THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

计量经济分析 = Econometric analysis

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

A review of process fault detection and diagnosis: Part III: Process history based methods

Weight-of-evidence approach to identify regionally representative sites for air-quality monitoring network: Satellite data-based analysis

A Correction to this paper has been published: 10.1007/s40435-022-00955-z 

/pdf/correction-to-continuous-discrete-filtering-techniques-for-3qj3wkcc.pdf

Correction to: Continuous–discrete filtering techniques for estimating states of nonlinear differential–algebraic equations (DAEs) systems

Recursive Bayesian approaches have been widely used for state and parameter estimation. More recently, sampling based approaches have become popular since they circumvent the issue of approximating the conditional density by selecting samples (either deterministically or stochastically) and subsequently propagating these through the nonlinear dynamics. In this work, we propose an analytical framework to evaluate the aposteriori density using Bayes’ rule for nonlinear state estimation. In this approach, apriori density is modeled as a sum of Gaussians, which are centered on the propagated samples. These are used alongwith the likelihood function in the Bayes’ rule to obtain the posteriori density thereby resulting in a Nonlinear Update Based Gaussian Sum Filter (NUGSF). Our simulations show that for a scalar case problem when used with large number of samples the estimation was superior to UKF. However, with limited number of samples as in UKF, the simulation performance of NUGSF deteriorates. We used the insight obtained from the limited number of samples to identify the cause for poor performance.

Bayesian Estimation in Nonlinear Dynamic Systems using Nonlinear Update

Computation of Alarm Relevant Probabilities Using Geometric Random Variable Modeling

Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

https://www.bohrpub.com/article/BIJCEES_ 20232108.pdf

Mani Bhushan

Papers

Weight-of-evidence approach to identify regionally representative sites for air-quality monitoring network: Satellite data-based analysis

Correction to: Continuous–discrete filtering techniques for estimating states of nonlinear differential–algebraic equations (DAEs) systems

Bayesian Estimation in Nonlinear Dynamic Systems using Nonlinear Update

Computation of Alarm Relevant Probabilities Using Geometric Random Variable Modeling

Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar