Articles de revues : « Log-linear parameters »

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ALBA, RICHARD D. « Interpreting the Parameters of Log-Linear Models ». Sociological Methods & ; Research 16, n^o 1 (août 1987) : 45–77. http://dx.doi.org/10.1177/0049124187016001003.

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Evans, Robin J., et Thomas S. Richardson. « Marginal log-linear parameters for graphical Markov models ». Journal of the Royal Statistical Society : Series B (Statistical Methodology) 75, n^o 4 (3 juillet 2013) : 743–68. http://dx.doi.org/10.1111/rssb.12020.

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Haber, Michael. « Log-Linear Models for Linked Loci : Variances of Estimated Parameters ». Biometrical Journal 30, n^o 5 (19 janvier 2007) : 589–93. http://dx.doi.org/10.1002/bimj.4710300513.

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Danaher, Peter J. « A Log-Linear Model for Predicting Magazine Audiences ». Journal of Marketing Research 25, n^o 4 (novembre 1988) : 356–62. http://dx.doi.org/10.1177/002224378802500403.

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A log-linear model for predicting magazine exposure distributions is developed and its parameters are estimated by the maximum likelihood technique. The log-linear model is compared empirically with the best-found model for equal-insertion schedules, one of Leckenby and Kishi's Dirichlet multinomial models. For unequal-insertion schedules the log-linear model is compared with the popular Metheringham beta-binomial model. The results show that the log-linear model has significantly smaller prediction errors than either of the other models.

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Mukhopadhyay, Late Anis Chandra, et Rabindra Nath Das. « Inference on log-linear regression model parameters with composite autocorrelated errors ». Model Assisted Statistics and Applications 10, n^o 3 (20 juillet 2015) : 231–42. http://dx.doi.org/10.3233/mas-150327.

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Gasmi, Soufiane. « Estimating parameters of a log-linear intensity for a repairable system ». Applied Mathematical Modelling 37, n^o 6 (mars 2013) : 4325–36. http://dx.doi.org/10.1016/j.apm.2012.09.050.

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Habib, Elsayed Ali. « Estimation of Log-Linear-Binomial Distribution with Applications ». Journal of Probability and Statistics 2010 (2010) : 1–13. http://dx.doi.org/10.1155/2010/423654.

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Log-linear-binomial distribution was introduced for describing the behavior of the sum of dependent Bernoulli random variables. The distribution is a generalization of binomial distribution that allows construction of a broad class of distributions. In this paper, we consider the problem of estimating the two parameters of log-linearbinomial distribution by moment and maximum likelihood methods. The distribution is used to fit genetic data and to obtain the sampling distribution of the sign test under dependence among trials.

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Janjic, Tomislav, Gordana Vuckovic et Milenko Celap. « Theoretical consideration and application of the SP and SP' scales in RP chromatographic systems in which Everett’s equation is valid ». Journal of the Serbian Chemical Society 67, n^o 3 (2002) : 179–86. http://dx.doi.org/10.2298/jsc0203179j.

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It is shown that in the case of ODS and less polar modifiers the log k values are a linear function of the SP?parameters. This findings differ from earlier investigated systems, in which a linear dependence between log k and SP parameters (SP = log SP?) was found. Both linear relationships have been analyzed and the corresponding possible separation mechanisms have been considered. In addition, the advantages of normalization of both scales are shown and how then they can be applied in the investigation of substances congenerity.

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Bucca, Mauricio. « Heatmaps for Patterns of Association in log-Linear Models ». Socius : Sociological Research for a Dynamic World 6 (janvier 2020) : 237802311989921. http://dx.doi.org/10.1177/2378023119899219.

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Log-linear models offer a detailed characterization of the association between categorical variables, but the breadth of their outputs is difficult to grasp because of the large number of parameters these models entail. Revisiting seminal findings and data from sociological work on social mobility, the author illustrates the use of heatmaps as a visualization technique to convey the complex patterns of association captured by log-linear models. In particular, turning log odds ratios derived from a model’s predicted counts into heatmaps makes it possible to summarize large amounts of information and facilitates comparison across models’ outcomes.

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Novak, Thomas P. « Log-Linear Trees : Models of Market Structure in Brand Switching Data ». Journal of Marketing Research 30, n^o 3 (août 1993) : 267–87. http://dx.doi.org/10.1177/002224379303000301.

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Log-linear trees restrict the log-linear model of quasi-symmetry so that parameters are interpretable as arc lengths in an additive tree. The tree representation can be interpreted further in terms of consumer heterogeneity, affording a dual interpretation in terms of both market structure and opportunities for market segmentation.

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Fan, J. H., J. H. Yang, D. X. Wu, S. H. Li, Y. Liu et Z. Y. Ji. « The Correlation between the Gamma-Ray Luminosity and the Core-Dominance Parameters for a Fermi Blazar Sample ». Proceedings of the International Astronomical Union 9, S304 (octobre 2013) : 157–58. http://dx.doi.org/10.1017/s1743921314003627.

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AbstractIn this work, we investigated the correlation between the γ-ray luminosity, logLγ and the core-dominance parameter, log (1+R), for a sample of 124 Fermi blazars with available core and extended radio emissions. Our analysis shows that there is no correlation between the γ-ray luminosity, log Lγ and the core-dominance parameter, log (1+R). However, there is a closely linear correlation between log Lγ − log LExt and log (1+R), log Lγ−log LExt = (0.95 ± 0.08) log (1+R) + (2.72 ± 0.11), for the whole sample. The result suggests that the γ-ray emissions are composed of two components, one is beamed, the other is unbeamed.

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Feng, Tao-Wei. « A linear log d log w model for the determination of consistency limits of soils ». Canadian Geotechnical Journal 38, n^o 6 (1 décembre 2001) : 1335–42. http://dx.doi.org/10.1139/t01-061.

