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Applications of Rasch modeling in chemometrics: binary data analysis and analytical platform selection

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 245卷

作者： Carnoli Jr, Andrea Lohuis, Petra oude Buydens, Lutgarde M. C. Jansen, Jeroen J. Tinnevelt, Gerjen H. Radboud Univ Nijmegen Inst Mol & Mat IMM Heyendaalseweg 135 NL-6525 AJ Nijmegen Netherlands Teijin Aramid Tivolilaan 50 NL-6824 BV Arnhem Netherlands

Often researchers make use of Principal Component analysis and Partial Least Squares Regression, an unsupervised and a supervised method, respectively, to extract the chemical information in the shape of one or more latent variables. However, when the research question is qualitative and requires a figure of merit, these two models will primarily focus on the quantitative and continuous information present in the data. In these cases, a valid approach may be to dichotomize the data and to analyze the resulting non-linear data via Non-Linear Principal Component analysis. However, the results of the method are not always easy to interpret due to the possible multidimensionality of the solution. Here we introduce an alternative framework, composed of Rasch modeling and Generalized Linear Mixed Effect Models, to extract information from multivariate binary chemical data with an underlying design of experiment. The model obtained by this framework provides information in a unidimensional representation that can be easily translated into one-dimensional action and control. Furthermore, we show that, through Generalized Linear Mixed Effect Models, it is possible to extend the Rasch model to its multilevel form, which enables the consideration of each random factor possibly present.

关键词： Generalized linear-mixed effects models Rasch modeling Chemometrics Design of experiment binary data analysis Chemical analytical platform selection

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Nonparametric Maximum Likelihood Methods for binary Response Models With Random Coefficients

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2022年第538期117卷 732-751页

作者： Gu, Jiaying Koenker, Roger Univ Toronto Dept Econ Toronto ON Canada UCL Dept Econ London WC1H 0AX England

The venerable method of maximum likelihood has found numerous recent applications innonparametricestimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients. However, computational difficulties have hindered its application. Combining recent developments in computational geometry and convex optimization, we develop a new approach to computation for such models that dramatically increases their computational tractability. Consistency of the method is established for an expanded profile likelihood formulation. The methods are evaluated in simulation experiments, compared to the deconvolution methods of Gautier and Kitamura and illustrated in an application to modal choice for journey-to-work data in the Washington DC *** this article are available online.

关键词： binary data analysis Convex optimization Empirical Bayes Hyperplane arrangements

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STATISTICAL PATTERN-CLASSIFICATION WITH binary VARIABLES

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IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 1981年第2期3卷 155-163页

作者： YOUNG, TY LIU, PS RONDON, RJ SENIOR MEMBER IEEE Department of Electrical Engineering University of Miami Coral Gables FL 33124 MEMBER IEEE Department of Electrical Engineering University of Miami Coral Gables FL 33124 Department of Electrical Engineering University of Miami Coral Gables FL 33124 Engineering Department Ford Motor de Venezuela Valencia Venezuela

binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

关键词： Pattern classification Vectors Probability distribution Statistical analysis Minimax techniques Entropy Error analysis Random variables data analysis Bioinformatics Author Keywords statistical analysis binary data analysis discriminant function minimax estimation pattern classification

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Optimal and Fast Confidence Intervals for Hypergeometric Successes

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AMERICAN STATISTICIAN 2023年第2期77卷 151-159页

作者： Bartroff, Jay Lorden, Gary Wang, Lijia Univ Texas Austin Dept Stat & Data Sci Austin TX 78712 USA CALTECH Dept Math Pasadena CA 91125 USA Univ Southern Calif Dept Math Los Angeles CA 90007 USA

We present an efficient method of calculating exact confidence intervals for the hypergeometric parameter representing the number of "successes," or "special items," in the population. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average size, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. The available R package hyperMCI implements the proposed method.

关键词： binary data analysis Estimation Exact methods Inference Quality control Sampling

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A NETWORK ALGORITHM FOR THE EXACT TREATMENT OF THE 2XK CONTINGENCY TABLE

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COMMUNICATIONS IN STATISTICS PART B-SIMULATION AND COMPUTATION 1980年第6期9卷 649-664页

作者： MEHTA, CR PATEL, NR INDIAN INST MANAGEMENT AHMEDABAD 380015GUJARATINDIA

A common statistical problem encountered in biomedical research is to test the hypothesis that the parameters of k binomial populations are all equal. An exact test of significance of this hypothesis is possible in principle, the appropriate null distribution being a normalized product of k binomial coefficients. However, the problem of computing the tail area of this distribution can be formidable since it requires the enumeration of all sets of k binomial coefficients whose product is less than a given constant. Existing algorithms, all of which rely on explicit enumeration to generate feasible binomial coefficients

