Bootstrap Confidence Regions for Homogeneity Analysis; the Influence of Rotation on Coverage Percentages

Compstat ◽  
1994 ◽  
pp. 337-342 ◽  
Author(s):  
Monica Th. Markus
Keyword(s):  
Confidence Regions ◽  
Homogeneity Analysis

Bridging the Gap between Quantitative and Qualitative Historical Research: an application of multiple regression analysis and homogeneity analysis with alternating least squares

1996 ◽  
Vol 8 (3) ◽  
pp. 133-144 ◽  
Author(s):  
María del Mar del Pozo Andrés ◽  
Jacques F A Braster
Keyword(s):  
Least Squares ◽  
Multiple Regression ◽  
Historical Research ◽  
Alternating Least Squares ◽  
Categorical Variables ◽  
Two Dimensional ◽  
Homogeneity Analysis ◽  
Regression Approach ◽  
Dimensional Picture ◽  
Selection Of

In this article we propose two research techniques that can bridge the gap between quantitative and qualitative historical research. These are: (1) a multiple regression approach that gives information about general patterns between numerical variables and the selection of outliers for qualitative analysis; (2) a homogeneity analysis with alternating least squares that results in a two-dimensional picture in which the relationships between categorical variables are graphically presented.

STUDI KOMPARASI KEPUTUSAN PEMBELIAN ANTARA AYAM GEPREK ABANG IRENG UMS DAN AYAM GEPREK KUMLOT UMS

2020 ◽  
Vol 4 (02) ◽  
Author(s):  
Metdi Permadi ◽  
Siti Maryam ◽  
Ratna Damayanti
Keyword(s):  
Quantitative Methods ◽  
Brand Image ◽  
Comparative Method ◽  
T Test ◽  
Purchase Decision ◽  
Purchasing Decisions ◽  
Homogeneity Analysis ◽  
Significant Difference

The purpose of this study was to determine differences in purchasing decisions between Abang Ireng UMS geprek chicken and UMS Geprek Kumlot chicken in terms of Brand Image, Price and Variation of the menu. This method is called the comparative and quantitative methods with the aim of the comparative method with the aim of finding out the differences in purchasing decisions between variables. The sample of this study was 100 respondents consisting of 50 consumers of Abang Ireng Geprek chickens and 50 Kumlot geprek consumers. This study uses validity, reliability, to test the instrument using homogeneity analysis and independent sample t-test to test differences between variables. Data obtained from questionnaires for respondents. This study was tested using SPSS 20 software. The results of this study indicate that the Brand Image variable has no difference in purchasing decisions. Namely with the t value of the Brand Image variable of 0.753 with the criteria of t table> 0.05, this shows that there is no significant difference, while for the price variables and menu variations there is each difference to the purchase decision, namely the value of t count in the variable price of 0.018 with the criteria of t table

Bootstrap Analysis

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Goodness Of Fit ◽  
Null Distribution ◽  
Real Data ◽  
Confidence Regions ◽  
Confidence Bands ◽  
Cumulative Distribution ◽  
Attractive Alternative ◽  
Bootstrap Analysis ◽  
Parametric Bootstrapping ◽  
Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

Exact tests and confidence regions in nonlinear regression

Statistics ◽  
2005 ◽  
Vol 39 (1) ◽  
pp. 13-42 ◽  
Author(s):  
C. Ittrich ◽  
W.-D. Richter
Keyword(s):  
Nonlinear Regression ◽  
Confidence Regions ◽  
Exact Tests
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