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A Parameter Dimension-Split Based Asymptotic regression Estimation Theory for a Multinomial Panel Data Model

SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY 2018年第2期80卷 301-329页

作者： Sutradhar, Brajendra C. Carleton Univ Ottawa ON Canada Mem Univ St John NF Canada

In this paper we revisit the so-called non-stationary regression models for repeated categorical/multinomial data collected from a large number of independent individuals. The main objective of the study is to obtain consistent and efficient regression estimates after taking the correlations of the repeated-multinomial data into account. The existing (1) 'working' odds ratios based GEE (generalized estimating equations) approach has both consistency and efficiency drawbacks. Specifically, the GEE-based regression estimates can be inconsistent which is a serious limitation. Some other existing studies use a MDL (multinomial dynamic logits) model among the repeated responses. As far as the estimation of the regression effects and dynamic dependence (i.e., correlation) parameters is concerned, they use either (2) a marginal or (3) a joint likelihood approach. In the marginal approach, the regression parameters are estimated for known correlation parameters by solving their respective marginal likelihood estimating equations, and similarly the correlation parameters are estimated by solving their likelihood equations for known regression estimates. Thus, this marginal approach is an iterative approach which may not provide quick convergence. In the joint likelihood approach, the regression and correlation parameters are estimated simultaneously by searching the maximum value of the likelihood function with regard to these parameters together. This approach may encounter computational drawback, specially when the number of correlation parameters gets large. In this paper, we propose a new estimation approach where the likelihood function for the regression parameters is developed from the joint likelihood function by replacing the correlation parameter with a consistent estimator involving unknown regression parameters. Thus the new approach relaxes the dimension issue, that is, the dimension of the correlation parameters does not affect the estimation of the main regres

关键词： Asymptotic properties Dynamic dependence parameters Lag 1 transitional multinomial probabilities Partially estimated likelihood function Product multinomial for independent groups regression parameters Time-dependent covariates

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Density functional efficiency in the calculations of vibrational frequencies and molecular structures of β-diketones

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JOURNAL OF STRUCTURAL CHEMISTRY 2016年第1期57卷 65-75页

作者： Azizi-Toupkanloo, H. Tayyari, S. F. Ferdowsi Univ Mashhad Dept Chem Mashhad Iran

Density functional theory (DFT) levels are employed to calculate the vibrational frequencies and geometrical data of beta-diketones. We evaluate the relative performance of the different levels by comparing theoretical results to experimental values. The applied DFT levels in this work are B3LYP, BLYP, B3P86, B3PW91, BPW91, G96LYP, BP86, and G96PW91 with the standard 6-31G, 6-31G*, 6-31G**, 6-31+G**, 6-31++G**, 6-311G**, 6-311++G** basis sets. The best results are obtained at the B3LYP, B3PW91, and B3P86 levels.

关键词： density functional theory vibrational frequencies geometrical parameters regression parameters

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Remarks on asymptotic efficient estimation for regression effects in stationary and nonstationary models for panel count data

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BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS 2014年第2期28卷 241-254页

作者： Sutradhar, Brajendra C. Jowaheer, Vandna Rao, R. Prabhakar Mem Univ Newfoundland Dept Math & Stat St John NF A1C 5S7 Canada Univ Mauritius Dept Math Reduit Mauritius Sri Sathya Sai Univ Dept Econ Prasanthinilayam 515134 Andhra Pradesh India

