Browsing by Author "Nyakundi, Cornelious"

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    A comparison of two sample approaches to regression calibration for measurement error correction
    (International Journal of Statistics and Applied Mathematics, 2023) Kamun, Samuel Joel; Nyakundi, Cornelious; Simwa, Richard Onyino
    This study compares ways for improving regression calibration. This is a method for combining two samples in order to reduce measurement error and improve the relative efficiency of linear regression models. Since two or more samples are more likely than a single sample to accurately represent the population under study, two samples are used in regression calibration to produce a realistic picture of the actual population. In this investigation, we compared independent estimates derived from two samples using a weight equal to the reciprocal of the estimated sampling probability. The study also examined the estimations produced after combining the two datasets into one, and modified the weight of each sample unit accordingly. The most typical application of regression calibration methods is to account for bias in projected responses induced by measurement inaccuracies in variables. Because of its simplicity, this method is commonly utilized. The conditional expectation of the genuine response is estimated using regression calibration, given that the predictor variables are measured with error and the other covariates are assessed without error. Instead of the unknown genuine response, predictors are estimated and used to examine the link between response and result. Regression calibration programs necessitate extensive knowledge of unobservable true predictors. This information is frequently collected from validation studies that employ unbiased measurements of true predictors. The results of two sample strategies were employed and compared in this study. Device fault, laboratory mistake, human error, difficulty documenting or completing measurements, self-reported errors, and intrinsic vibrations of the underlying instrument can all cause measurement inaccuracies. Covariate measurement error has three consequences: In addition to obscuring data features and making graphical model analysis more difficult, estimates of statistical model parameters might be skewed, and effectiveness in detecting correlations between variables can be severely impaired. This study's two sampling procedures produced satisfactory results.
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    Two Sample Approaches to Regression Calibration for Measurement Error Correction
    (International Journal of Statistical Distributions and Applications, 2023) Kamun, Samuel Joel; Nyakundi, Cornelious; Simwa, Richard Onyino
    The goal of this work is to create methods for enhancing measurement error using regression calibration as a strategy by combining two samples, thereby increasing the relative efficiency of linear regression models. Because two or more samples are more likely to provide an accurate representation of the population than a single sample under inquiry, utilizing two samples in regression calibration is likely to produce a realistic depiction of what the actual population is when error-free. This study has generated independent estimates from two samples and combined them with weights equal to the inverse of their estimated probabilities of sample inclusion. It has also integrated two data sets into a single data set and suitably adjusted the weights on each sampled unit. The regression calibration method is most commonly used to correct predictor-response bias caused by variable measurement imperfections. Because of its simplicity, this method is often used. The fundamental principle behind regression calibration is to estimate the conditional expectation of a genuine response, given predictors measured with error and other covariates supposed to be measured without error. The predicted values are then estimated and used to assess the relationship between the response and an outcome in place of the unknown genuine response. Further information on the unobservable true predictors is required by the regression calibration program. This data is frequently obtained from a validation study that employs unbiased measurements for genuine predictors. This study has employed and compared the results obtained from the two sample approaches. Measuring errors can be produced by a variety of sources, including instrument error, laboratory error, human error, problems in documenting or executing measurements, self-reporting errors, and natural oscillations in the underlying amount. Covariate measurement error has three effects: In addition to hiding the properties of the data, which makes graphical model analysis difficult, it produces bias in parameter estimates for statistical models, resulting in a sometimes significant loss of power for detecting fascinating correlations between variables. The two sample approaches employed by the study have yielded acceptable results.