Construction and selection of quick switching double-sampling system: sample size tightening

1995 ◽  
Vol 22 (1) ◽  
pp. 105-119 ◽  
Author(s):  
S. Devaraj Arumainayagam ◽  
V. Soundararajan
Keyword(s):  
Sample Size ◽  
Double Sampling ◽  
Sampling System ◽  
Selection Of

data a; * significance level; a=0.05; * variance of difference of two observations on the log scale; * sigmaW = within-subjects standard deviation; sigmaW=0.355; s=sqrt(2)*sigmaW; * total number of subjects (needs to be a multiple of 2); n=58; * error degrees of freedom for AB/BA cross-over with n subjects in total; n2=n-2; * ratio = mu_T/mu_R; ratio=1.00; run; data b; set a; * calculate power; t1=tinv(1-a,n-2); t2=-t1; nc1=(sqrt(n))*((log(ratio)-log(0.8))/s); nc2=(sqrt(n))*((log(ratio)-log(1.25))/s); df=(sqrt(n-2))*((nc1-nc2)/(2*t1)); prob1=probt(t1,df,nc1); prob2=probt(t2,df,nc2); answer=prob2-prob1; power=answer*100; run; proc print data=b; run; As an example of using this SAS code, suppose µ = 1, σ = 0.355, α = 0.05 and n = 58. The power (as a percentage) is calculated as 90.4. The required number of subjects to achieve a given power can easily be obtained by trial and error using a selection of values of n. An alternative approach is to use trial and error directly on the sample size n for a given power. For more on this see Phillips (1990) and Diletti et al. (1991), for example. 7.4 Individual bioequivalence As noted in Section 7.1, individual bioequivalence (IBE) is a criterion for deciding if a patient who is currently being treated with R can be

2003 ◽  
pp. 362-362
Keyword(s):  
Standard Deviation ◽  
Sample Size ◽  
Degrees Of Freedom ◽  
Trial And Error ◽  
Significance Level ◽  
Individual Bioequivalence ◽  
Alternative Approach ◽  
Within Subjects ◽  
Log Ratio ◽  
Selection Of

scholarly journals Resampled dimensional reduction for feature representation in machine learning

2021 ◽  
Author(s):  
Herdiantri Sufriyana ◽  
Yu Wei Wu ◽  
Emily Chia-Yu Su
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Prediction Model ◽  
Sample Size ◽  
Dimensional Reduction ◽  
Latent Variables ◽  
Feature Representation ◽  
Estimated Parameters ◽  
Representation Technique ◽  
Selection Of

Abstract We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can improve generalization by preventing latent variables to overfit data used to conduct the dimensional reduction. However, this technique may warrant more computational capacity and time to conduct the procedure. The key stages consisted of derivation of latent variables from multiple resampling subsets, parameter estimation of latent variables in population, and selection of latent variables transformed by the estimated parameters.

Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers

2017 ◽  
Vol 28 (4) ◽  
pp. 1019-1043 ◽  
Author(s):  
Shi-Fang Qiu ◽  
Xiao-Song Zeng ◽  
Man-Lai Tang ◽  
Wai-Yin Poon
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Test Procedure ◽  
Sample Size Determination ◽  
Double Sampling ◽  
Score Statistic ◽  
Wald Statistic ◽  
Test Procedures ◽  
Practical Applications ◽  
Logit Transformation

Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example.

scholarly journals Aspek Lingual Tegursapa Sosial Masyarakat Jawa Kota Semarang

2018 ◽  
Vol 13 (2) ◽  
pp. 180
Author(s):  
M Suryadi
Keyword(s):  
Data Analysis ◽  
Focus Group ◽  
Structured Interviews ◽  
Sampling System ◽  
Purposive Sampling ◽  
System Data ◽  
Depth Interviews ◽  
Collection Methods ◽  
Separation Of Components ◽  
Selection Of

AbstractThe purpose of this study is to describe lingual aspects that strengthen the speech of the clergy of Java Coastal community in Semarang City. This greeting remarks as a glue of the socioal harmony and horizontal conflict absorbers, as well as a feature of uniqueness in the connection of social intimacy in the coastal region.Research location in Semarang City. Selection of respondents and informants using purposive sampling system. Data collection methods were conducted through structured interviews, in-depth interviews, and focus group discs. The method of data analysis using the separation of components on speech by greeting sad lingual aspects contained in the speech.There are three lingual aspects that influence the power of speech:  speech sauce, variety, and formIntisariTujuan penelitian ini adalah mendeskripsikan aspek lingual yang memperkuat tuturan tegur-sapa social masyarakat Jawa Pesisir di Kota Semarang.  Tuturan bertegur-sapa ini sebagai perekat harmoni sosioal dan peredam konflik horizontal, sekaligus sebagai fitur keunikan dalam pertalian keakraban sosial di wilayah pesisiran.  Lokasi penelitian di Kota Semarang. Pemilihan responden dan informan menggunakan sistem purposive sampling. Metode pengumpulan data dilakukan melalui wawancara terstruktur, wawancara  mendalam, dan focus group discus.Metode analisis data menggunakan pemilahan komponen pada tuturan bertegur-sapa melalui aspek-aspek lingual yang terdapat pada tuturan. Ditemukan tiga aspek lingual yang mempengaruhi kekuatan tuturan bertegur sapa social, yakni kecap ujaran,ragam, dan bentuk.

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