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A linear logarithmlogarithm model for the fall cone penetration depth versus water content relationship (flow curve) has been developed based on the results of an experimental study using the British fall cone apparatus. The fall cone flow curve is expressed by a simple equation with parameters m and c, which represent the slope of the flow curve and the water content at a penetration depth of 1 mm, respectively. For a soil, the flow curve can be determined by applying a linear regression analysis to at least four data points with penetration depths approximately evenly distributed between 25 and 3 mm. It is shown in this paper that both the liquid limit and the plastic limit determined from the linear logarithmlogarithm flow curve are in close agreement with those determined from conventional methods. A one-point method for determination of the liquid limit is developed from the model and is verified by applying statistical analysis to a large volume of experimental data.Key words: fall cone, laboratory tests, consistency limits, clays.

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Jing, W., et M. Papathomas. « On the correspondence of deviances and maximum-likelihood and interval estimates from log-linear to logistic regression modelling ». Royal Society Open Science 7, n^o 1 (janvier 2020) : 191483. http://dx.doi.org/10.1098/rsos.191483.

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Consider a set of categorical variables P where at least one, denoted by Y , is binary. The log-linear model that describes the contingency table counts implies a logistic regression model, with outcome Y . Extending results from Christensen (1997, Log-linear models and logistic regression , 2nd edn. New York, NY, Springer), we prove that the maximum-likelihood estimates (MLE) of the logistic regression parameters equals the MLE for the corresponding log-linear model parameters, also considering the case where contingency table factors are not present in the corresponding logistic regression and some of the contingency table cells are collapsed together. We prove that, asymptotically, standard errors are also equal. These results demonstrate the extent to which inferences from the log-linear framework translate to inferences within the logistic regression framework, on the magnitude of main effects and interactions. Finally, we prove that the deviance of the log-linear model is equal to the deviance of the corresponding logistic regression, provided that no cell observations are collapsed together when one or more factors in P ∖ { Y } become obsolete. We illustrate the derived results with the analysis of a real dataset.

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Zighera, Jacques A. « Partitioning information in a multidimensional contingency table and centring of log-linear parameters ». Applied Stochastic Models and Data Analysis 1, n^o 2 (1985) : 93–108. http://dx.doi.org/10.1002/asm.3150010203.

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Rózylo, Jan K., Anna Zabiñska, Joanna Matysiak et Andrzej Niewiadomy. « Reversed-Phase Thin-Layer Chromatography with Different Stationary Phases in Studies of Quantitative Structure–Biological Activity Relationship of New Antimycotic Compounds ». Journal of AOAC INTERNATIONAL 82, n^o 1 (1 janvier 1999) : 31–37. http://dx.doi.org/10.1093/jaoac/82.1.31.

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Abstract Reversed-phase thin-layer chromatography with RP-8, RP-18, and RP-18W stationary phases was used in quantitative structure–activity relationship (QSAR) studies of new antimycotic compounds. The retention behavior of 10 dihydroxythioben-zanilides was examined for acquisition of log k data. With water–acetone mixtures as the mobile phases, the concentration range for which the correlation between log k′ and acetone concentration is linear was established for each stationary phase and used to determine hydrophobicity parameters log k′w by linear extrapolation. The effect of substituents on retention constants was quantitated by using the group contribution parameters τw. On the basis of QSAR equations obtained from these studies, log k′w, data can be used to predict antifungal activities of dihydroxythiobenzanilides with satisfactory accuracy.

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Balakrishnan, N., et H. J. Malik. « Best linear unbiased estimation of location and scale parameters of the log-logistic distribution ». Communications in Statistics - Theory and Methods 16, n^o 12 (janvier 1987) : 3477–95. http://dx.doi.org/10.1080/03610928708829586.

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Martínez-Flórez, Guillermo, Rafael Bráz Azevedo-Farias et Roger Tovar-Falón. « An Exponentiated Multivariate Extension for the Birnbaum-Saunders Log-Linear Model ». Mathematics 10, n^o 8 (14 avril 2022) : 1299. http://dx.doi.org/10.3390/math10081299.

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In this work, a bivariate extension of the univariate exponentiated sinh-normal distribution is proposed. The properties of the new distribution, which is called the bivariate exponentiated sinh-normal distribution, are studied in detail, and the maximum likelihood method is considered to estimate the unknown model parameters. In addition, the extension of the new distribution to the case of regression models is proposed. Monte Carlo simulation experiments are carried out to investigate the performance of the used estimation method, and two applications to real datasets are presented for illustrative purposes.

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Livingston, E. H., T. Reedy, F. W. Leung et P. H. Guth. « Computerized curve fitting in the analysis of hydrogen gas clearance curves ». American Journal of Physiology-Gastrointestinal and Liver Physiology 257, n^o 4 (1 octobre 1989) : G668—G675. http://dx.doi.org/10.1152/ajpgi.1989.257.4.g668.

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Hydrogen gas clearance curves obtained from the rat gastric corpus were digitized into a computer and then analyzed by three methods: 1) linear regression of log-transformed data, 2) direct curve fitting with a modified Gauss-Newton nonlinear regression algorithm, and 3) Zierler's height-over-area algorithm. For linear regression of log-transformed data, if the initial base-line estimate was inaccurate or normal amounts of experimental noise were present, the log-transformed data was skewed, leading to deviation of the regression line and incorrect estimation of blood flow. By utilization of the direct-fit routine, the initial estimate of the parameters or experimental noise had little influence on the blood flow determination because of iterative improvement of the parameters. In a study of isoproterenol-stimulated gastric blood flow, Zierler's algorithm underestimated the blood flow estimate. We conclude that analysis of hydrogen gas clearance curves by linear regression of log-transformed data or by Zierler's algorithm may potentially introduce errors in blood flow estimates that may be avoided by analysis with a direct-fitting, nonlinear regression algorithm.

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GARCÉS-VEGA, FRANCISCO, et BRADLEY P. MARKS. « Use of Simulation Tools To Illustrate the Effect of Data Management Practices for Low and Negative Plate Counts on the Estimated Parameters of Microbial Reduction Models ». Journal of Food Protection 77, n^o 8 (1 août 2014) : 1372–79. http://dx.doi.org/10.4315/0362-028x.jfp-13-462.

