22 research outputs found
Robustness against outliers in ordinal response model via divergence approach
This study deals with the problem of outliers in ordinal response model,
which is a regression on ordered categorical data as the response variable.
``Outlier" means that the combination of ordered categorical data and its
covariates is heterogeneous compared to other pairs. Although the ordinal
response model is important for data analysis in various fields such as
medicine and social sciences, it is known that the maximum likelihood method
with probit, logit, log-log and complementary log-log link functions, which are
often used, is strongly affected by outliers, and statistical analysts are
forced to limit their analysis when there may be outliers in the data. To solve
this problem, this paper provides inference methods with two robust divergences
(the density-power and -divergences). We also derive influence
functions for the proposed methods and show conditions on the link function for
them to be bounded and to redescendence. Since the commonly used link functions
satisfy these conditions, the analyst can perform robust and flexible analysis
with our methods. In addition, and this is a result that further highlights our
contributions, we show that the influence function in the maximum likelihood
method does not have redescendence for any link function in the ordinal
response model. Through numerical experiments using artificial and two real
data, we show that the proposed methods perform better than the maximum
likelihood method with and without outliers in the data for various link
functions.Comment: 30 page
Two-dimensional fluid viscosity measurement in microchannel flow using fluorescence polarization imaging
This study describes the development of a noncontact and two-dimensional fluid viscosity measurement technique based on fluorescence polarization microscopy. This technique exploits fluorescence depolarization due to rotational Brownian motion of fluorophores and determines fluid viscosity in microchannel flow by measuring steady-state fluorescence polarization. The main advantage of the technique is that planar distributions of fluid viscosity can be visualized by noncontact optical measurement, while commonly-used mechanical viscometers measure the viscosity of bulk liquids. Moreover, steady-state polarization measurements are realized using a simpler experimental setup compared to other noncontact techniques such as time-resolved fluorescence lifetime/polarization measurements. The relationship between the fluid viscosity (Ī¼) and the fluorescence polarization degree () was experimentally obtained using casein molecules labeled with fluorescein isothiocyanate as a fluorescent probe. The fluid viscosity was controlled within the range of 0.7-3.0 mPa s, which is the range often encountered in biological materials, by mixing sucrose or glucose with the solution. The fluid temperature was maintained uniform at 30 Ā°C during the measurement. The calibration result showed that 1/ linearly increased with 1/Ī¼ which qualitatively agreed well with the theoretical prediction. The measurement uncertainty was 7.5%-9.5% based on the slope of the calibration curve. The viscosity gradient generated by the mass diffusion between the two solutions co-flowing in the Y-shaped microchannel was clearly visualized under uniform temperature conditions by applying the calibration curve. Finally, the influence of the temperature change on was experimentally evaluated. The results supported the applicability of the present technique for visualization of the viscosity distribution induced by temperature change. These results confirmed the feasibility of the present technique for analyzing microscale viscosity fields associated with mass transport or temperature change
Semiparametric Copula Estimation for Spatially Correlated Multivariate Mixed Outcomes: Analyzing Visual Sightings of Fin Whales from Line Transect Survey
Multivariate data having both continuous and discrete variables is known as
mixed outcomes and has widely appeared in a variety of fields such as ecology,
epidemiology, and climatology. In order to understand the probability structure
of multivariate data, the estimation of the dependence structure among mixed
outcomes is very important. However, when location information is equipped with
multivariate data, the spatial correlation should be adequately taken into
account; otherwise, the estimation of the dependence structure would be
severely biased. To solve this issue, we propose a semiparametric Bayesian
inference for the dependence structure among mixed outcomes while eliminating
spatial correlation. To this end, we consider a hierarchical spatial model
based on the rank likelihood and a latent multivariate Gaussian process. We
develop an efficient algorithm for computing the posterior using the Markov
Chain Monte Carlo. We also provide a scalable implementation of the model using
the nearest-neighbor Gaussian process under large spatial datasets. We conduct
a simulation study to validate our proposed procedure and demonstrate that the
procedure successfully accounts for spatial correlation and correctly infers
the dependence structure among outcomes. Furthermore, the procedure is applied
to a real example collected during an international synoptic krill survey in
the Scotia Sea of the Antarctic Peninsula, which includes sighting data of fin
whales (Balaenoptera physalus), and the relevant oceanographic data.Comment: 23 pages, 5 figure
Androgenās effects in female
The metabolic effects of androgens and their underlying mechanisms in females have been revealed by recent studies. An excess of androgens can have adverse effects on feeding behavior and metabolic functions and induce metabolic disorders / diseases, such as obesity, insulin resistance, and diabetes, in women and experimental animals of reproductive age. Interestingly, these effects of androgens are not observed in ovariectomized animals, indicating that their effects might be dependent on the estrogen milieu. Central and peripheral mechanisms, such as alterations in the activity of hypothalamic factors, reductions in energy expenditure, skeletal muscle insulin resistance, and Ī²-cell dysfunction, might be related to these androgensā effects
Association Between PSCA Variants and Duodenal Ulcer Risk
Background: While duodenal ulcer (DU) and gastric cancer (GC) are both H. pylori infection-related diseases, individuals with DU are known to have lower risk for GC. Many epidemiological studies have identified the PSCA rs2294008 T-allele as a risk factor of GC, while others have found an association between the rs2294008 C-allele and risk of DU and gastric ulcer (GU). Following these initial reports, however, few studies have since validated these associations. Here, we aimed to validate the association between variations in PSCA and the risk of DU/GU and evaluate its interaction with environmental factors in a Japanese population.
