In this paper, we propose an information-theoretic-criterion-based modelselection procedure for log-linear model of contingency tables under multinomial sampling, andestablish the strong consistency of the method unde...In this paper, we propose an information-theoretic-criterion-based modelselection procedure for log-linear model of contingency tables under multinomial sampling, andestablish the strong consistency of the method under some mild conditions. An exponential bound ofmiss detection probability is also obtained. The selection procedure is modified so that it can beused in practice. Simulation shows that the modified method is valid. To avoid selecting the penaltycoefficient in the information criteria, an alternative selection procedure is given.展开更多
Purpose:We aim to extend our investigations related to the Relative Intensity of Collaboration(RIC)indicator,by constructing a confidence interval for the obtained values.Design/methodology/approach:We use Mantel-Haen...Purpose:We aim to extend our investigations related to the Relative Intensity of Collaboration(RIC)indicator,by constructing a confidence interval for the obtained values.Design/methodology/approach:We use Mantel-Haenszel statistics as applied recently by Smolinsky,Klingenberg,and Marx.Findings:We obtain confidence intervals for the RIC indicatorResearch limitations:It is not obvious that data obtained from the Web of Science(or any other database)can be considered a random sample.Practical implications:We explain how to calculate confidence intervals.Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement.Our approach presents a suggestion to solve this problem.Originality/value:Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.展开更多
In this paper the amalgamation of (I+1)×J tables under consistent significance is considered and the sufficient condition is obtained. The sufficient and necessary conditions are given to avoid the famous paradox...In this paper the amalgamation of (I+1)×J tables under consistent significance is considered and the sufficient condition is obtained. The sufficient and necessary conditions are given to avoid the famous paradoxes 'YAP' and 'YRP'. A practical algorithm is given to compute the critical value when pooling tables with odds ratio less than 1.展开更多
For the analysis of square contingency tables with the same row and column ordinal classifications, this article proposes new models which indicate the structures of symmetry with respect to the anti-diagonal of the t...For the analysis of square contingency tables with the same row and column ordinal classifications, this article proposes new models which indicate the structures of symmetry with respect to the anti-diagonal of the table. Also, this article gives a simple decomposition in 3 ′ 3 contingency table using the proposed models. The proposed models are applied to grip strength data.展开更多
The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal ...The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.展开更多
Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions.Information about covariation can be learned from either a summary description of frequencies(i.e.,conting...Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions.Information about covariation can be learned from either a summary description of frequencies(i.e.,contingency tables)or trial-by-trial experience(i.e.,sampling individual instances).Two studies were conducted to investigate the impact of information learning mode(i.e.,description vs.self-truncated sampling)on covariation judgment and related decision.In each trial under the description condition,participants were presented with a contingency table with explicit cell frequencies,whereas in each trial under the self-truncated sampling condition,participants were allowed to determine when to stop sampling instances and thus the actual sample size.To eliminate sampling error,an other-yoked(i.e.,between-subject)design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self-truncated sampling condition.Experiment 1 showed that the selftruncated sampling condition led to more moderate covariation judgments than the description condition(i.e.,a description-experience gap).Experiment 2 demonstrated further that the same gap extended to covariation-related decisions in terms of relative contingent preference(RCP).Regressive frequency estimation under self-truncated sampling appeared to underlie the consistent gaps found in the two studies,whereas the impact of regressive diagnosticity(i.e.,the same sample of instances was viewed as less diagnostic under description than under self-truncated sampling)or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data.Future research might examine alternative accounts of the observed gaps,such as the impacts of belief updating and stopping rule under self-truncated sampling.展开更多
Graphical models are wildly used to describe conditional dependence relationships among interacting random variables.Among statistical inference problems of a graphical model,one particular interest is utilizing its i...Graphical models are wildly used to describe conditional dependence relationships among interacting random variables.Among statistical inference problems of a graphical model,one particular interest is utilizing its interaction structure to reduce model complexity.As an important approach to utilizing structural information,decomposition allows a statistical inference problem to be divided into some sub-problems with lower complexities.In this paper,to investigate decomposition of covariate-dependent graphical models,we propose some useful definitions of decomposition of covariate-dependent graphical models with categorical data in the form of contingency tables.Based on such a decomposition,a covariate-dependent graphical model can be split into some sub-models,and the maximum likelihood estimation of this model can be factorized into the maximum likelihood estimations of the sub-models.Moreover,some sufficient and necessary conditions of the proposed definitions of decomposition are studied.展开更多
基金This research is partially supported by National Natural Science Foundation of China (10171094),Ph.D. Program Foundation of Ministry of Education of China and Special Foundations of the Chinese Academy of SciencesUSTC.