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In the last 20 years, the use of microbial reduction models has expanded significantly, including inactivation (linear and nonlinear), survival, and transfer models. However, a major constraint for model development is the impossibility to directly quantify the number of viable microorganisms below the limit of detection (LOD) for a given study. Different approaches have been used to manage this challenge, including ignoring negative plate counts, using statistical estimations, or applying data transformations. Our objective was to illustrate and quantify the effect of negative plate count data management approaches on parameter estimation for microbial reduction models. Because it is impossible to obtain accurate plate counts below the LOD, we performed simulated experiments to generate synthetic data for both log-linear and Weibull-type microbial reductions. We then applied five different, previously reported data management practices and fit log-linear and Weibull models to the resulting data. The results indicated a significant effect (α = 0.05) of the data management practices on the estimated model parameters and performance indicators. For example, when the negative plate counts were replaced by the LOD for log-linear data sets, the slope of the subsequent log-linear model was, on average, 22% smaller than for the original data, the resulting model underpredicted lethality by up to 2.0 log, and the Weibull model was erroneously selected as the most likely correct model for those data. The results demonstrate that it is important to explicitly report LODs and related data management protocols, which can significantly affect model results, interpretation, and utility. Ultimately, we recommend using only the positive plate counts to estimate model parameters for microbial reduction curves and avoiding any data value substitutions or transformations when managing negative plate counts to yield the most accurate model parameters.

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Izadbakhsh, Hamidreza, Rassoul Noorossana et Seyed Taghi Akhavan Niaki. « Monitoring multinomial logistic profiles in Phase I using log-linear models ». International Journal of Quality & ; Reliability Management 35, n^o 3 (5 mars 2018) : 678–89. http://dx.doi.org/10.1108/ijqrm-04-2017-0068.

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Purpose The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate. Design/methodology/approach Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring. Findings The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently. Originality/value The PGLM with log link has not been used to monitor multinomial profiles in Phase I.

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Bölücü, Necva, et Burcu Can. « A Cascaded Unsupervised Model for PoS Tagging ». ACM Transactions on Asian and Low-Resource Language Information Processing 20, n^o 1 (avril 2021) : 1–23. http://dx.doi.org/10.1145/3447759.

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Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

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Girard, Claude, Taha B. M. J. Ouarda et Bernard Bobée. « Étude du biais dans le modèle log-linéaire d'estimation régionale ». Canadian Journal of Civil Engineering 31, n^o 2 (1 février 2004) : 361–68. http://dx.doi.org/10.1139/l03-099.

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Log-linear models are frequently used in hydrology, especially for the regional estimation of flood volumes based on the physiographic data of a set of basins. A log-linear model describes a linear relationship between the log of a dependant variable and independent variables which are functions of parameters, of which the value remains to be determined. It is determined by using a set of basins with known values of dependant and independent variables. The model is then used to obtain a prediction for the dependant variable logarithm of a basin of interest, based on the known values of independent variables in the model. This prediction is unbiased with relation to the log of the target variable. However, the exponential value of this prediction is biased with relation to the target variable. This paper addresses the measures to correct the bias in the prediction, which is introduced by exponentiation; the impacts on the variance of the ensuing predictions is also discussed. Key words: bias, transformation, log-linear model.[Journal translation]

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Lorenzen, Robert. « Multivariate linear regression of sonic logs on petrophysical logs for detailed reservoir characterization in producing fields ». Interpretation 6, n^o 3 (1 août 2018) : T543—T553. http://dx.doi.org/10.1190/int-2018-0030.1.

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Many development wells do not have sonic logs recorded, often because of mechanical issues with deviated wellbores or high cost. Consequently, tying development wells to the seismic data covering the field becomes difficult. This issue is magnified in fields where multiple heterogeneous thin sands form thick-stacked pay packages. Multivariate linear regression is a powerful tool to analyze the interdependence of data. Well data from three producing fields in the Balingian Province, offshore Sarawak, Malaysia, are used to calculate parameters relating the recorded sonic-log data to other recorded petrophysical log data. Those parameters are used next to estimate sonic logs from petrophysical log data alone. The petrophysical log data include depth, gamma ray, density, neutron porosity, and resistivity, thus reflecting the natural assumption that the formation velocity is dependent on compaction, lithology, density, pore space, and fluid content. Parameters are calculated separately for coals, gas-filled sands, and the normal shale and sand sequences, giving one set of parameters for each well. The regression is computed at log scale for every depth point. The coefficient of determination between recorded and estimated sonic logs for the same well is up to 0.96. Blind testing is applied to assess the actual reliability of the linear regression by using the parameters from each well in turn to estimate sonic logs for the other wells with only their petrophysical logs. The best set of parameters is obtained from composite wells with tens of thousands of depth points, where the data from several wells are combined. This ensures that there are multiple instances of coal layers and gas-filled sand layers at many depths, thus providing the most representative data set. Interpretation indicates that the synthetic seismic from estimated sonic logs leads to reliable observations regarding sands and coals and their seismic character.

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CHUNG, HYUN-JUNG, SHAOJIN WANG et JUMING TANG. « Influence of Heat Transfer with Tube Methods on Measured Thermal Inactivation Parameters for Escherichia coli ». Journal of Food Protection 70, n^o 4 (1 avril 2007) : 851–59. http://dx.doi.org/10.4315/0362-028x-70.4.851.