In a panel count data setup, repeated counts of an individual are assumed to be influenced by the individual's random effect. Consequently, conditional on the random effect, the repeated responses of the individual are assumed to be serially correlated. Under the assumption that the random effects of the individuals follow a normal distribution, Jowaheer and Sutradhar (Statist. Probab. Letters 79 (2009) 1928-1934) have demonstrated that the generalized quasi-likelihood (GQL) estimation approach produces more efficient estimates than the so-called generalized method of moments (GMM) approach for both regression effects and the variance component of the normal random effects. For the cases where the distribution of the random effects is unknown, there exist two estimation approaches, namely the conditional maximum likelihood (CML) and instrumental variables based GMM (IVGMM) approaches, for the estimation of the regression effects. The purpose of this paper is to examine the asymptotic efficiency performances of the CML and IVGMM approaches as compared to the GQL approach for the regression estimation. When the covariates are stationary, that is, time independent, it is, however, known that the CML and IVGMM approaches are useless for the regression estimation, whereas the GQL approach does not encounter any such limitations. For the general case, that is, when the covariates are time dependent, the IVGMM approach appears to be computationally expensive and hence it is not included in efficiency comparison. Between the CML and GQL approaches, it is found through exact asymptotic variance calculations that the GQL approach is asymptotically more efficient than the CIVIL approach in estimating the regression effects. This makes the GQL as a unified efficient approach irrespective of the cases whether the panel count data are stationary or nonstationary.

关键词： Conditional autocorrelations consistency and efficiency generalized quasi-likelihood estimating equations nonstationary counts random effects causing overdispersion regression parameters

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Urban Traffic Flow Prediction: A MapReduce Based Parallel Multivariate Linear regression Approach

Urban Traffic Flow Prediction: A MapReduce Based Parallel Mu...

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IEEE International Conference on Intelligent Transportation Systems

作者： Liang Dai Wen Qin Hongke Xu Ting Chen Chao Qian Sch. of Electron. & Control Eng. Chang'an Univ. Xian China

ISBN: (纸本)9781479960798

Urban traffic flow has the property of complexity, uncertainty, and time-varying, which bring large difficulty to real-time and accurately forecast the traffic flow for traffic control and route guidance. In this paper, according to the characteristics of existing traffic flow prediction model for long processing time and memory constraints, a parallel multivariate linear regression model was designed based on MapReduce to real-time predict traffic flow. The model is composed of three MapReduce process to estimate the regression parameters. Furthermore, we design and implement a series of experiments to verify the effectiveness of the proposed parallel multivariate linear regression model through empirical research. Experimental results show that the multivariate linear regression prediction model based on MapReduce has better performance in both speedup and scaleup, and suit for analysis and prediction of large-scale multi dimensional and time-series traffic data.

关键词： control engineering computing parallel processing regression analysis road traffic control time series traffic engineering computing MapReduce based parallel multivariate linear regression approach large-scale multidimensional traffic data memory constraints real-time traffic flow prediction regression parameters route guidance time-series traffic data traffic control traffic flow forecast urban traffic flow prediction Computational modeling Data models Forecasting Intelligent transportation systems Linear regression Mathematical model Predictive models

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Rain attenuation predictions on terrestrial radio links: differential equations approach

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2012年第3期23卷 293-301页

作者： Abdulrahman, A. Y. Rahman, T. A. Rahim, S. K. A. Islam, Md Rafiqul Abdulrahman, M. K. A. Univ Teknologi Malaysia Wireless Commun Ctr Skudai Johor Malaysia Univ Ilorin Ilorin Kwara Nigeria Univ Teknologi Malaysia Dept Math Fac Sci Skudai Johor Malaysia Islamic Int Univ Malaysia Kuala Lumpur Selangor Malaysia

The results of rain attenuation on terrestrial microwave links in a tropical climate has been reported in this paper. The results are presented in the form of rate of change of attenuation with respect to rain rate, denoted by S(R-%p). This is in turn used for predicting the expected rain attenuation at any %p of the time rain rate is exceeded in any tropical location. The predictions of the proposed model have been validated using the data collected in six locations in Malaysia and experimental results reported in other tropical locations that have a similar rainfall regime. The Malaysian data consist of 1-year measured rain attenuation over six DIGI MINI-LINKs operating at 15 GHz, and rainfall rates measured with both 1-minute and 1-hour integration times at the respective locations. The validity of the proposed model is further validated by comparing its estimates with the method of ITU-R Radiocommunication Sector of ITU and two classical rain attenuation prediction models particularly developed for tropical regions. The test results have shown that the proposed method seems to be more accurate than the proposed method seems to be more accurate than ITU-R and the other two prediction models in Malaysia. The method could be used as an alternative approach for predicting rain attenuation over any terrestrial microwave links in Malaysia and similar tropical climates. Copyright (C) 2012 John Wiley & Sons, Ltd.

