26–0.80, I2 = 39%). Conclusion: The meta-analyses demonstrated that moxibustion and or acupuncture showed a positive effect in managing CRI. Such modalities could be considered an add-on option in the current CRI management regimen.


2021 ◽  
Vol 54 (1) ◽  
Author(s):  
Pau Amaro Seoane ◽  
Manuel Arca Sedda ◽  
Stanislav Babak ◽  
Christopher P. L. Berry ◽  
Emanuele Berti ◽  
...  

AbstractThe science objectives of the LISA mission have been defined under the implicit assumption of a 4-years continuous data stream. Based on the performance of LISA Pathfinder, it is now expected that LISA will have a duty cycle of $$\approx 0.75$$ ≈ 0.75 , which would reduce the effective span of usable data to 3 years. This paper reports the results of a study by the LISA Science Group, which was charged with assessing the additional science return of increasing the mission lifetime. We explore various observational scenarios to assess the impact of mission duration on the main science objectives of the mission. We find that the science investigations most affected by mission duration concern the search for seed black holes at cosmic dawn, as well as the study of stellar-origin black holes and of their formation channels via multi-band and multi-messenger observations. We conclude that an extension to 6 years of mission operations is recommended.


Stats ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Felix Mbuga ◽  
Cristina Tortora

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.


2021 ◽  
Vol 923 (2) ◽  
pp. L30
Author(s):  
Jin-Hang Zou ◽  
Bin-Bin Zhang ◽  
Guo-Qiang Zhang ◽  
Yu-Han Yang ◽  
Lang Shao ◽  
...  

Abstract We performed a systematic search for X-ray bursts of the SGR J1935+2154 using the Fermi Gamma-ray Burst Monitor continuous data dated from 2013 January to 2021 October. Eight bursting phases, which consist of a total of 353 individual bursts, are identified. We further analyze the periodic properties of our sample using the Lomb–Scargle periodogram. The result suggests that those bursts exhibit a period of ∼238 days with a ∼63.2% duty cycle. Based on our analysis, we further predict two upcoming active windows of the X-ray bursts. Since 2021 July, the beginning date of our first prediction has been confirmed by the ongoing X-ray activities of the SGR J1935+2154.


2021 ◽  
pp. 1471082X2110592
Author(s):  
Jian-Wei Gou ◽  
Ye-Mao Xia ◽  
De-Peng Jiang

Two-part model (TPM) is a widely appreciated statistical method for analyzing semi-continuous data. Semi-continuous data can be viewed as arising from two distinct stochastic processes: one governs the occurrence or binary part of data and the other determines the intensity or continuous part. In the regression setting with the semi-continuous outcome as functions of covariates, the binary part is commonly modelled via logistic regression and the continuous component via a log-normal model. The conventional TPM, still imposes assumptions such as log-normal distribution of the continuous part, with no unobserved heterogeneity among the response, and no collinearity among covariates, which are quite often unrealistic in practical applications. In this article, we develop a two-part nonlinear latent variable model (TPNLVM) with mixed multiple semi-continuous and continuous variables. The semi-continuous variables are treated as indicators of the latent factor analysis along with other manifest variables. This reduces the dimensionality of the regression model and alleviates the potential multicollinearity problems. Our TPNLVM can accommodate the nonlinear relationships among latent variables extracted from the factor analysis. To downweight the influence of distribution deviations and extreme observations, we develop a Bayesian semiparametric analysis procedure. The conventional parametric assumptions on the related distributions are relaxed and the Dirichlet process (DP) prior is used to improve model fitting. By taking advantage of the discreteness of DP, our method is effective in capturing the heterogeneity underlying population. Within the Bayesian paradigm, posterior inferences including parameters estimates and model assessment are carried out through Markov Chains Monte Carlo (MCMC) sampling method. To facilitate posterior sampling, we adapt the Polya-Gamma stochastic representation for the logistic model. Using simulation studies, we examine properties and merits of our proposed methods and illustrate our approach by evaluating the effect of treatment on cocaine use and examining whether the treatment effect is moderated by psychiatric problems.


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