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The purpose of this study was to investigate the influence of heat transfer on measured thermal inactivation kinetic parameters of bacteria in solid foods when using tube methods. The bacterial strain selected for this study, Escherichia coli K-12, had demonstrated typical first-order inactivation characteristics under isothermal test conditions. Three tubes of different sizes (3, 13, and 20 mm outer diameter) were used in the heat treatments at 57, 60, and 63°C with mashed potato as the test food. A computer model was developed to evaluate the effect of transit heat transfer behavior on microbial inactivation in the test tubes. The results confirmed that the survival curves of E. coli K-12 obtained in 3-mm capillary tubes were log linear at the three tested temperatures. The survival curves observed under nonisothermal conditions in larger tubes were no longer log linear. Slow heat transfer alone could only partially account for the large departures from log-linear behavior. Tests with the same bacterial strain after 5 min of preconditioning at a sublethal temperature of 45°C revealed significantly enhanced heat resistance. Confirmative tests revealed that the increased heat resistance of the test bacterium in the center of the large tubes during the warming-up periods resulted in significantly larger D-values than those obtained with capillary tube methods.

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Sharma, D., et Rakhi Nanda. « Volume prediction model for Chir pine (Pinus roxburghii Sargent) ». Indian Journal of Forestry 31, n^o 1 (1 mars 2008) : 57–60. http://dx.doi.org/10.54207/bsmps1000-2008-9x1dvo.

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The study was conducted on Chir pine stand (Pinus roxburghii Sargent) at Barog range (R-31) under Solan Forest Division (H.P.) during the year 2004-2005, to develop volume prediction model based allometric relationships between stand volume and stem growth parameters (DBH and Height). Among various linear and non-linear function, both log-linear and power function performed comparatively better over other functions. In both the functions, DBH and Height parameters explained 99 per cent and 95 per cent of variation in the stem volume, respectively. However, the power function outperformed the log-linear function, when data were subjected to chi-square test of goodness of fit and thereafter using Theil-U test. The predicted volumes based on DBH and Height was cross validated and the DBH proved to be the best predictive parameter for stem volume estimation.

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BLACK, D. GLENN, X. PHILIP YE, FEDERICO HARTE et P. MICHAEL DAVIDSON. « Thermal Inactivation of Escherichia coli O157:H7 When Grown Statically or Continuously in a Chemostat ». Journal of Food Protection 73, n^o 11 (1 novembre 2010) : 2018–24. http://dx.doi.org/10.4315/0362-028x-73.11.2018.

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The objective of this study was to determine if survivor curves for heat-inactivated Escherichia coli O157:H7 were affected by the physiological state of the cells relative to growth conditions and pH of the heating menstruum. A comparison was made between the log-linear model and non–log-linear Weibull approach. Cells were grown statically in aerobic culture tubes or in an aerobic chemostat in tryptic soy broth (pH 7.2). The heating menstruum was unbuffered peptone or phosphate buffer (pH 7.0). Thermal inactivation was carried out at 58, 59, 60, and 61°C, and recovery was on a nonselective medium. Longer inactivation times for statically grown cells indicated potential stress adaptation. This was more prevalent at 58°C. Shape response was also significantly different, with statically grown cells exhibiting decreasing thermal resistance over time and chemostat cells showing the opposite effect. Buffering the heating menstruum to ca. pH 7 resulted in inactivation curves that showed less variability or scatter of data points. Time to specific log reduction values (td) for the Weibull model were conservative relative to the log-linear model depending upon the stage of reduction. The Weibull model offered the most accurate fit of the data in all cases, especially considering the log-linear model is equivalent to the Weibull model with a fixed shape factor of 1. The determination of z-value for the log-linear model showed a strong correlation between log D-value and process temperature. Correlations for the Weibull model parameters (log δand log p) versus process temperature were not statistically significant.

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Helmy, Adel, Ivan Timofeev, Christopher R. Palmer, Alison Gore, David K. Menon et Peter J. Hutchinson. « Hierarchical log linear analysis of admission blood parameters and clinical outcome following traumatic brain injury ». Acta Neurochirurgica 152, n^o 6 (13 janvier 2010) : 953–57. http://dx.doi.org/10.1007/s00701-009-0584-y.

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Janjic, T. J., G. Vuckovic et M. B. Celap. « Influence of the composition of the stationary and mobile phase on the retention factors and solvent strength parameters in RP chromatographic systems in which the Everett equation is valid ». Journal of the Serbian Chemical Society 66, n^o 10 (2001) : 671–83. http://dx.doi.org/10.2298/jsc0110671j.

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It is shown how in RP chromatography the Everett equation for ideal phase equilibriums can be used to estimate SP values (SP = log xs/x1, xs and x1 denoting the modifier mole fractions in the stationary and mobile phases, respectively) which are in a linear dependence with the log k values. The described procedure includes the determination of the approximate phase equilibrium constant K. By analysis of the Everett equation it was found that in the field of x1/K there are regions of linear dependence of the SP parameter or log k values and the mole fraction of modifiers or its logarithm. Consequently, only in these regions it is possible for two different chromatographic systems to have the same solvent strength scale: x1 or log x1.

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Wang, Zhi Ming. « Log-Linear Process Modeling of Reliability Assessment for Numerical Control Machine Tools ». Advanced Materials Research 542-543 (juin 2012) : 1200–1203. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1200.

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The point maximum likelihood and interval estimators of the parameters, as well as two reliability indices of the log-linear process model, including cumulative mean time between failures and reliability at given time are given. In tests for failure time trends, both the graphical methods include the mean cumulative function versus time plot and the total-time-on-test plot, and the analytical methods include the Laplace, the military handbook, and the Lewis-Robinson tests are used. A real case of failure dada with time truncation for multiple numerical control (NC) machine tools is given to illustrate the use of the proposed model.

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Li, Lixiong, Chris L. Wilbur et Kathryn L. Mintz. « Kinetics of Hydrothermal Inactivation of Endotoxins ». Applied and Environmental Microbiology 77, n^o 8 (30 décembre 2010) : 2640–47. http://dx.doi.org/10.1128/aem.01460-10.

