Comments on “A boundary-optimized rejection region test for the two-sample binomial problem”

2018 ◽  
Vol 37 (14) ◽  
pp. 2301-2302
Author(s):  
Antonio Martín Andrés
Keyword(s):  
Rejection Region

scholarly journals The Marketing Mix Affect on the Consumer Buying Decision (Case Study of Sausage Products at PT. Aroma Denpasar)

2017 ◽  
Vol 1 (2) ◽  
pp. 65
Author(s):  
I Made Jaminyasa ◽  
I Made Pulawan ◽  
Anak Agung Media Martadiani ◽  
I Made Suniastha Amerta
Keyword(s):  
Linear Regression ◽  
Product Quality ◽  
Regression Coefficient ◽  
Regression Line ◽  
Positive Influence ◽  
Marketing Mix ◽  
T Test ◽  
Rejection Region ◽  
Line Equation

The more intense competition within the similar business as well as happened in the business of making sausages, especially in Denpasar city. PT. Aroma was one of the companies in Denpasar that produces sausages, corned beef, and nuggets. In an effort to attract consumers to buy sausages, companies pay attention to product quality, price, and promotion. The attitude of each consumer varies before buying and in buying products. Consumer considerations in buying the products that need to be considered by marketers, so that products that are marketed can be accepted and would be bought by the consumers. The linear regression line equation: Y = 0.1920 + 0.2145 X1 + 0.2592 X2 + 0.3828 X3 explains that there was a simultaneous positive influence between product quality, price, and promotion on the buying decision of sausage. The result of t-test of regression coefficient obtained t1-count was 3,3628, t2-count was 3,9879 and t3-count was 6,2641 bigger than t-table equal to 1,980 was in rejection region Ho, hence Ho rejected or Hi accepted. It meant it was true, that there was a positive influence simultaneously between the marketing mix and the consumer buying decision.

Credibility Hypothesis Testing of Variance of Fuzzy Normal Distribution

2017 ◽  
pp. 193-220
Author(s):  
S. Sampath ◽  
B. Ramya
Keyword(s):  
Hypothesis Testing ◽  
Normal Distribution ◽  
Test Procedures ◽  
Rejection Region ◽  
Ratio Criterion ◽  
Expected Values ◽  
Fuzzy Normal Distribution

This paper considers the problem of developing test procedures for testing credibility hypotheses about the variance of fuzzy normal distribution assuming the expected values of the distributions mentioned under null and alternative credibility hypotheses are known and equal. The cases where the underlying hypothesis is simple and composite (one sided) are considered. Tests have been derived with the help of the membership ratio criterion. Properties possessed by the developed tests, like best credibility rejection region and uniformly best rejection region have been studied. Examples are also given to illustrate the usage of the derived tests.

Estimating the Arm-Wise False Discovery Rate in Array Comparative Genomic Hybridization Experiments

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Daniel P Gaile ◽  
Elizabeth D Schifano ◽  
Jeffrey C Miecznikowski ◽  
James J Java ◽  
Jeffrey M Conroy ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Comparative Genomic Hybridization ◽  
Array Comparative Genomic Hybridization ◽  
Multiple Hypothesis Testing ◽  
Comparative Genomic ◽  
Test Statistic ◽  
Genomic Hybridization ◽  
Rejection Region ◽  
Spatially Correlated ◽  
False Discovery

Array Comparative Genomic Hybridization (aCGH) is an array-based technology which provides simultaneous spot assays of relative genetic abundance (RGA) levels at multiple sites across the genome. These spot assays are spatially correlated with respect to genomic location and, as a result, the univariate tests conducted using data generated from these spot assays are also spatially correlated. In the context of multiple hypothesis testing, this spatial correlation complicates the question of how best to define a `discovery' and consequently, how best to estimate the false discovery rate (FDR) corresponding to a given rejection region.One can quantify the number of discoveries as the total number of spots for which the spot-based univariate test statistic falls within a given rejection region. Under this spot-based method, separate but correlated discoveries are identified. We show via a simulation study that the method of Benjamini and Hochberg (1995) can provide a reasonable estimate of the spot-wise FDR, but these results require that the simulated spot assays are categorized as true or false discoveries in a particular way. However, laboratory researchers may actually be interested in estimating a `regional' FDR, rather than a `local' spot-wise FDR. We describe an example of such circumstances, and present a method for estimating the (chromosome) arm-wise False Discovery Rate. In this framework, one can quantify the number of discoveries as the total number of chromosome arms for which at least one spot-based test statistic falls into a given rejection region. Defining the discoveries in this way, both the biological and testing objectives coincide. We provide results from a series of simulations which involved the analysis of preferentially re-sampled spot assay values from a real aCGH dataset.

Response to letter by Antonio Martín Andrés on “A boundary-optimized rejection region test for the two-sample binomial problem”

2018 ◽  
Vol 37 (14) ◽  
pp. 2303-2306
Author(s):  
Erin E. Gabriel ◽  
Martha Nason ◽  
Michael P. Fay ◽  
Dean Follmann
Keyword(s):  
Rejection Region
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