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"Here is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition."

"Analisis regresi adalah salah satu metode yang digunakan dalam menganalisisdata. Metode yang sering digunakan untuk menaksir parameter dalam modelregresi linier adalah ordinary least square OLS. Metode OLS akan memberikantaksiran terbaik ketika semua asumsinya terpenuhi. Namun pada kenyataannya,asumsi tersebut seringkali tidak terpenuhi. Asumsi yang seringkali tidak terpenuhiadalah adanya multikolinieritas dan adanya pencilan outlier. Multikolinieritasakan membuat variansi taksiran parameter regresi menjadi sangat besar, sedangkanoutlier akan membuat taksiran parameter menjadi bias. Jika kedua pelanggaranasumsi ini terjadi pada data yang akan dianalisis digunakan robust jackknife ridgeregression. Robust jackknife ridge regression adalah regresi yang punya sifatrobust sehingga tidak terpengaruh oleh outlier dan menggunakan metode ridgeuntuk mengatasi masalah multikolinieritas serta menggunakan metode jackknifeuntuk mereduksi bias yang dihasilkan metode ridge. Metode yang digunakanuntuk mencapai sifat robust adalah MM-estimation sehingga taksiran yangdihasilkan punya breakdown point serta efficiency yang tinggi.

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Regression Analysis is one of many methods used for analyzing data. Method thatusually used for estimating parameter in linear regression model is ordinary leastsquare OLS . OLS will give best estimator when all the assumptions are met. Butin reality, sometimes not all the assumptions are met. Assumptions that usuallyviolated are multicollinearity and outlier. Multicollinearity will make variance ofthe estimated parameter become large, while outlier will make the estimatedparameter become biased. If this two violation of assumptions happened, robustjackknife ridge regression is used. Robust jackknife ridge regression is regressionthat have robust property so that it will not affected by outlier and using ridgemethod to handle multicollinearity with jackknife method to reduce biased fromridge method. Method used to achieve robust property is MM estimation so thatthe estimated parameter have high breakdown point and high efficiency."

"This book discusses the recent developments in robust optimization (RO) and information gap design theory (IGDT) methods and their application for the optimal planning and operation of electric energy systems. Chapters cover both theoretical background and applications to address common uncertainty factors such as load variation, power market price, and power generation of renewable energy sources. Case studies with real-world applications are included to help undergraduate and graduate students, researchers and engineers solve robust power and energy optimization problems and provide effective and promising solutions for the robust planning and operation of electric energy systems."

"Smartphone telah dikembangkan sebagai alat deteksi pothole oleh berbagai penelitian karena potensinya dalam memberikan manfaat pengumpulan data secara crowdsourcing tanpa memerlukan suatu infrastruktur khusus dan mahal. Namun, metode deteksi pothole berbasis smartphone memiliki tantangan dalam menghadapi berbagai ketidakpastian intrinsik dalam mengukur sinyal yang dihasilkan oleh perangkat smartphone berbeda. Ketangguhan metode dalam menghadapi ketidakpastian intrinsik tersebut diperlukan agar potensi pengumpulan data secara crowdsourcing dapat tercapai. Meskipun telah banyak penelitian yang menghasilkan kinerja deteksi yang memuaskan, berbagai macam faktor ketidakpastian masih mencegah ketangguhan penuh dari metode deteksi pothole tersebut. Penelitian menanggapi faktor-faktor ketidakpastian potensial sebagai faktor prediktor dalam mengembangkan model deteksi berbasis algoritma Random Forest dengan memanfaatan sudut Euler untuk menyelaraskan percepatan akselerometer terhadap percepatan vektor gravitasi; menerapan profil matriks untuk mengurangi kesalahan pelabelan pothole dan memberikan apriori untuk klasifikasi secara efisien; dan diskritisasi temporal pada data sensor dengan penghalusan data tersegmentasi berdasarkan jarak roda platform deteksi (Zona Deteksi). Ketangguhan metode dibuktikan dengan eksperimen faktorial bertingkat dengan variasi spesifikasi perangkat sensor, variasi rute dan tingkatan pothole, serta variasi ketersediaan sensor. Eksperimen membuktikan bahwa faktor-faktor ketidakpastian memiliki efek signifikan secara statistik, namun tidak mempengaruhi kinerja model-model yang dihasilkan. Selain tangguh, kinerja model klasifikasi yang dihasilkan menunjukkan hasil serupa atau bahkan lebih baik dari metode lain yang ada saat ini.

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Smartphones have been developed as a pothole detection tool by various studies due to their potential in providing crowdsourced data collection without the need for special and expensive infrastructure. However, a reliable smartphone-based pothole detection method is challenging to develop due to various uncertainties in measuring the signal generated by different smartphone devices. A robust method is needed to deal with said uncertainties so crowdsourced data collection potential can be achieved. Although many studies have yielded satisfactory performance, various uncertainty factors still prevent the full robustness of the existing pothole detection methods. This study endeavors to address the potential uncertainty factors as predictors in developing a pothole detection model with Random Forest algorithm. This is done by incorporating Euler angles to align the relevant sensor data to gravitational vector acceleration; matrix profile to reduce pothole labeling errors and provide a priori for efficient classification; and temporal discretization of sensor data with data segment-smoothing based on detection platform wheelbase (Detection Zone). The robustness of the proposed method is proven using multilevel factorial experiment with variations of sensor device specifications, variations in routes and levels of potholes, and variations in sensor availability. The conducted experiment proves the statistical significance of the simulated uncertainty factors does not affect the performance of the resulting models. Besides showing robustness, the performance of the resulting classification models shows promising results that are comparable to or better than other currently available smartphone-based pothole methods."