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

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

Systems validation: application to statistical programs

BACKGROUND: In 2003, the United States Food and Drug Administration (FDA) released a guidance document on the scope of "Part 11" enforcement. In this guidance document, the FDA indicates an expectation of a risk-based approach to determining which systems should undergo validation. Since statistical programs manage and manipulate raw data, their implementation should be critically reviewed to determine whether or not they should undergo validation. However, the concepts of validation are not often discussed in biostatistics curriculum. DISCUSSION: This paper summarizes a "Plan, Do, Say" approach to validation that can be incorporated into statistical training so that biostatisticians can understand and implement validation principles in their research. SUMMARY: Validation is a process that requires dedicated attention. The process of validation can be easily understood in the context of the scientific method

CVTresh: R Package for Level-Dependent Cross-Validation Thresholding

The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006), and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional cross-validation with a fast imputation method, so that it overcomes a limitation of data length, a power of 2. It can be easily applied to the classical leave-one-out cross-validation and K-fold cross-validation. Since the procedure is computationally fast, a level-dependent cross-validation can be developed for wavelet shrinkage of data with various sparseness according to levels.