Face Validation Method Alternatives for Shiphandling Fuzzy Logic Difficulty Model

The development of shiphandling difficulty model for ferry is based on the empirical experience through the Master of Ro-Ro ferries. The SHDMF is consisted from two parts which are the Analytic Hierarchy Process (AHP) and Fuzzy Inference System. Both parts had been validated through internal validation in the form of consistency test for the first part and robustness test for the second part. Further, the external/face validation is required to compare the proposed model with similar model through benchmarking approach. The benchmarking approaches are elaborated for the reliability, validity, possibility, efficiency and effectiveness. Through fuzzy group decision making method, the questionnaire survey is performed to verify the most appropriate approach based on the shiphandling simulator as the most preferred benchmarking tool by experts. Next, the proposed scenario is overviewed and discussed especially related to the advantages and drawbacks of shiphandling simulator. Keywords: shiphandling difficulty, fuzzy group decision making, internal validation Model pengukuran kesulitan pengendalian feri didasarkan pada pengalaman empiris melalui pernyataan nahkoda kapal feri Ro-Ro. SHDMF terdiri atas dua bagian, yaitu Analytic Hierarchy Process dan Fuzzy Inference System. Kedua bagian ini telah divalidasi melalui validasi internal dalam bentuk uji konsistensi untuk bagian pertama dan uji kehandalan untuk bagian kedua. Selanjutnya validasi atau wajah eksternal diperlukan untuk membandingkan model yang diusulkan dengan model yang diperoleh dari benchmarking. Pendekatan benchmarking dijabarkan untuk kehandalan, validitas, kemungkinan, efisiensi, dan efektivitas. Melalui metode fuzzy kelompok pembuatan keputusan, survei kuesioner dilakukan untuk memverifikasi pendekatan yang paling tepat dengan simulator pengendalian kapal sebagai alat yang paling disukai oleh para ahli untuk benchmarking. Selanjutnya skenario yang ditinjau-ulang dan dibahas terutama terkait dengan keuntungan dan kelemahan simulator pengendalian kapal. Kata

Validation of community pharmacy : confirming the effectiveness of a pharmacist in a community setting

Validation of community pharmacy is a concept we coined to define the process carried out to demonstrate that the services provided by a community pharmacist are needed by the community. One method to undertake such a process was developed. This method involves the use of a series of Validation Tools which are measurement tools with which to monitor the standards of the service provided by a community pharmacist. Five Validation Tools were developed and subsequently the validity and reliability of these tools were tested. The developed Validation Tools were found to be valid and reliable methods which can be confidently used by community pharmacists to confirm the impact of their services on patient care.peer-reviewe

Fast Cross-Validation via Sequential Testing

With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses nonparametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of the full cross-validation. Theoretical considerations underline the statistical power of our procedure. The experimental evaluation shows that our method reduces the computation time by a factor of up to 120 compared to a full cross-validation with a negligible impact on the accuracy

Cross-validation in nonparametric regression with outliers

A popular data-driven method for choosing the bandwidth in standard kernel regression is cross-validation. Even when there are outliers in the data, robust kernel regression can be used to estimate the unknown regression curve [Robust and Nonlinear Time Series Analysis. Lecture Notes in Statist. (1984) 26 163--184]. However, under these circumstances standard cross-validation is no longer a satisfactory bandwidth selector because it is unduly influenced by extreme prediction errors caused by the existence of these outliers. A more robust method proposed here is a cross-validation method that discounts the extreme prediction errors. In large samples the robust method chooses consistent bandwidths, and the consistency of the method is practically independent of the form in which extreme prediction errors are discounted. Additionally, evaluation of the method's finite sample behavior in a simulation demonstrates that the proposed method performs favorably. This method can also be applied to other problems, for example, model selection, that require cross-validation.Comment: Published at http://dx.doi.org/10.1214/009053605000000499 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org