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ABSTRACTA kinetic model was established for the inactivation of endotoxins in water at temperatures ranging from 210°C to 270°C and a pressure of 6.2 × 106Pa. Data were generated using a bench scale continuous-flow reactor system to process feed water spiked with endotoxin standard (Escherichia coliO113:H10). Product water samples were collected and quantified by theLimulusamebocyte lysate assay. At 250°C, 5-log endotoxin inactivation was achieved in about 1 s of exposure, followed by a lower inactivation rate. This non-log-linear pattern is similar to reported trends in microbial survival curves. Predictions and parameters of several non-log-linear models are presented. In the fast-reaction zone (3- to 5-log reduction), the Arrhenius rate constant fits well at temperatures ranging from 120°C to 250°C on the basis of data from this work and the literature. Both biphasic and modified Weibull models are comparable to account for both the high and low rates of inactivation in terms of prediction accuracy and the number of parameters used. A unified representation of thermal resistance curves for a 3-log reduction and a 3Dvalue associated with endotoxin inactivation and microbial survival, respectively, is presented.

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Liu, Tangwei, Hehua Xu, Xiaobin Shi, Xuelin Qiu et Zhen Sun. « Reservoir Parameters Estimation with Some Log Curves of Qiongdongnan Basin Based on LS-SVM Method ». Journal of Physics : Conference Series 2092, n^o 1 (1 décembre 2021) : 012024. http://dx.doi.org/10.1088/1742-6596/2092/1/012024.

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Abstract Reservoir porosity and permeability are considered as very important parameters in characterizing oil and gas reservoirs. Traditional methods for porosity and permeability prediction are well log and core data analysis to get some regression empirical formulas. However, because of strong non-linear relationship between well log data and core data such as porosity and permeability, usual statistical regression methods are not completely able to provide meaningful estimate results. It is very difficult to measure fine scale porosity and permeability parameters of the reservoir. In this paper, the least square support vector machine (LS-SVM) method is applied to the parameters estimation with well log and core data of Qiongdongnan basin reservoirs. With the log and core exploration data of Qiongdongnan basin, the approach and prediction models of porosity and permeability are constructed and applied. There are several type of log data for the determination of porosity and permeability. These parameters are related with the selected log data. However, a precise analysis and determine of parameters require a combinatorial selection method for different type data. Some curves such as RHOB,CALI,POTA,THOR,GR are selected from all obtained logging curves of a Qiongdongnan basin well to predict porosity. At last we give some permeability prediction results based on LS-SVM method. High precision practice results illustrate the efficiency of LS-SVM method for practical reservoir parameter estimation problems.

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Chen, Bo, Chunying Ma, Witold F. Krajewski, Pei Wang et Feipeng Ren. « Logarithmic transformation and peak-discharge power-law analysis ». Hydrology Research 51, n^o 1 (2 décembre 2019) : 65–76. http://dx.doi.org/10.2166/nh.2019.108.

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Abstract The peak-discharge and drainage area power-law relation has been widely used in regional flood frequency analysis for more than a century. The coefficients and can be obtained by nonlinear or log-log linear regression. To illustrate the deficiencies of applying log-transformation in peak-discharge power-law analyses, we studied 52 peak-discharge events observed in the Iowa River Basin in the United States from 2002 to 2013. The results show that: (1) the estimated scaling exponents by the two methods are remarkably different; (2) for more than 80% of the cases, the power-law relationships obtained by log-log linear regression produce larger prediction errors of peak discharge in the arithmetic scale than that predicted by nonlinear regression; and (3) logarithmic transformation often fails to stabilize residuals in the arithmetic domain, it assigns higher weight to data points representing smaller peak discharges and drainage areas, and it alters the visual appearance of the scatter in the data. The notable discrepancies in the scaling parameters estimated by the two methods and the undesirable consequences of logarithmic transformation raise caution. When conducting peak-discharge scaling analysis, especially for prediction purposes, applying nonlinear regression on the arithmetic scale to estimate the scaling parameters is a better alternative.

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Bi, Chenyang, Jordan E. Krechmer, Manjula R. Canagaratna et Gabriel Isaacman-VanWertz. « Correcting bias in log-linear instrument calibrations in the context of chemical ionization mass spectrometry ». Atmospheric Measurement Techniques 14, n^o 10 (11 octobre 2021) : 6551–60. http://dx.doi.org/10.5194/amt-14-6551-2021.

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Abstract. Quantitative calibration of analytes using chemical ionization mass spectrometers (CIMSs) has been hindered by the lack of commercially available standards of atmospheric oxidation products. To accurately calibrate analytes without standards, techniques have been recently developed to log-linearly correlate analyte sensitivity with instrument operating conditions. However, there is an inherent bias when applying log-linear calibration relationships that is typically ignored. In this study, we examine the bias in a log-linear-based calibration curve based on prior mathematical work. We quantify the potential bias within the context of a CIMS-relevant relationship between analyte sensitivity and instrument voltage differentials. Uncertainty in three parameters has the potential to contribute to the bias, specifically the inherent extent to which the nominal relationship can capture true sensitivity, the slope of the relationship, and the voltage differential below which maximum sensitivity is achieved. Using a prior published case study, we estimate an average bias of 30 %, with 1 order of magnitude for less sensitive compounds in some circumstances. A parameter-explicit solution is proposed in this work for completely removing the inherent bias generated in the log-linear calibration relationships. A simplified correction method is also suggested for cases where a comprehensive bias correction is not possible due to unknown uncertainties of calibration parameters, which is shown to eliminate the bias on average but not for each individual compound.

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Okoli, Cecilia N., Sidney I. Onyeagu et George A. Osuji. « On the Estimation of Parameters and Best Model Fits of Log Linear Model for Contingency Table ». OALib 02, n^o 01 (2015) : 1–11. http://dx.doi.org/10.4236/oalib.1101189.

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Oler, Jacqueline. « Noncentrality Parameters in Chi-Squared Goodness-of-Fit Analyses with an Application to Log-Linear Procedures ». Journal of the American Statistical Association 80, n^o 389 (mars 1985) : 181–89. http://dx.doi.org/10.1080/01621459.1985.10477158.

