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"The multiple signal demixing and parameter estimation problems that result from the impacts of background noise and interference are issues that are frequently encountered in the fields of radar, sonar, communications, and navigation. Research in the signal processing and control fields has always focused on improving the estimation performance of parameter estimation methods at low SNR and maintaining the robustness of estimations in the presence of model errors. This book presents a universal and robust relaxation estimation method (RELAX), and introduces its basic principles and applications in the fields of classical line spectrum estimation, time of delay estimation, DOA estimation, and radar target imaging. This information is explained comprehensively and in great detail, and uses metaphors pertaining to romantic relationships to visualize the basic problems of parameter estimation, the basic principles of the five types of classical parameter estimation methods, and the relationships between these principles. The book serves as a reference for scientists and technologists in the fields of signal processing and control, while also providing relevant information for graduate students in the related fields."

"The book then presents its core material, which is a more robust maximum principle for both deterministic and stochastic systems. The results obtained have applications in production planning, reinsurance-dividend management, multi-model sliding mode control, and multi-model differential games. Using powerful new tools in optimal control theory, this book explores material that will be of great interest to post-graduate students, researchers, and practitioners in applied mathematics and engineering, particularly in the area of systems and control."

"ABSTRAKItem response theory atau yang sering disingkat sebagai IRT memberikan estimasi kemampuan peserta yang lebih tepat jika dibandingkan dengan classical test theory. Estimasi yang dihasilkan pada IRT bergantung pada ketepatan model yang digunakan. Pemilihan model IRT dapat dilakukan setelah didapatkan hasil confirmatory factor analysis dengan melihat nilai model fit. Model dengan nilai good fit yang lebih baik akan menjadi model yangterpilih. Pemilihan model fit dengan langkah ini disebut sebagai pemilihan model melalui data empirik. Pemilihan model dan struktur dapat dibantu dengan melihat nature dari sebuah tes. Seperti pada tes seleksi dengan bentuk pilihan maka model IRT yang tepat digunakan untuk mengestimasi adalah model 3 parameter logistik. Kesalahan dalam memilih struktur dan model IRT terkadang tidak dapat dihindari karena ketidaktahuan peneliti. Diantara estimator yang ada dalam IRT terdapat satu estimator yang dikenal memiliki robust standar error atau dapat menghasilkan standar eror yang kecil jikadigunakan pada model IRT yang tidak tepat. Estimator ini dinamakan maximum likelihood with robust standard errors. Memperkecil standar eror berarti memperkecil ketidakakuratan estimasi yang disebabkan oleh kesalahan pemilihan model. Keakuratan MLR akan disandingkan dengan maximum likelihood estimator dalam mengestimasi. MLE dikenal dengan propertinya yang asimptotik jika digunakan pada sampel besar. Hasil yang didapatmemperlihatkan bahwa MLR dapat menghasilkan akurasi yang lebih baik pada model dengan sampel kecil sedangkan pada sampel besar MLE dan MLR memberikan hasil yang tidak berbeda.

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ABSTRACTItem response theory gives more acurrate estimates of latent trait compared to classical test theory. These estimates are independent to any sample and test. But the result of estimates are depend on which model is used. That is why the selection of model in IRT is very important. The wrong model will cause the estimates inflate or underrated. Before a data can be calculated with IRT model we need to check the appropriate model and structure first. To know what structure will be used we first check the data using confirmatory factor analysis. The result will show which structure fits the data more, is it first order or second order data. To select the IRT model we do a fit of model testing. This is a trial and error step. Usually in fit model testing we propose more than one model to be tested. As not all models can be included for being tested, there are the chance for using a wrong model. Using a wrong structure and model sometimes can not be helped. In IRT there are estimator named maximum likelihood with robust standard error which is specialized to estimateparameters when the model is wrong. This can be done because of MLR is using Huber Sandwich method as estimator. In this research MLR is being compared to MLE to estimate a second order data which is treated as first order data. The error is being accompanied with IRT model variations (1-PL, 2-PL, and 3-PL) and three samples variations (350, 500, and 2000). As 2 x 3 x 3 combination models, we will have 18 models in result. The results showing that MLR produces smaller standard. But MLE is quite good too when the samplebeing used is as big as 2000"