Get A bias analysis of Weibull models under heaped data PDF

By Augustin T., Wolff J.

Retrospectively gathered length information are usually stated incorrectly. a huge form of such an errors is heaping - respondents are inclined to round-off or round-up the knowledge in response to a few rule of thumb. for 2 designated situations of the Weibull version we research the behaviour of the 'naive estimators', which easily forget about the dimension errors because of heaping, and derive closed expressions for the asymptotic bias. those effects provide a proper justification of empirical proof and simulation-based findings said within the literature. also, events the place a awesome bias needs to be anticipated may be pointed out, and an actual bias correction could be played.

Show description

Read or Download A bias analysis of Weibull models under heaped data PDF

Similar organization and data processing books

Get Grid Computing: A Practical Guide To Technology and PDF

As a rule conversing, grid computing seeks to unify geographically dispersed computing structures to create one huge, robust procedure. over the last twenty years, grid computing has had a comparatively small impression on company productiveness, as a result of the tremendous funding it required to installation and keep it.

Download e-book for iPad: Understanding Delta Sigma Data Converters by Shanthi Pavan

This re-creation introduces operation and layout thoughts for Sigma-Delta converters in actual and conceptual phrases, and comprises chapters which discover advancements within the box over the past decade contains info on MASH architectures, digital-to-analog converter (DAC) mismatch and mismatch shaping Investigates new subject matters together with continuous-time ΔΣ analog-to-digital converters (ADCs) rules and designs, circuit layout for either continuous-time and discrete-time ΔΣ ADCs, decimation and interpolation filters, and incremental ADCs offers emphasis on sensible layout matters for execs

Get Data Conversion: Calculating the Monetary Benefits PDF

This booklet tackles the 3rd significant problem and the second one such a lot tricky step within the ROI method: changing info to financial values. while a specific undertaking or application is hooked up to a enterprise degree, the subsequent logical query is: what's the financial worth of that influence? For ROI research, it's at this severe aspect the place the financial merits are constructed to check to the prices of this system to calculate the ROI.

Extra info for A bias analysis of Weibull models under heaped data

Sample text

An } and B = {b1 , b2 . . bn } are sets of lower and upper bounds of the integral, and W = {w1 , w2 . . wn } is the set of the weights, with bj > aj and wj = 0 for 1 ≤ j ≤ n. It can be clearly seen that the input space can be regarded as a hyper-rectangle bounded by A and B in n-dimensional space From a geometric point of view, the hyperplane P can be regarded as a dividing plane. It divides the n-dimensional space into three parts: the points on P, the points below P, and the points above P.

A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Chapter 4 Sensitivity Analysis with Parameterized Activation Function Among all the traditional methods introduced in Chap. 2, none has involved activation function in the calculation of sensitivity analysis. This chapter attempts to generalize Piché’s method by parameterizing antisymmetric squashing activation functions, through which a universal expression of MLP’s sensitivity will be derived without any restriction on input or output perturbations.

Initialization. Form the matrix H in Eq. 7) with the RBF function responses of all the training examples. STEP 2. First critical vector neuron selection. Calculate the sensitivity of each column of H with Eq. 5). The column that provides the maximum sensitivity is selected as the first column of matrix Q(1) . Calculate the classification error Err(1) with the selected RBF center. Let K= 2. STEP 3. Orthogonalization and critical vector selection. Orthogonalize all remaining columns of H with all the columns of Q(K−1) using Eq.

Download PDF sample

A bias analysis of Weibull models under heaped data by Augustin T., Wolff J.

by William

Rated 4.96 of 5 – based on 26 votes

Author: admin