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Melo, José Roberto Tude, Federico Di Rocco, Michel Zerah et Philippe Meyer. « About hierarchical log linear analysis of admission blood parameters and clinical outcome following traumatic brain injury ». Acta Neurochirurgica 152, n^o 11 (31 août 2010) : 2005. http://dx.doi.org/10.1007/s00701-010-0745-z.

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White, Sarah J., Denis Drieghe, Simon P. Liversedge et Adrian Staub. « The word frequency effect during sentence reading : A linear or nonlinear effect of log frequency ? » Quarterly Journal of Experimental Psychology 71, n^o 1 (janvier 2018) : 46–55. http://dx.doi.org/10.1080/17470218.2016.1240813.

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The effect of word frequency on eye movement behaviour during reading has been reported in many experimental studies. However, the vast majority of these studies compared only two levels of word frequency (high and low). Here we assess whether the effect of log word frequency on eye movement measures is linear, in an experiment in which a critical target word in each sentence was at one of three approximately equally spaced log frequency levels. Separate analyses treated log frequency as a categorical or a continuous predictor. Both analyses showed only a linear effect of log frequency on the likelihood of skipping a word, and on first fixation duration. Ex-Gaussian analyses of first fixation duration showed similar effects on distributional parameters in comparing high- and medium-frequency words, and medium- and low-frequency words. Analyses of gaze duration and the probability of a refixation suggested a nonlinear pattern, with a larger effect at the lower end of the log frequency scale. However, the nonlinear effects were small, and Bayes Factor analyses favoured the simpler linear models for all measures. The possible roles of lexical and post-lexical factors in producing nonlinear effects of log word frequency during sentence reading are discussed.

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Luo, S., et Shuxia Pang. « Empirical likelihood for quantile regression models with response data missing at random ». Open Mathematics 15, n^o 1 (27 mars 2017) : 317–30. http://dx.doi.org/10.1515/math-2017-0028.

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Abstract This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random. It follows that a class of quantile empirical log-likelihood ratios including quantile empirical likelihood ratio with complete-case data, weighted quantile empirical likelihood ratio and imputed quantile empirical likelihood ratio are defined for the regression parameters. Then, a bias-corrected quantile empirical log-likelihood ratio is constructed for the mean of the response variable for a given quantile level. It is proved that these quantile empirical log-likelihood ratios are asymptotically χ2 distribution. Furthermore, a class of estimators for the regression parameters and the mean of the response variable are constructed, and the asymptotic normality of the proposed estimators is established. Our results can be used directly to construct the confidence intervals (regions) of the regression parameters and the mean of the response variable. Finally, simulation studies are conducted to assess the finite sample performance and a real-world data set is analyzed to illustrate the applications of the proposed method.

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Beniušienė, Lina, Edmundas Petrauskas, Marius Aleinikovas, Iveta Varnagirytė-Kabašinskienė, Ričardas Beniušis et Benas Šilinskas. « Norway Spruce Stem Parameters in Sites with Different Stand Densities in Lithuanian Hemiboreal Forest ». Forests 12, n^o 2 (10 février 2021) : 201. http://dx.doi.org/10.3390/f12020201.

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Background and Objectives: The study aimed to determine the changes of the main stem and branch parameters of Norway spruce (Picea abies (L.) H. Karst) trees under different stand densities. More specifically, the objective was to develop the models for the determination of branch diameter in 0–6 m log from root collar, taken as one of the parameters directly influencing the stem quality. The study continues a piece of research on stem and branch parameters’ responses to different stand density (SD) in the plantations of coniferous tree species in Lithuania. Materials and Methods: The following key parameters were measured in this study: total tree height, diameter at breast height, height to the lowest live branch, height to the lowest dead branch, and diameter of all branches in 0–6 m log. The linear regression models to predict branch diameter in 0–6 m log were developed based on stand density (SD), tree characteristics (tree diameter at breast height, DBH; and tree height, H) and other related stem and branch parameters. Results and Conclusions: Directly measured tree DBH, branch diameters and number of branches in 0–6 m log decreased significantly with the increasing SD. In the 0–6 m log, the branch diameter and the diameter of the thickest branch were identified as the main parameters related to stem quality. The best fitted models, developed including SD, tree DBH, branch diameter, and diameter of the thickest branch in 0–3 m log, can be proposed as a predictor for stem-wood quality for Norway spruce in hemiboreal forest zone.

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Poirier, D., T. H. Sanders et J. P. Davis. « Salmonella Surrogate Reduction Using Industrial Peanut Dry Roasting Parameters ». Peanut Science 41, n^o 2 (1 juillet 2014) : 72–84. http://dx.doi.org/10.3146/ps13-21.1.

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ABSTRACT Studies were conducted to evaluate the effectiveness of industrial peanut dry roasting parameters in Salmonella reduction using a Salmonella surrogate, Enterococcus faecium, which is slightly more heat tolerant than Salmonella. Runner-type peanuts were inoculated with E. faecium and roasted in a laboratory scale roaster simulator in which temperature, airflow, airflow direction and bed depth were highly controlled, allowing for conditions that duplicate industrial dry roasting. Temperature data were collected at the top, middle and bottom of the roasting bed in addition to internal peanut temperature via thermocouples in the bed of peanuts and embedded in a peanut. Regardless of roast conditions, peanuts in the middle of the roasting bed received the least amount of heat and hence, represent the worst case scenario for microbial reduction. E. faecium reductions, reported as the logarithm of colony forming units/g (log CFU/g), followed a linear trend with increasing roasting time when peanuts were roasted at 149, 163, and 177 C, with > 5-log CFU/g reductions occurring at the middle of the peanut bed after 21, 15 and 11 min, respectively, at a bed depth of 75 mm and an air flow of 1.3 m/s. Increased air flow increased E. faecium reduction. At 16 min roast time and a 75 mm bed depth, reduction at the middle of the bed was ≤ 3-log CFU/g at 1 m/s and > 5-log CFU/g at 1.3 m/s. When all other roast parameters were held constant, decreasing bed depth also increased reduction of E. faecium in the middle of the bed. Comparing various samples roasted at 149, 163 and 177 C over a range of times, roast color (Hunter L-value) was positively correlated (R2 = 0.73) with the log reduction of E. faecium. Most peanuts with an L-value darker than 53, a common threshold for light roast had ≥ 5-log CFU/g reductions; however, further study is required, including roasting peanuts from different origins and maturity, to fully understand the implications of roast color development and microbial reduction. This work provides valuable practical information for manufacturers of roasted peanuts when validating Salmonella reductions under a particular set of roasting parameters.

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Bestugin, Aleksandr, Sergey Dvornikov, Sergey Dvornikov et Irina Kirshina. « Study of the electrical parameters dependences of oblique log-periodic antennas on the geometric structure of their reducing elements ». Journal of Physics : Conference Series 2373, n^o 6 (1 décembre 2022) : 062007. http://dx.doi.org/10.1088/1742-6596/2373/6/062007.

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Abstract Antenna systems are an integral part of any transmitter-receiver device. A broad-band and frequency-independent antenna can be created applying a principle of electrical similarity. Its essence is that the electrical parameters of antennas remain unchanged if, with a decrease or increase in wavelength n times and, accordingly, decrease or increase the linear dimensions of the antenna elements. It is very important to guarantee the constancy of the antenna-radiating element’s parameters in a sufficiently wide frequency range. Log-periodic, biconical and various types of helical antennas satisfy these requirements. Structurally, a log-periodic antenna is a complex structure consisting of a symmetrical two-wire or four-wire collecting line. Radiators of the same type are connected to it in a cross order. The paper presents results obtained on the basis of modeling the electrical parameters of wide-range log-periodic antennas of the decameter range with different shapes of radiating elements in the MMANA-GAL environment. The authors considered methods for constructing periodic structures of flat log-periodic antennas. The interrelation of their structural elements is presented. It determines properties of wide-range operation. The electrical parameters dependences of inclined log-periodic antennas on the structural differences of their radiating elements are investigated. The conditions for the vibrator and frame emitters application are substantiated while maintaining the range properties of the log-periodic structure.

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Alsasua, Roberto, Daniel Lapresa, Javier Arana et M. Teresa Anguera. « A log-linear analysis of efficiency in elite basketball applied to observational methodology ». International Journal of Sports Science & ; Coaching 14, n^o 3 (25 mars 2019) : 363–71. http://dx.doi.org/10.1177/1747954119837819.

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Although the full potential of observational methodology is realized through diachronic analyses, synchronic analyses can be used to investigate associations between categorical variables. Log-linear modeling is an appropriate method for investigating associations between three or more dimensions using multidimensional contingency tables. We provide a practical example of how we used log-linear analysis to study efficiency in a men’s basketball competition played by Spain’s top teams using a model containing three dimensions (and their respective categories): position of last pass before a shot, position of shot, and result of shot. The best-fit and most parsimonious model (i.e., the model that provided the best explanation of the observed frequencies in the contingency table and that contained the fewest effects) was a conditional independence model in which last pass position and shot position were associated independently of the categories in the shot result dimension and the interaction between shot position and shot result was not affected by the categories in the last pass dimension. Estimation and subsequent interpretation of the significant parameters in the selected model showed how log-linear modeling can provide basketball coaches with practical insights within an observational methodology study.

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Bitar, S. D., C. P. Campos et C. E. C. Freitas. « Applying fuzzy logic to estimate the parameters of the length-weight relationship ». Brazilian Journal of Biology 76, n^o 3 (3 mai 2016) : 611–18. http://dx.doi.org/10.1590/1519-6984.20014.

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Abstract We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.

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Gabsi, W., T. Boubaker et R. Goumont. « Quantification of the Nucleophilicities of 3-X-Thiophenes : Highlighting the Hyperortho Correlation ». Journal of Chemistry 2020 (19 mars 2020) : 1–9. http://dx.doi.org/10.1155/2020/2164759.

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Kinetics studies for the coupling reactions of the 3-X-thiophene 1a-c (X = CH3, H and Br) with the electrophiles 2a and 3a-c have been investigated in acetonitrile at 20°C The second-order rate constants have been employed to determine the nucleophilicity parameters N and s of the thiophene 1 according the Mayr equation log k (20°C) = s (E + N). The nucleophilic-specific parameters N and s quantified in this work have been derived and compared with the reactivity of other C nucleophiles. Based on the linear correlations log k1 = f(E) and log k1 = f(σp+), we have shown that the mechanism of interactions occurs by a unique process: electrophilic heteroaromatic substitution of an α-carbon position of substituted 3-X-thiophenes 1 known hyperortho correlation.

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Crump, Kenny S. « The Effect of Random Error in Exposure Measurement upon the Shape of the Exposure Response ». Dose-Response 3, n^o 4 (1 octobre 2005) : dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.002.

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Although statistical analyses of epidemiological data usually treat the exposure variable as being known without error, estimated exposures in epidemiological studies often involve considerable uncertainty. This paper investigates the theoretical effect of random errors in exposure measurement upon the observed shape of the exposure response. The model utilized assumes that true exposures are log-normally distributed, and multiplicative measurement errors are also log-normally distributed and independent of the true exposures. Under these conditions it is shown that whenever the true exposure response is proportional to exposure to a power r, the observed exposure response is proportional to exposure to a power K, where K < r. This implies that the observed exposure response exaggerates risk, and by arbitrarily large amounts, at sufficiently small exposures. It also follows that a truly linear exposure response will appear to be supra-linear—i.e., a linear function of exposure raised to the K-th power, where K is less than 1.0. These conclusions hold generally under the stated log-normal assumptions whenever there is any amount of measurement error, including, in particular, when the measurement error is unbiased either in the natural or log scales. Equations are provided that express the observed exposure response in terms of the parameters of the underlying log-normal distribution. A limited investigation suggests that these conclusions do not depend upon the log-normal assumptions, but hold more widely. Because of this problem, in addition to other problems in exposure measurement, shapes of exposure responses derived empirically from epidemiological data should be treated very cautiously. In particular, one should be cautious in concluding that the true exposure response is supra-linear on the basis of an observed supra-linear form.