Methods: Six PSCA SNPs were genotyped in 584 DU cases, 925 GU cases, and 8,105 controls from the Japan Multi-Institutional Collaborative Cohort (J-MICC). Unconditional logistic regression models were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between the SNPs and risk of DU/GU.
Results: PSCA rs2294008 C-allele was associated with per allele OR of 1.34 (95% CI, 1.18ā1.51; P = 2.28 Ć 10ā6) for the risk of DU. This association was independent of age, sex, study site, smoking habit, drinking habit, and H. pylori status. On the other hand, we did not observe an association between the risk of GU and PSCA SNPs.
Conclusions: Our study confirms an association between the PSCA rs2294008 C-allele and the risk of DU in a Japanese population
Generalized Cram\'er's coefficient via -divergence for contingency tables
This study proposes measures describing the strength of association between
the row and column variables via the -divergence. Cram\'er's coefficient is
a possible mechanism for the analysis of two-way contingency tables. Tomizawa
et al. (2004) proposed more general measures, including Cram\'er's coefficient,
using the power-divergence. In this paper, we propose more general measures and
show some of their properties, demonstrating that the proposed measures are
beneficial for comparing the strength of association in several tables.Comment: 20 page
An Index for the Degree and Directionality of Asymmetry for Square Contingency Tables with Ordered Categories
For square contingency tables with ordered categories, an index based on Cressie and Read's power divergence (or Patil and Taillie's diversity index) has been proposed in order to measure the degree of departure from symmetry. Although there are two types of maximum asymmetry (i.e., whether (1) all the observations concentrate in the top-right cell in the table, or (2) they concentrate in the bottom-left cell), the existing index cannot distinguish the two directions of maximum asymmetry. This paper proposes a directional index based on an arc-cosine function in order to simultaneously represent the degree and directionality of asymmetry.
The proposed index would be useful for comparing degrees of asymmetry for several square contingency tables. Numerical examples show the utility of the proposed index using some datasets. We evaluate the usefulness of the proposed index by applying it to real data of the clinical study. The proposed index provides analysis results that are easier to interpret than the existing index
Two-Dimensional Index of Departure from the Symmetry Model for Square Contingency Tables with Nominal Categories
In the analysis of two-way contingency tables, the degree of departure from independence is measured using measures of association between row and column variables (e.g., Yuleās coefficients of association and of colligation, CramĆ©rās coefficient, and Goodman and Kruskalās coefficient). On the other hand, in the analysis of square contingency tables with the same row and column classifications, we are interested in measuring the degree of departure from symmetry rather than independence. Over past years, many studies have proposed various types of indexes based on their power divergence (or diversity index) to represent the degree of departure from symmetry. This study proposes a two-dimensional index to measure the degree of departure from symmetry in terms of the log odds of each symmetric cell with respect to the main diagonal of the table. By measuring the degree of departure from symmetry in terms of the log odds of each symmetric cell, the analysis results are easier to interpret than existing indexes. Numerical experiments show the utility of the proposed two-dimensional index. We show the usefulness of the proposed two-dimensional index by using real data
Two-Dimensional Index of Departure from the Symmetry Model for Square Contingency Tables with Nominal Categories
In the analysis of two-way contingency tables, the degree of departure from independence is measured using measures of association between row and column variables (e.g., Yuleās coefficients of association and of colligation, CramĆ©rās coefficient, and Goodman and Kruskalās coefficient). On the other hand, in the analysis of square contingency tables with the same row and column classifications, we are interested in measuring the degree of departure from symmetry rather than independence. Over past years, many studies have proposed various types of indexes based on their power divergence (or diversity index) to represent the degree of departure from symmetry. This study proposes a two-dimensional index to measure the degree of departure from symmetry in terms of the log odds of each symmetric cell with respect to the main diagonal of the table. By measuring the degree of departure from symmetry in terms of the log odds of each symmetric cell, the analysis results are easier to interpret than existing indexes. Numerical experiments show the utility of the proposed two-dimensional index. We show the usefulness of the proposed two-dimensional index by using real data