A Multiple Comparison Procedure Based on a Variant of the Schwarz Information Criterion in a Mixed Model

Abstract Maximum likelihood methodology was applied to determine the mode of inheritance of rare binary traits with data structures typical for swine populations. The genetic models considered included a monogenic, a digenic, a polygenic, and three mixed polygenic and major gene models. The main emphasis was on the detection of major genes acting on a polygenic background. Deterministic algorithms were employed to integrate and maximize likelihoods. A simulation study was conducted to evaluate model selection and parameter estimation. Three designs were simulated that differed in the number of sires/number of dams within sires (10/10, 30/30, 100/30). Major gene effects of at least one SD of the liability were detected with satisfactory power under the mixed model of inheritance, except for the smallest design. Parameter estimates were empirically unbiased with acceptable standard errors, except for the smallest design, and allowed to distinguish clearly between the genetic models. Distributions of the likelihood ratio statistic were evaluated empirically, because asymptotic theory did not hold. For each simulation model, the Average Information Criterion was computed for all models of analysis. The model with the smallest value was chosen as the best model and was equal to the true model in almost every case studied.

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Power of a stagewise intersection protected multiple comparison procedure

Communications in Statistics - Simulation and Computation ◽

10.1080/03610919608813352 ◽

1996 ◽

Vol 25 (4) ◽

pp. 953-960

Author(s):

Alan Klockars ◽

Gregory Hancock

Keyword(s):

Multiple Comparison ◽

Multiple Comparison Procedure

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Nonparametric Multiple Comparison Procedure Using Alignment Method Under Randomized Block Design

Korean Journal of Applied Statistics ◽

10.5351/kjas.2006.19.3.555 ◽

2006 ◽

Vol 19 (3) ◽

pp. 555-564

Keyword(s):

Block Design ◽

Multiple Comparison ◽

Alignment Method ◽

Randomized Block Design ◽

Multiple Comparison Procedure

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Effect of phoneme variations on blind reverberation time estimation

Acta Acustica ◽

10.1051/aacus/2020001 ◽

2020 ◽

Vol 4 (1) ◽

pp. 3

Author(s):

Andrea Andrijašević

Keyword(s):

Time Estimation ◽

Multiple Comparison ◽

Reverberation Time ◽

Impulse Responses ◽

Estimation Errors ◽

Significance Level ◽

Frequency Bands ◽

Interaction Terms ◽

Multiple Comparison Procedure

This study focuses on an unexplored aspect of the performance of algorithms for blind reverberation time (T) estimation – on the effect that speech signal’s phonetic content has on the value of the estimate of T that is obtained from the reverberant version of that signal. To this end, the performance of three algorithms is assessed on a set of logatome recordings artificially reverberated with room impulse responses from four rooms, with their T20 value in the [0.18, 0.55] s interval. Analyses of variance showed that the null hypotheses of equal means of estimation errors can be rejected at the significance level of 0.05 for the interaction terms between the factors “vowel”, “consonant”, and “room”, while the results of Tukey’s multiple comparison procedure revealed that there are both some similarities in the behaviour of the algorithms and some differences, where the latter are stemming from the differences in the details of algorithms’ implementation such as the number of frequency bands and whether T is estimated continuously or only on the selected, the so-called speech decay, segments of the signal.

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Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data

Stats ◽

10.3390/stats3010006 ◽

2020 ◽

Vol 3 (1) ◽

pp. 56-67

Author(s):

Dewi Rahardja

Keyword(s):

Real Data ◽

Multiple Comparisons ◽

Multiple Comparison ◽

Omnibus Test ◽

Binomial Data ◽

Sequential Tests ◽

Multiple Sample ◽

Multiple Comparison Procedure ◽

Multiple Comparison Procedures ◽

Equality Test

In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.

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