文摘In this paper, we propose an information-theoretic-criterion-based modelselection procedure for log-linear model of contingency tables under multinomial sampling, andestablish the strong consistency of the method under some mild conditions. An exponential bound ofmiss detection probability is also obtained. The selection procedure is modified so that it can beused in practice. Simulation shows that the modified method is valid. To avoid selecting the penaltycoefficient in the information criteria, an alternative selection procedure is given.
文摘Purpose:We aim to extend our investigations related to the Relative Intensity of Collaboration(RIC)indicator,by constructing a confidence interval for the obtained values.Design/methodology/approach:We use Mantel-Haenszel statistics as applied recently by Smolinsky,Klingenberg,and Marx.Findings:We obtain confidence intervals for the RIC indicatorResearch limitations:It is not obvious that data obtained from the Web of Science(or any other database)can be considered a random sample.Practical implications:We explain how to calculate confidence intervals.Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement.Our approach presents a suggestion to solve this problem.Originality/value:Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.
文摘In this paper the amalgamation of (I+1)×J tables under consistent significance is considered and the sufficient condition is obtained. The sufficient and necessary conditions are given to avoid the famous paradoxes 'YAP' and 'YRP'. A practical algorithm is given to compute the critical value when pooling tables with odds ratio less than 1.
文摘For the analysis of square contingency tables with the same row and column ordinal classifications, this article proposes new models which indicate the structures of symmetry with respect to the anti-diagonal of the table. Also, this article gives a simple decomposition in 3 ′ 3 contingency table using the proposed models. The proposed models are applied to grip strength data.
文摘The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.
基金National Natural Science Foundation of China,Grant/Award Number:31872780。
文摘Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions.Information about covariation can be learned from either a summary description of frequencies(i.e.,contingency tables)or trial-by-trial experience(i.e.,sampling individual instances).Two studies were conducted to investigate the impact of information learning mode(i.e.,description vs.self-truncated sampling)on covariation judgment and related decision.In each trial under the description condition,participants were presented with a contingency table with explicit cell frequencies,whereas in each trial under the self-truncated sampling condition,participants were allowed to determine when to stop sampling instances and thus the actual sample size.To eliminate sampling error,an other-yoked(i.e.,between-subject)design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self-truncated sampling condition.Experiment 1 showed that the selftruncated sampling condition led to more moderate covariation judgments than the description condition(i.e.,a description-experience gap).Experiment 2 demonstrated further that the same gap extended to covariation-related decisions in terms of relative contingent preference(RCP).Regressive frequency estimation under self-truncated sampling appeared to underlie the consistent gaps found in the two studies,whereas the impact of regressive diagnosticity(i.e.,the same sample of instances was viewed as less diagnostic under description than under self-truncated sampling)or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data.Future research might examine alternative accounts of the observed gaps,such as the impacts of belief updating and stopping rule under self-truncated sampling.
基金supported by the National Key R&D Program of China (Grant 2020YFA0714102)the National Natural Science Foundation of China (Grant 12171079).
文摘Graphical models are wildly used to describe conditional dependence relationships among interacting random variables.Among statistical inference problems of a graphical model,one particular interest is utilizing its interaction structure to reduce model complexity.As an important approach to utilizing structural information,decomposition allows a statistical inference problem to be divided into some sub-problems with lower complexities.In this paper,to investigate decomposition of covariate-dependent graphical models,we propose some useful definitions of decomposition of covariate-dependent graphical models with categorical data in the form of contingency tables.Based on such a decomposition,a covariate-dependent graphical model can be split into some sub-models,and the maximum likelihood estimation of this model can be factorized into the maximum likelihood estimations of the sub-models.Moreover,some sufficient and necessary conditions of the proposed definitions of decomposition are studied.
