Sirikasemsuk, Kittiwat
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Sirikasemsuk, Kittiwat
Alternative Name
Sirikasemsuk, K.
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kittiwat.si@kmitl.ac.th
16 results
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Item type:Publication, Analysis of two-missing-observation 4 × 4 latin squares using the exact approach(2018-01-01); This research deals with the analysis of incomplete Latin square designs using the exact approach. Specifically, the study investigated the 4 × 4 Latin square designs with two missing observations without replication. In the research, the general regression significance test (i.e. the exact approach) was used to derive the estimation formulae of fitted parameters for the full and reduced linear statistical models, thereby simplifying the calculation process. In addition, the proposed exact approach-based formulae facilitate the determination of the treatment sum of squares and the error sum of squares, both of which are subsequently employed in the analysis of variance (ANOVA). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Full-model regression sum of squares of randomized complete block design having one unrecorded observation(2019-01-01); ; Sirikasemsuk, SirisakIn the classical design of experiment, a randomized complete block design (RCBD) is a helpful experimental design because this design uses a small number of experimental units. The randomized complete block design is comprised of two factors, i.e., a nuisance factor and a potential factor. In many real experiments, some data might be missing or unrecorded. The instant formulae were not provided for an analysis of variance in this case. This paper took into account the randomized complete block design (RCBD) with a treatments and b blocks. In this contribution, the RCBD with an unrecorded value was analyzed by means of the exact scheme with the model comparison approach. The advantages of the exact scheme are that the unrecorded value for the unfilled cell is not estimated and the treatment sum of squares is unbiased. It is important to note that there is no ready-made formula for RCBD in the past. Hence, this research paper provided the mathematical formulae for the fitted parameters and the overall regression sum of squares (ORSS) for the full model of experimental data. It is also noted that the ORSS is imperative for the analysis of the variance by means of the exact scheme. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Industry cluster using cluster analysis to support industry cluster policy of Thailand(2019-06-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying Bayesian network for noncommunicable diseases risk analysis: Implementing national health examination survey in Thailand(2017-07-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, One missing value problem in Latin square design of any order: Exact analysis of variance(2017-01-01); This research proposes a simplified exact approach based on the general linear model for solving the K × K Latin square design (LSD) with one replicate and one missing value, given the lack of ready-made mathematical formulas for the sub-variance. Under the proposed scheme, the effects of the potential variable were determined by means of the regression sums of squares under the full and reduced treatment models. The mathematical expressions could be applied to the LSD with one missing value of any order. Moreover, the treatment, row and column sums of squares are unbiased. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model(2017-02-01); Luong, Huynh TrungWith supply chains becoming increasingly global, the issue of bullwhip effect, a phenomenon attributable to demand fluctuation in the upstream section of the supply chains, has received greater attention from many researchers. However, most existing research studies on quantifying the bullwhip effect were conducted under the first-order autoregressive [AR(1)] incoming demand process or its variants as the fundamental demand process, thereby failing to account for the retailer demand dependency. This research work thus examined the bullwhip effect for the first-order bivariate vector autoregression [VAR(1)] demand process in a two-stage supply chain consisting of one supplier and two retailers. The impacts of the correlation parameters of the demand process, the correlation coefficient between the two error terms, and the variances of the error terms on the bullwhip effect were investigated. As such, the measure of the bullwhip effect was established using an analytical approach in which the minimum mean square error (MMSE) forecasting method and the base stock policy were applied to all members of the supply chain. Numerical experiments were then conducted to illustrate the behavior of the bullwhip effect with respect to various parameters of the demand processes to see in which situations the bullwhip effect would be absent. In addition, an evaluation of the inventory variance ratio was analyzed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of Bayesian Networks for Diabetes Prediction(2019-01-01); A Bayesian network (BN) can be used to predict the prevalence of diabetes from the cause–effect relationship among risk factors. By applying a BN model, we can capture the interdependencies between direct and indirect risks hierarchically. In this study, we propose to investigate and compare the predictive performances of BN models with non-hierarchical (BNNH), and non-hierarchical and reduced variables (BNNHR) structures, hierarchical structure by expert judgment (BNHE), and hierarchical learning structure (BNHL) with type-2 diabetes. ROC curves, AUC, percentage error, and F1 score were applied to compare performances of those classification techniques. The results of the model comparison from both datasets (training and testing) obtained from the Thai National Health Examination Survey IV ensured that BNHE can predict the prevalence of diabetes most effectively with the highest AUC values of 0.7670 and 0.7760 from the training and the testing dataset, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Factors associated with Thai exporter's interest in using New Dawei deep seaport(2017-01-01); ; Bhundarak, KomnDawei deep seaport in a part of the Dawei Special Economic Zone (Dawei SEZ) in Myanmar aims to support the new economics along the GMS Southern Corridor. The Dawei seaport can serve the potential new industries along the new industry zones. The new port will be the alternative route for Thai exporters in the future as it is under construction. The exploratory study by employing survey was selected and analyzed to identify the significant influencing factors. The results of hypothesis testing by Pearson Chi-Square test confirm the relation between the interest of using new Dawei deep seaport and the location of manufacturer (p-value = 0.027). In addition, the results of t-test confirm the significant six decision variables of Time for transportation (v7), Reliability of service (v10), Port size and capability (v15), Facility (v25), Professionals and skilled labors in port operation (v30), and Port accessibility (v31) are more important for exporters who are interested in Dawei seaport than the exporters who may not be intend to use the new seaport, p-value (1-tailed) < 0.05. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A review on incomplete Latin square design of any order(2016-10-24)In this paper, the aim was to introduce uninitiated readers extensive research on an incomplete Latin square design and pitfalls in methodology for solving the incomplete Latin square design. The incomplete Latin square design commonly leads to difficulties in the analysis of variance (ANOVA). Accordingly, several techniques were discussed by a number of researchers. It should be noted that the values of sums of squares in the ANOVA table by means of a missing-plot approach without adjusting the bias and an exact approach with a general regression significance test are not identical. This paper suggests to readers that the Latin square design of any order consisting of missing values should be analyzed by means of the exact approach or the missing-plot technique with adjusting the bias. A comprehensive review of the literature provided the gap for further research. It was a lack of definite formula for the ANOVA table with the exact approach for the cases of the incomplete Latin square design. It is thus of interest to provide the ready-made formula of the sums of squares by means of the exact approach in order to reduce the calculation process. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimated parameters of 6 x 6 Latin square design consisting of two missing values(2019-01-01); Thachongthumla, KanokwanA design of experiment is a computational technique which is used to select the significant independent variables. The well-designed experiments should reduce the experiment error. We referred to the blocking technique to prevent the error from other variables. This paper focused on the incomplete latin square design (ILSD) based on the blocking technique. The missing values from experiments caused the unbalance design in which the instant formulae were not provided for the analysis of the variance (ANOVA). By means of the exact approach, this paper considered the incomplete latin square design of order 6 x 6 with the two missing values to develop the mathematical formulae for the estimated parameters for full effect model. Finally, it is easy to make the ANOVA. It is noted that the mathematical formulae in this paper can be used to solve all indexes.