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"Recursive methods offer a powerful approach for characterizing and solving complicated problems in dynamic macroeconomics. Recursive Macroeconomic Theory provides both an introduction to recursive methods and advanced material, mixing tools and sample applications. Only experience in solving practical problems fully conveys the power of the recursive approach, and the book provides many applications. This third edition offers substantial new material, with three entirely new chapters and significant revisions to others. The new content reflects recent developments in the field, further illustrating the power and pervasiveness of recursive methods. New chapters cover asset pricing empirics with possible resolutions to puzzles; analysis of credible government policy that entails state variables other than reputation; and foundations of aggregate labor supply with time averaging replacing employment lotteries. Other new material includes a multi-country analysis of taxation in a growth model, elaborations of the fiscal theory of the price level, and age externalities in a matching model. The book is suitable for both first- and second-year graduate courses in macroeconomics and monetary economics. Most chapters conclude with exercises. Many exercises and examples use Matlab programs, which are cited in a special index at the end of the book."

""Just as macroeconomic models describe the overall economy within a changing or dynamic, framework, the models themselves change over time. In this text Stephen J. Turnovsky reviews several early models as well as a representation of more recent models. They include traditional (backward-looking) models, linear rational expectations (future-looking) models, intertemporal optimization models, endogenous growth models, and continuous-time stochastic models. The author uses examples from both closed and open economies."--BOOK JACKET."

"Kematian yang disebabkan oleh kanker diperkirakan akan terus meningkat, padahal jumlah kematian ini dapat dikurangi dengan adanya deteksi dini. Salah satunya adalah dengan klasifikasi data kanker. Data kanker yang digunakan merupakan data kanker berdimensi tinggi dengan ribuan fitur, tetapi tidak semua fitur yang ada merupakan fitur yang relevan. Oleh karena itu, perlu adanya proses seleksi fitur. Untuk meningkatkan tingkat akurasi yang dihasilkan, digunakan sebuah metode seleksi fitur yang meninjau adanya korelasi antar gen, yaitu CSVM-RFE. Pada metode tersebut, data yang ada diproyeksikan dan diubah menjadi sebuah data baru dengan ekstraksi fitur, dan kemudian dilakukan proses seleksi fitur. Penggunaan dua metode tersebut pada klasifikasi tiga data kanker yang ada terbukti menghasilkan tingkat akurasi yang tinggi, pada data kanker kolon tingkat akurasi yang didapatkan adalah sebesar 96.6, pada kanker prostat sebesar 98.9, dan pada kanker lymphoma sebesar 98,6.

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The number of death caused by cancer expected to rise over two decades, whereas the number of death can be reduced by early detection. One of them is cancer classification. Cancer dataset is a high dimensional dataset that consist of thousands of features, but not all of these features are relevant. Therefore, it is necessary to remove the redundant features using feature selection. Feature selection can also improve the accuracy of classification. Many feature selection methods do not consider the correlated genes, so we need a new feature selection method that consider the correlated genes. It is CSVM RFE, in this method the existing data is projected and converted into a new data with feature extraction. These two methods are applied to the cancer datasets, and produce the accuracy of 96.6 using colon cancer dataset, 98.9 using prostate cancer dataset, and 98.6 using lymphoma cancer dataset."