Expectation of the Ratio of the Sum of Squares to the Square of the Sum: Exact and Asymptotic Results

2002 ◽  
Vol 46 (2) ◽  
pp. 243-255 ◽  
Author(s):  
A. Fuchs ◽  
A. Joffe ◽  
J. Teugels
Keyword(s):  
Sum Of Squares ◽  
Asymptotic Results

PEMODELAN MATEMATIS ADSORPSI CO2 DENGAN STRONG BASE ANION EXCHANGE RESIN SEBAGAI ADSORBEN UNTUK PURIFIKASI BIOGAS

2015 ◽  
Vol 5 (1) ◽  
pp. 11
Author(s):  
Anies Mutiari ◽  
Wiratni Wiratni ◽  
Aswati Mindaryani
Keyword(s):  
Anion Exchange ◽  
Pilot Plant ◽  
Exchange Resin ◽  
Anion Exchange Resin ◽  
Scale Up ◽  
Sum Of Squares ◽  
Strong Base ◽  
Model Transfer ◽  
Plant Parameter

Pemurnian biogas telah banyak dilakukan untuk menghilangkan kadar CO2  dan meningkatkan kandungan CH4  yang terkandung di dalamnya. Kandungan CH4 yang tinggi akan memberikan unjuk kerja yang lebih baik. Model  matematis proses adsorpsi CO2 disusun berdasarkan teori lapisan film antar fasa, dimana pada proses yang ditinjau terdapat tiga fase yaitu gas, cair dan padat. Model matematis dari data eksperimental   kecepatan dan kesetimbangan proses adsorpsi CO2 melalui mekanisme pertukaran ion di suatu kolom adsorpsi telah dibuat. Model ini dibuat untuk mencari konstanta yang dapat dipergunakan pada proses scale up data laboratorium ke skala pilot plant. Parameter proses kecepatan yang dicari nilainya adalah koefisien transfer massa massa volumetris CO2 pada fase cair (kLa), koefisien transfer massa volumetris CO2 pada fasegas (kGa) dan tetapan laju reaksi (k1 dan k2). Pada hasil penelitian ini ditunjukkan bahwa nilai parameter yang diperoleh sesuai hasil fitting data dengan model matematis yang digunakan, yaitu model transfer massa pada lapisan film antar fase secara seri: adalah kGa, kla, k1 dan k2  dengan nilai Sum of Squares Error (SSE) rata-rata 0,0431. Perbandingan nilai kGa hasil simulasi dan teoritisnya memberikan kesalahan rata-rata 18,79%. Perbandingan nilai kLa hasil simulasi dan teoritis memberikan kesalahan rata-rata 7,92%.Kata kunci: model matematis, adsorpsi CO2, pemurnian biogas

Asymptotic for LS estimators in the EV regression model for dependent errors

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4845-4856
Author(s):  
Konrad Furmańczyk
Keyword(s):  
Regression Model ◽  
Functional Dependence ◽  
Dependent Data ◽  
Errors In Variables ◽  
Mixing Conditions ◽  
Asymptotic Results ◽  
Dependence Measure ◽  
Wide Range ◽  
Ev Regression Model ◽  
Linear And Nonlinear

We study consistency and asymptotic normality of LS estimators in the EV (errors in variables) regression model under weak dependent errors that involve a wide range of linear and nonlinear time series. In our investigations we use a functional dependence measure of Wu [16]. Our results without mixing conditions complete the known asymptotic results for independent and dependent data obtained by Miao et al. [7]-[10].

Standard Asymptotic Theory

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Variance ◽  
Statistical Significance ◽  
Regularity Conditions ◽  
Parameter Estimates ◽  
Asymptotic Results ◽  
Ml Estimation ◽  
True Parameter ◽  
Log Likelihood ◽  
Ml Estimator

This book relies on maximum likelihood (ML) estimation of parameters. Asymptotic theory assumes regularity conditions hold when the ML estimator is consistent. Typically an additional third derivative condition is assumed to ensure that the ML estimator is also asymptotically normally distributed. Standard asymptotic results that then hold are summarized in this chapter; for example, the asymptotic variance of the ML estimator is then given by the Fisher information formula, and the log-likelihood ratio, the Wald and the score statistics for testing the statistical significance of parameter estimates are all asymptotically equivalent. Also, the useful profile log-likelihood then behaves exactly as a standard log-likelihood only in a parameter space of just one dimension. Further, the model can be reparametrized to make it locally orthogonal in the neighbourhood of the true parameter value. The large exponential family of models is briefly reviewed where a unified set of regular conditions can be obtained.

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

scholarly journals On cryptographic properties of (n + 1)-bit S-boxes constructed by known n-bit S-boxes

2020 ◽  
Vol 15 (1) ◽  
pp. 258-265
Author(s):  
Yu Zhou ◽  
Daoguang Mu ◽  
Xinfeng Dong
Keyword(s):  
Sufficient Conditions ◽  
Sum Of Squares ◽  
Basic Component ◽  
Necessary And Sufficient Conditions ◽  
Autocorrelation Functions ◽  
Walsh Spectrum ◽  
Cryptographic Algorithms ◽  
Necessary And Sufficient ◽  
Relationship Of

AbstractS-box is the basic component of symmetric cryptographic algorithms, and its cryptographic properties play a key role in security of the algorithms. In this paper we give the distributions of Walsh spectrum and the distributions of autocorrelation functions for (n + 1)-bit S-boxes in [12]. We obtain the nonlinearity of (n + 1)-bit S-boxes, and one necessary and sufficient conditions of (n + 1)-bit S-boxes satisfying m-order resilient. Meanwhile, we also give one characterization of (n + 1)-bit S-boxes satisfying t-order propagation criterion. Finally, we give one relationship of the sum-of-squares indicators between an n-bit S-box S0 and the (n + 1)-bit S-box S (which is constructed by S0).

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

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