Hasil Pencarian  ::  Simpan CSV :: Kembali

"Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general than earlier methods and sufficient software has been developed to make adaptive tests easy to use for many real-world problems. Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a comprehensive approach to tests of significance and confidence intervals. It shows how to make confidence intervals shorter and how to make tests of significance more powerful by using the data itself to select the most appropriate procedure.Adaptive tests can be used for testing the slope in a simple regression, testing several slopes in a multiple linear regression, and for the analysis of covariance. The increased power is achieved without compromising the validity of the test, by using adaptive methods of weighting observations and by using permutation techniques. An adaptive approach can also be taken to construct confidence intervals and to estimate the parameters in a linear model. Adaptive confidence intervals are often narrower than those obtained from traditional methods and maintain the same coverage probabilities."

"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."

"Provides a comprehensive theory of the approximations of quantile processes in light of recent advances, as well as some of their statistical applications."

"Originally published in 1986, this valuable reference provides a detailed treatment of limit theorems and inequalities for empirical processes of real-valued random variables. It also includes applications of the theory to censored data, spacings, rank statistics, quantiles, and many functionals of empirical processes, including a treatment of bootstrap methods, and a summary of inequalities that are useful for proving limit theorems. At the end of the Errata section, the authors have supplied references to solutions for 11 of the 19 Open Questions provided in the book's original edition."

"This volume provides a complete record of presentations made at Industrial Engineering, Management Science and Applications 2015 (ICIMSA 2015), and provides the reader with a snapshot of current knowledge and state-of-the-art results in industrial engineering, management science and applications. The goal of ICIMSA is to provide an excellent international forum for researchers and practitioners from both academia and industry to share cutting-edge developments in the field, and to exchange and distribute the latest research and theories from the international community. The conference is held every year, making it an ideal platform for people to share their views and experiences in industrial engineering, management science and applications related field"

"This book is covers Markov chains in discrete and continuous time, poisson processes, renewal processes, martingales, and mathematical finance. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding. There are many new examples and problems with solutions that use the TI-83. Some material that was too advanced for the level has been eliminated while the treatment of other topics useful for applications has been expanded. In addition, the ordering of topics has been improved. For example, the difficult subject of martingales is delayed until its usefulness can be seen in the treatment of mathematical finance. "

"The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced statistical methods for the analysis of large data-sets.”"

"Fungsi hazard dapat dikategorikan menjadi dua, yaitu monoton (naik atau turun) dan non monoton (bathtub shape dan upside down bathtub shape). Untuk memodelkan datadengan fungsi hazard monoton, naik atau turun, dan non monoton bathtub shape umumnya digunakan distribusi Gamma atau Weibull. Pada skripsi ini, akan diperkenalkan sebuah distribusi yang dapat memodelkan data dengan fungsi hazard berbentuk upside down bathtub shape. Distribusi ini diturunkan dari distribusi Lindley dengan melakukan transformasi yang disebut distribusi generalized inverse Lindley. Distribusi ini lebih fleksibel dalam memodelkan data dengan fungsi hazard non-monoton upside down bathtub. Hal ini dikarenakan parameter shape pada distribusi tersebut menyebabkan fungsi hazard memiliki banyak variasi bentuk namun tetap mempertahankan bentuk upside down bathtub. Beberapa karakteristik dari distribusi seperti fungsi kepadatan peluang, fungsi distribusi, fungsi survival, fungsi hazard,dan momen ke-r akan dicari. Untuk mengestimasi parameter distribusinya akan digunakan metode maximum likelihood. Di akhir skripsi ini, akan dibangun data untuk mengestimasi parameter dari distribusi yang bersangkutan

---

Hazard rate are categorized by their shape, either its monotone (decreasing or increasing) or non-monotone (upside down bathtub shaped and bathtub shaped). Modelling data from monotone hazard rate, either decreasing or increasing, and bathtub shaped hazard rate are possible with common distribution such as Gamma distribution or Weibull distribution. For data which has upside down bathtub shaped hazard rate is usually done by using inverse transformation of exponential distribution such as inverse Gamma, inverse Weibull, and inverse Lindley. In this paper, a distribution that can model a data with upside down bathtub shaped hazard rate is introduced. The distribution is derived from Lindley distribution with transformation and is called generalized inverse Lindley distribution. The distribution is more flexible because shape parameter which make wide variety of shape without changing its hazard rate from upside down bathtub shaped. Somestatistic properties of the distribution such as density function, cumulative function, survival function, hazard function, and moment will be discussed. For estimatingparameter of the distribution, maximum likelihood method will be used. In the end, simulation data will be generated to see the estimation of the distributions parameter."