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"Pada skripsi ini akan dibahas mengenai distribusi Weibull power series WPS. Distribusi WPS merupakan salah satu jenis kelas distribusi probabilitas yang diperoleh dengan melakukan compounding antara distribusi Weibull dan distribusi power series yang terpancung di nol Distribusi power series yang terpancung di nol adalah kelas distribusi dari variabel random diskrit terpancung di nol yang dinyatakan dalam bentuk power series. Prosedur compounding yang digunakan adalah prosedur compounding yang diperkenalkan oleh Adamidis dan Loukas 1998. Beberapa karakteristik penting dari distribusi WPS adalah fungsi distribusi pdf fungsi survival fungsi hazard momen ke r statistik terurut dan estimasi parameter Estimasi parameter dilakukan dengan menggunakan metode maksimum likelihood. Selanjutnya dibahas bentuk khusus dari distribusi WPS diantaranya adalah distribusi Webull Poisson dan Weibull logaritmik. Pada akhirnya data mengenai kekuatan dari gelas fibres berketebalan cm akan digunakan sebagai ilustrasi.

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In this skripsi Weibull power series distributions WPS will be studied WPS distributions are one kind of probability distribution which is obtained by compounding Weibull distribution and zero truncated power series distributions. Power series form of discrete random variabel distribution is an element of power series distributions class Compounding procedure that is introduced by Adamidis and Loukas 1998 will be applied in this skripsi. Firstly some important characteristics of WPS distribution that will be considered are distribution function pdf survival function hazard function r th moment ordered statistic and parameter estimation by maximum likelihood. Then Weibull Poisson and Weibull logarithmic are some special distribution of WPS distributions that will also be studied Finally a set of data about strength of 1 5 cm glass fibres will used as ilustration."

"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."

"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."