411,699 research outputs found
Maximally selected chi-square statistics and umbrella orderings
No full textBinary outcomes that depend on an ordinal predictor in a non-monotonic way are common in medical data analysis. Such patterns can be addressed in terms of cutpoints: for example, one looks for two cutpoints that define an interval in the range of the ordinal predictor for which the probability of a positive outcome is particularly high (or low). A chi-square test may then be performed to compare the proportions of positive outcomes in and outside this interval. However, if the two cutpoints are chosen to maximize the chi-square statistic, referring the obtained chi-square statistic to the standard chi-square distribution is an inappropriate approach. It is then necessary to correct the p-value for multiple comparisons by considering the distribution of the maximally selected chi-square statistic instead of the nominal chi-square distribution. Here, we derive the exact distribution of the chi-square statistic obtained by the optimal two cutpoints. We suggest a combinatorial computation method and illustrate our approach by a simulation study and an application to varicella data
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The Hexagon of Alpha Chi Sigma
No full textQuarterly publication of the Alpha Chi Sigma chemistry fraternity containing articles related to chemistry research and the activities of the organization, including local chapters and groups
Recommended from our members
The Hexagon of Alpha Chi Sigma
No full textQuarterly publication of the Alpha Chi Sigma chemistry fraternity containing articles related to chemistry research and the activities of the organization, including local chapters and groups
Recommended from our members
The Hexagon of Alpha Chi Sigma
No full textQuarterly publication of the Alpha Chi Sigma chemistry fraternity containing articles related to chemistry research and the activities of the organization, including local chapters and groups
Recommended from our members
The Hexagon of Alpha Chi Sigma
No full textQuarterly publication of the Alpha Chi Sigma chemistry fraternity containing articles related to chemistry research and the activities of the organization, including local chapters and groups
CHI-1, but not CHI-2 mice acquired an active place avoidance task.
No full textThe sham-CHI, CHI- 1 or CHI-2 groups received four 10-minute trials with a 50-minute intertrial interval. Panel A, Representative tracks of the final trial of sham-CHI, CHI-1 and CHI-2 mice. The 60° stationary shock zone is shown in red. Black circles show the location of the mouse when being shocked. Panel B, Summary of total shock zone entrances during the 4 trials of active place avoidance. The number of shock zone entrances significantly differed on the basis of trial number for the Sham-CHI (F3,12 = 12.88, p 3,18 = 8.07, p = 0.001), but not for the CHI 2 group (F3,21 = 0.32, p = 0.81). Furthermore, there was a statistically significant interaction between group and trial number (F6,51 = 3.81, p < 0.003). The CHI-2 group had significantly more shock zone entrances than the sham-CHI or CHI-1 groups. In contrast, the sham-CHI and CHI-1 groups had a similar number of shock zone entrances (post hoc, p = 0.05) suggesting that the sham-CHI and CHI-1 groups, but not the CHI-2 group acquired the shock zone location.</p
Maximally selected chi-square statistics for at least ordinal scaled variables
No full textThe association between a binary variable Y and a variable X with an at least ordinal measurement scale might be examined by selecting a cutpoint in the range of X and then performing an association test for the obtained 2x2 contingency table using the chi-square statistic. The distribution of the maximally selected chi-square statistic (i.e. the maximal chi-square statistic over all possible cutpoints) under the null-hypothesis of no association between X and Y is different from the known chi-square distribution. In the last decades, this topic has been extensively studied for continuous X variables, but not for non-continuous variables with an at least ordinal measurement scale (which include e.g. classical ordinal or discretized continuous variables). In this paper, we suggest an exact method to determine the distribution of maximally selected chi-square statistics in this context. This novel approach can be seen as a method to measure the association between a binary variable and variables with an at least ordinal scale of different types (ordinal, discretized continuous, etc). As an illustration, this method is applied to a new data set describing pregnancy and birth for 811 babies
Maximally selected chi-square statistics and binary splits of nominal variables
No full textWe address the problem of maximally selected chi-square statistics in the case of a binary Y variable and a nominal X variable with several categories. The distribution of the maximally selected chi-square statistic has already been derived when the best cutpoint is chosen from a continuous or an ordinal X, but not when the best split is chosen from a nominal X. In this paper, we derive the exact distribution of the maximally selected chi-square statistic in this case using a combinatorial approach. Applications of the derived distribution to variable selection and hypothesis testing are discussed based on simulations. As an illustration, our method is applied to a pregnancy and birth data set
Chi-squared test P-value for each TFBS motif in every cell type.
No full textChi-squared test P-value for each TFBS motif in every cell type.</p
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