关键词： exponential law total differential rain attenuation regression parameters tropical climates

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Comparison of measured rain attenuation and ITU-R predictions on experimental microwave links in Malaysia

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2011年第4期3卷 477-483页

作者： Abdulrahman, Amuda Yusuf Bin Abdulrahman, Tharek Bin Abdulrahim, Sharul Kamal Kesavan, Ulaganathen UTM Fac Elect Engn WCC Skudai 81310 Johor Malaysia

This paper presents the results of direct rain attenuation measurements carried out on four experimental microwave links, installed at UTM, Malaysia. The links operate at frequencies of 15, 22, 26, and 38 GHz and the cumulative distribution function for different rain rates have been generated from the measured 4-year rain gauge data. The experimentally measured attenuation data have been compared with International Telecommunication Union-R rain attenuation predictions;and it has been found that the latter have underestimated the measured values, especially at higher rain rates. The deviations have been modeled as a function of rain rate exceedances R-%p. It is hoped that the study will provide useful information for estimation of rainfall attenuation on microwave links in tropical regions that have similar situation to Malaysia.

关键词： Prediction Errors Rain Attenuation regression parameters Wet Antenna Losses

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regression Model of the Hole Drilling Principle for the Stress State Identification

Regression Model of the Hole Drilling Principle for the Stre...

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49th International Scientific on Experimental Stress Analysis

作者： Vitek, Karel Czech Tech Univ Fac Mech Engn Dept Mech Biomech & Mechatron Tech 4 Prague 16607 6 Czech Republic

ISBN: (纸本)9788021442757

The idea of the presented numerical simulation technique corresponds to the E 837 standard concepts but is more universal. It transforms the strains, arising during the hole drilling experiment, in a way similar to that of the E 837 standard but, unlike the E 837 standard, it executes the transformation completely. This theory enlargement enables the drilling method to be applied for a wider spectrum of further measuring appliances. Moreover, the hole drilling process do not have to be extremely precise, which the whole procedure simplifies, since the new method principle includes an objective stress state identification, when evaluating drilling experiments, with respect to the drilled hole eccentricity. The identification method of the regression parameters of the hole drilling principle are derived in this paper.

关键词： Stress state identification Hole drilling method regression parameters

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Analysis of parameter variations in L-Q equations for river runoff processes from the viewpoint of spatial and temporal conditions

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WATER SCIENCE AND TECHNOLOGY 2006年第10期53卷 141-152页

作者： Fujii, S. Shivakoti, B. R. Shichi, K. Songprasert, P. Ihara, H. Moriya, M. Kitpati, S. Tanaka, S. Kyoto Univ Grad Sch Engn Res Ctr Environm Qual Management Otsu Shiga 5200811 Japan Natl Agr Res Ctr Dept Soils & Fertilizers Tsukuba Ibaraki 3058666 Japan

This study aims to find out variation characteristics of the parameters of 'a' and 'b' in L = a(.)Q(b), an empirical equation for run-off loading (L) and flow rate (0), by evaluating the effects of flow conditions and regional properties of the watersheds on the values. We selected the Kamo River basin (155 km(2)) as a study field, and conducted various kinds of investigations, such as 80 day high frequency observations, continuous monitoring for more than 2 years, storm event surveys, and simultaneous surveys of 39 stations. Then, we obtained 7-170 data in each of 39 sampling stations. The main results obtained are as follows: 1) L-Q equation with a range of 'a' can express L-Q relation in most of the WQIs (water quality indices);2) V receives temporal (flow condition) effects more in SS, VSS and Al, while it receives regional effect more in inorganic carbon, TN, Ca and Fe;3) both of flow change in storm events, and base flow levels affect the L-Q relation, and their effects can classify the WQIs into several groups;4) the effects of regional properties were obviously observed in 'a', and quantitatively evaluated, especially for density of population.