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Laurinčikas, A., et G. Vadeikis. « Joint weighted universality of the Hurwitz zeta-functions ». St. Petersburg Mathematical Journal 33, n^o 3 (5 mai 2022) : 511–22. http://dx.doi.org/10.1090/spmj/1712.

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Joint weighted universality theorems are proved concerning simultaneous approximation of a collection of analytic functions by a collection of shifts of Hurwitz zeta-functions with parameters α 1 , … , α r \alpha _1,\dots ,\alpha _r . For this, linear independence is required over the field of rational numbers for the set { log ⁡ ( m + α j ) : m ∈ N 0 = N ∪ { 0 } , j = 1 , … , r } \{\log (m+\alpha _j)\,:\, m\in \mathbb {N}_0=\mathbb {N}\cup \{0\},\;j=1,\dots ,r\} .

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Yuan, Mingao, et Yue Zhang. « Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline ». International Journal of Statistics and Probability 8, n^o 1 (24 décembre 2018) : 135. http://dx.doi.org/10.5539/ijsp.v8n1p135.

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In this paper, we apply empirical likelihood method to infer for the regression parameters in the partial functional linear regression models based on B-spline. We prove that the empirical log-likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi-square distributions. Our simulation shows that the proposed empirical likelihood method produces more accurate confidence regions in terms of coverage probability than the asymptotic normality method.

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Olanrewaju, Rasaki Olawale. « Integer-valued Time Series Model via Generalized Linear Models Technique of Estimation ». International Annals of Science 4, n^o 1 (29 avril 2018) : 35–43. http://dx.doi.org/10.21467/ias.4.1.35-43.

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The paper authenticated the need for separate positive integer time series model(s). This was done from the standpoint of a proposal for both mixtures of continuous and discrete time series models. Positive integer time series data are time series data subjected to a number of events per constant interval of time that relatedly fits into the analogy of conditional mean and variance which depends on immediate past observations. This includes dependency among observations that can be best described by Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model with Poisson distributed error term due to its positive integer defined range of values. As a result, an integer GARCH model with Poisson distributed error term was formed in this paper and called Integer Generalized Autoregressive Conditional Heteroscedasticity (INGARCH). Iterative Reweighted Least Square (IRLS) parameter estimation technique type of the Generalized Linear Models (GLM) was adopted to estimate parameters of the two spilt models; Linear and Log-linear INGARCH models deduced from the identity link function and logarithmic link function, respectively. This resulted from the log-likelihood function generated from the GLM via the random component that follows a Poisson distribution. A study of monthly successful bids of auction from 2003 to 2015 was carried out. The Probabilistic Integral Transformation (PIT) and scoring rule pinpointed the uniformity of the linear INGARCH than that of the log-linear INGARCH in describing first order autocorrelation, serial dependence and positive conditional effects among covariates based on the immediate past. The linear INGARCH model outperformed the log-linear INGARCH model with (AIC = 10514.47, BIC = 10545.01, QIC = 34128.56) and (AIC = 37588.83, BIC = 37614.28, QIC = 37587.3), respectively.

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Hansen, Nikolaus. « An Analysis of Mutative σ-Self-Adaptation on Linear Fitness Functions ». Evolutionary Computation 14, n^o 3 (septembre 2006) : 255–75. http://dx.doi.org/10.1162/evco.2006.14.3.255.

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This paper investigates σ-self-adaptation for real valued evolutionary algorithms on linear fitness functions. We identify the step-size logarithm log σ as a key quantity to understand strategy behavior. Knowing the bias of mutation, recombination, and selection on log σ is sufficient to explain σ-dynamics and strategy behavior in many cases, even from previously reported results on non-linear and/or noisy fitness functions. On a linear fitness function, if intermediate multi-recombination is applied on the object parameters, the i-th best and the i-th worst individual have the same σ-distribution. Consequently, the correlation between fitness and step-size σ is zero. Assuming additionally that σ-changes due to mutation and recombination are unbiased, then σ-self-adaptation enlarges σ if and only if μ < λ/2, given (μ, λ)-truncation selection. Experiments show the relevance of the given assumptions.

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Akkermans, Simen, Davy Verheyen, Cindy Smet et Jan F. M. Van Van Impe. « A Population Balance Model to Describe the Evolution of Sublethal Injury ». Foods 10, n^o 7 (20 juillet 2021) : 1674. http://dx.doi.org/10.3390/foods10071674.

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The detection and quantification of sublethal injury (SI) of pathogenic microorganisms has become a common procedure when assessing the efficiency of microbial inactivation treatments. However, while a plethora of studies investigates SI in function of time, no suitable modelling procedure for SI data has been proposed thus far. In this study, a new SI model structure was developed that relies on existing microbial inactivation models. This model is based on the description of inactivation kinetics between the subpopulations of healthy, sublethally injured and dead cells. The model was validated by means of case studies on previously published results, modelled by different inactivation models, i.e., (i) log-linear inactivation; (ii) biphasic inactivation; and (iii) log-linear inactivation with tailing. Results were compared to those obtained by the traditional method that relies on calculating SI from independent inactivation models on non-selective and selective media. The log-linear inactivation case study demonstrated that the SI model is equivalent to the use of independent models when there can be no mistake in calculating SI. The biphasic inactivation case study illustrated how the SI model avoids unrealistic calculations of SI that would otherwise occur. The final case study on log-linear inactivation with tailing clarified that the SI model provides a more mechanistic description than the independent models, in this case allowing the reduction of the number of model parameters. As such, this paper provides a comprehensive overview of the potential and applications for the newly presented SI model.

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