关键词： contingency tables Fisher's exact test of significance binary data analysis network algorithm implicit enumeration dynamic programming

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Centered Isotonic Regression: Point and Interval Estimation for Dose-Response Studies

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2017年第3期9卷 258-267页

作者： Oron, Assaf P. Flournoy, Nancy Seattle Childrens Res Inst Childrens Core Biomed Stat 1904 9th Ave Seattle WA 98101 USA Univ Missouri Dept Stat Columbia MO 65211 USA

Univariate isotonic regression (IR) has been used for nonparametric estimation in dose-response and dose-finding studies. One undesirable property of IR is the prevalence of piecewise-constant stretches in its estimates, whereas the dose-response function is usually assumed to be strictly increasing. We propose a simple modification to IR, called centered isotonic regression (CIR). CIR's estimates are strictly increasing in the interior of the dose range. In the absence of monotonicity violations, CIR and IR both return the original observations. Numerical examination indicates that for sample sizes typical of dose-response studies and with realistic dose-response curves, CIR provides a substantial reduction in estimation error compared with IR when monotonicity violations occur. We also develop analytical interval estimates for IR and CIR, with good coverage behavior. An R package implements these point and interval estimates. Supplementary materials for this article are available online.

关键词： binary data analysis Dose-finding Nonparametric regression Nonparametric statistics Small-sample methods Up-and-down

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An Encrypted File Detection Algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2021年第8期55卷 1121-1128页

作者： Kozachok, A., V Kozachok, V., I Spirin, A. A. Acad Fed Guard Serv Russian Federat Oryol 302034 Russia

Despite the availability of data leak detection and prevention tools, there are currently a growing number of confidential data leaks through the fault of insiders. One of the possible data leak channels is encrypted or compressed data transfer, because the existing data leak detection tools use content data analysis methods. This article presents an algorithm of detecting encrypted and compressed data that is based on the statistical model of pseudo-random sequences and allows detecting encrypted and compressed data to an accuracy of 0.99.

关键词： statistical data analysis classification of encrypted and compressed data machine learning binary data analysis pseudo-random sequences

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Link Prediction for Egocentrically Sampled Networks

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2023年第4期32卷 1296-1319页

作者： Li, Tianxi Wu, Yun-Jhong Levina, Elizaveta Zhu, Ji Univ Virginia Stat Charllottesville VA USA Univ Michigan Dept Stat Ann Arbor MI USA Univ Michigan Ann Arbor MI USA

Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes. In practice, especially for social networks, the data are often collected by egocentric sampling, which means selecting a subset of nodes and recording all of their edges. This sampling mechanism requires different prediction tools than the typical assumption of links missing at random. We propose a new computationally efficient link prediction algorithm for egocentrically sampled networks, estimating the underlying probability matrix by estimating its row space. We empirically evaluate the method on several synthetic and real-world networks and show that it provides accurate predictions for network links. including the code for experiments are available online.

关键词： binary data analysis Machine learning Network modeling

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A STOCHASTIC MULTIDIMENSIONAL-SCALING VECTOR THRESHOLD-MODEL FOR THE SPATIAL REPRESENTATION OF PICK ANY/N data

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PSYCHOMETRIKA 1989年第1期54卷 105-129页

作者： DESARBO, WS CHO, JW UNIV MICHIGAN GRAD SCH BUSINESS DEPT STAT ANN ARBOR MI 48109 USA ARIZONA STATE UNIV DEPT MKT TEMPE AZ 85287 USA

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze “pick any/n” choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.

关键词： binary data analysis multidimensional scaling nonlinear probit model

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An introduction to logistic regression analysis and reporting

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JOURNAL OF EDUCATIONAL RESEARCH 2002年第1期96卷 3-14页

作者： Peng, CYJ Lee, KL Ingersoll, GM Indiana Univ Sch Educ Dept Conseling & Educ Psychol Bloomington IN 47405 USA

The purpose of this article is to provide researchers, editors, and readers with a set of guidelines for what to expect in an article using logistic regression techniques. Tables, figures, and charts that should be included to comprehensively assess the results and assumptions to be verified are discussed. This article demonstrates the preferred pattern for the application of logistic methods with an illustration of logistic regression applied to a data set in testing a research hypothesis. Recommendations are also offered for appropriate reporting formats of logistic regression results and the minimum observation-to-predictor ratio. The authors evaluated the use and interpretation of logistic regression presented in 8 articles published in The Journal of Educational Research between 1990 and 2000. They found that all 8 studies met or exceeded recommended criteria.

关键词： binary data analysis categorical variables dichotomous outcome logistic modeling logistic regression

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