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Author: search.filters.author.Leerojanaprapa, KanogkanSubject: search.filters.subject.design of experiment

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    Solving the incomplete data problem in Greco-Latin square experimental design by exact-scheme analysis of variance without data imputation
    (2024-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Wongsriya, Sirilak
    ;
    Leerojanaprapa, Kanogkan
    This study introduced a novel exact-scheme analysis of variance to tackle the challenge of incomplete data within the Greco-Latin square experimental design (GLSED), specifically for scenarios with a single missing observation across any treatment and block level, thus eliminating the need for conventional data imputation methods. This approach innovatively addresses and mitigates the bias in the treatment sum of squares, a significant drawback of traditional missing plot techniques, by providing a precise, exact-scheme-based formula for calculating the treatment sum of squares in fixed-effect GLSED contexts with unrecorded values. Moreover, it offers a method for correcting biased treatment sum of squares values, presenting an adjustment mechanism for instances where the least squares method was previously employed to estimate missing values. This comprehensive strategy not only enhances the methodological accuracy and integrity of GLSED studies but also contributes significantly to the field by offering a solution to navigate the complexities of incomplete datasets without resorting to data imputation, thus improving the rigor and validity of experimental designs in the face of missing data challenges.
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    Regression sum of squares of symmetric balanced incomplete block design consisting of another one missing observation by accident
    (2017-07-02)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    A balanced incomplete block design (BIBD) is the effective way to help analyze a treatment variable and one block variable under the condition where experimental units are limited. This paper considered the symmetric balanced incomplete block design (SBIBD) with t treatments and t blocks of size t-1. The trouble of analysis is caused if another one missing value unintentionally occurs in the experiments. The SBIBD with another one missing value was analyzed by means of the exact approach, i.e., the general regression significance testing procedure. There was no ready-made formula in the past. Hence, the paper provided the mathematical formulae for the fitted parameters and the regression sum of squares for the full effect model of experimental data.