关键词： flow conditions L-Q equation regression parameters regional properties small watershed

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Multilevel regression modeling of nonlinear processes: Derivation and applications to climatic variability

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JOURNAL OF CLIMATE 2005年第21期18卷 4404-4424页

作者： Kravtsov, S Kondrashov, D Ghil, M Univ Calif Los Angeles Dept Atmospher & Ocean Sci Los Angeles CA 90095 USA Univ Calif Los Angeles Inst Geophys & Planetary Phys Los Angeles CA 90095 USA

Predictive models are constructed to best describe an observed field's statistics within a given class of nonlinear dynamics driven by a spatially coherent noise that is white in time. For linear dynamics, such inverse stochastic models are obtained by multiple linear regression (MLR). Nonlinear dynamics, when more appropriate, is accommodated by applying multiple polynomial regression (MPR) instead;the resulting model uses polynomial predictors, but the dependence on the regression parameters is linear in both MPR and MLR. The basic concepts are illustrated using the Lorenz convection model, the classical double-well problem, and a three-well problem in two space dimensions. Given a data sample that is long enough, MPR successfully reconstructs the model coefficients in the former two cases, while the resulting inverse model captures the three-regime structure of the system's probability density function (PDF) in the latter case. A novel multilevel generalization of the classic regression procedure is introduced next. In this generalization, the residual stochastic forcing at a given level is subsequently modeled as a function of variables at this level and all the preceding ones. The number of levels is determined so that the lag-0 covariance of the residual forcing converges to a constant matrix, while its lag-1 covariance vanishes. This method has been applied to the output of a three-layer, quasigeostrophic model and to the analysis of Northern Hemisphere wintertime geopotential height anomalies. In both cases, the inverse model simulations reproduce well the multiregime structure of the PDF constructed in the subspace spanned by the dataset's leading empirical orthogonal functions, as well as the detailed spectrum of the dataset's temporal evolution. These encouraging results are interpreted in terms of the modeled low-frequency flow's feedback on the statistics of the subgrid-scale processes.

关键词： .Multiple linear regression enough spatially Los Angeles stochastic forcing Atmospheric and Oceanic inverse model polynomial Stochastic Models Multilevel MLR nonlinear dynamics white in time MPR regression parameters double-well

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On pseudo-likelihood inference in the binary longitudinal mixed model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2002年第3期31卷 397-417页

作者： Sutradhar, BC Sinha, SK Mem Univ Newfoundland St Johns NF A1C 5S7 Canada Univ Alberta Edmonton AB T6G 2G1 Canada

Binary logistic and Poisson mixed models are used to analyse over/under-dispersed proportion and count data, respectively. It is, however, well known that a full likelihood analysis for such mixed models is hampered by the need for numerical integrations. To overcome such integration problems, recently Sutradhar and Qu (On Approximate Likelihood Inference in Poisson Mixed Model. The Canadian Journal of Statistics 1998, 26, 169-186) has introduced a small variance component (for random effects) based likelihood approximation (LA) approach to estimate the parameters of the Poisson mixed models and have shown that their LA approach performs better as compared to other leading approaches. More recently, Sutradhar and Das (A Higher-Order Approximation to the Likelihood Inference in the Poisson Mixed Model. Statistics and Probability Letters 2001, 52, 59-67) further improved the LA approach of Sutradhar and Qu to accommodate larger values of the variance component. These likelihood approximation techniques developed for Poisson mixed models are however not applicable to the binary mixed models. In this paper, we propose a multivariate binary distribution based pseudo-likelihood approach for the estimation of the parameters of the binary mixed models. We, in fact, do this in a wider binary longitudinal mixed model set up, binary mixed model being a special case. More specifically, two types of binary longitudinal mixed models are considered. Under the first model, conditional on certain independent random effects, repeated binary responses are assumed to follow a Bahadur type multivariate binary distribution, so that, unconditionally, the responses in the cluster follow a longitudinal binary mixed model. Under the second model, however, the binary responses in the cluster are assumed to be conditionally independent, conditional on certain correlated random effects, so that, unconditionally, responses in the cluster also follow a binary longitudinal mixed model. It is of pr

关键词： binary mixed model consistency efficiency regression parameters repeated measures data variance component of the random effects

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