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"Berdasarkan Social Accounting Matrix Indonesia 2015 dan metode micro-simulation, penelitian ini menunjukkan bagaimana pertumbuhan sektoral mempengaruhi ketimpangan pendapatan dan kemiskinan di Indonesia, serta peran Usaha Kecil Menengah (UKM) sektor manufaktur terhadap penurunan ketimpangan pendapatan. Dari 24 sektor, hanya pertumbuhan di 10 sektor saja akan menurunkan ketimpangan pendapatan, dengan penurunan terbesar di sektor pertanian tanaman lainnya. UKM tidak memiliki peran khusus terhadap penurunan ketimpangan pendapatan di Indonesia, yang berpengaruh adalah sektor dari UKM tersebut. Diketahui juga bahwa semua pertumbuhan sektor ekonomi akan menurunkan kemiskinan dengan kontribusi yang berbeda-beda. Pertumbuhan di sektor pertanian tanaman lainnya memiliki kontribusi terbesar dalam pengentasan kemiskinan di Indonesia.

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Based on extended Indonesia’s Social Accounting Matrix (SAM) 2015 and using micro-simulation method, this study shows how sectoral growth affects income inequality and poverty in Indonesia, also the contribution of Small and Medium Enterprises (SMEs) manufacturing sector in reducing income inequality. Of the 24 sectors in Indonesia, only growth in 10 sectors can reduce income inequality, with the largest reduction is growth in other corp agricultural sector. SMEs have no special effect in reducing income inequality. The impact depends on which sector the SME is in. This study also shows that growth in all economic sectors have an impact on poverty alleviation in different contributions. Growth in other crop agriculture sector had the largest contribution to poverty alleviation in Indonesia."

"Bank dipersyaratkan oleh pemerintah untuk mengelola risiko-risiko perbankan, salah satunya adalah risiko operasional. Risiko operasional merupakan salah satu jenis risiko yang melekat pada setiap aktifitas fungsional bank. Bank mengelola risiko operasional dengan cara menghitung kerugian yang diperkirakan sebagai kebutuhan modal bagi risiko operasional (Economic Capital). Loss distribution Approach (LDA) merupakan salah satu metode untuk perhitungan Economic Capital (EC). Dalam metode LDA, bank harus mengestimasi loss severity distribution (distribusi severitas) dan frequency distribution (distribusi frekuensi) kemudian membentuk compound distribution (distribusi majemuk). Nilai EC diperoleh dari Value at Risk (VaR) dari distribusi majemuk dengan tingkat kepercayaan 99,9%. Estimasi distribusi severitas umumnya menggunakan model distribusi tertentu yang telah ada, namun pada banyak kasus kurang baik dalam menggambarkan data. Estimasi distribusi severitas berbasis data diharapkan mampu menjadi solusi permasalahan ini. Salah satu metode yang mengestimasi distribusi severitas dengan berbasis pada data adalah Gaussian Mixture Model (GMM). GMM merupakan suatu metode parametrik untuk mengestimasi fungsi distribusi probabilitas dari suatu variabel acak. Model GMM ini merupakan model kombinasi linear sederhana dari beberapa komponen distribusi Gaussian. Hasil penelitian menunjukkan bahwa GMM mampu menggambarkan data lebih baik dibandingkan dengan menggunakan model distribusi yang ada. Nilai EC yang dihasilkan oleh metode LDA yang distribusi severitasnya menggunakan GMM lebih kecil 2-2,8% dibandingkan nilai EC yang dihasilkan oleh metode LDA yang menggunakan model distribusi tersebut.

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Bank must be able to manage all of banking risk, on of them is operational risk. Operational risk is a risk that come from any functional activity of bank. Bank manage operational risk by calculate estimated risk (Economic Capital). Loss Distribution Approach (LDA) is a popular method to estimate Economic Capital (EC) of operational risk on banking. In LDA method, loss severity distribution (severity distribution) and loss frequency distribution (frequency distribution) must be estimated and then compound distribution is formed from both of them. Through LDA method, the value of EC can be gotten from Value at Risk (VaR) in compound distribution with the level of confidence reaches 99,9%. Severity distribution estimation that used a model on particular distribution cannot describe a data well through. So, Severity distribution estimation based on data is used to solved this problem. One of methode that estimated severity distribution based on data is Gaussian Mixture Model (GMM). GMM is parametric methode that estimate probability density of random variable. Model of GMM is a linear combination of many Gaussian distribution . The result on this research is estimation of severity distribution through GMM is better than existing distribution model in describing the data. The value at EC of LDA method using GMM is smaller 2 % - 2,8 % than the value at EC of LDA using existing distribution model."

"This textbook provides an introduction to the field of mathematical biology through the integration of classical applications in ecology with more recent applications to epidemiology, particularly in the context of spread of infectious diseases. "

""This graduate level text on economic growth surveys neoclassical and more recent growth theories, stressing their empirical implications and the relation of theory to data and evidence. The authors have undertaken a major revision for the long-awaited second edition of this widely used text, the first modern textbook devoted to growth theory. The book has been expanded in many areas and incorporates the latest research."--BOOK JACKET."

"Interest in the temporal fluctuations of biological populations can be traced to the dawn of civilization. How can mathematics be used to gain an understanding of population dynamics? This monograph introduces the theory of structured population dynamics and its applications, focusing on the asymptotic dynamics of deterministic models. This theory bridges the gap between the characteristics of individual organisms in a population and the dynamics of the total population as a whole. In this monograph, many applications that illustrate both the theory and a wide variety of biological issues are given, along with an interdisciplinary case study that illustrates the connection of models with the data and the experimental documentation of model predictions. The author also discusses the use of discrete and continuous models and presents a general modeling theory for structured population dynamics. Cushing begins with an obvious point: individuals in biological populations differ with regard to their physical and behavioral characteristics and therefore in the way they interact with their environment. Studying this point effectively requires the use of structured models. Specific examples cited throughout support the valuable use of structured models. Included among these are important applications chosen to illustrate both the mathematical theories and biological problems that have received attention in recent literature."