Search results for 'Parameter estimation' (try it on Scholar)

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  1. Patrizio E. Tressoldi & Adam Rock (forthcoming). Everyone Can Be a Bayesian: A Step-by-Step Guide to Model Comparison and Parameter Estimation. Frontiers in Psychology.score: 90.0
    The shortcomings of Null Hypothesis Significance Testing (NHST) are numerous and well known. An alternative that circumvents many of the limitations of NHST is the Bayesian approach to statistics. However, Bayesian techniques are often criticized as esoteric and difficult to understand. Thus, the aim of the present paper was to provide a clearly articulated guide to performing two Bayesian techniques: model comparison and parameter estimation. Guidelines are also provided regarding the presentation and interpretation of Bayesian results.
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  2. Richard Scheines, Estimating Latent Causal Influences: Tetrad III Variable Selection and Bayesian Parameter Estimation.score: 57.0
    The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead (...)
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  3. Mostafa Bachar (forthcoming). Modeling the Cardiovascular-Respiratory Control System: Data, Model Analysis, and Parameter Estimation. Acta Biotheoretica.score: 45.0
    Several key areas in modeling the cardiovascular and respiratory control systems are reviewed and examples are given which reflect the research state of the art in these areas. Attention is given to the interrelated issues of data collection, experimental design, and model application including model development and analysis. Examples are given of current clinical problems which can be examined via modeling, and important issues related to model adaptation to the clinical setting.
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  4. M. I. Charles E. Woodson (1969). Parameter Estimation Vs. Hypothesis Testing. Philosophy of Science 36 (2):203-204.score: 45.0
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  5. Jacoby Nori, Keller Peter & Repp Bruno (2013). Parameter Estimation of Sensorimotor Synchronization Models. Frontiers in Human Neuroscience 7.score: 45.0
  6. Ernst PÖppel (1975). Parameter Estimation or Hypothesis Testing in the Statistical Analysis of Biological Rhythms? Bulletin of the Psychonomic Society 6 (5):511-512.score: 45.0
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  7. Yuan Shi & Xing Zhong (2008). Hierarchical Differential Evolution for Parameter Estimation in Chemical Kinetics. In. In Tu-Bao Ho & Zhi-Hua Zhou (eds.), Pricai 2008: Trends in Artificial Intelligence. Springer. 870--879.score: 45.0
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  8. Rolf Niedermeier (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.score: 42.0
    A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a (...)
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  9. Jean-Fran�ois Laslier (1989). Estimation of a Bernouilli Parameter: A Normative Approach to Replace the Bayesian One. Theory and Decision 26 (3):253-262.score: 42.0
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  10. M. P. Michaelides (2009). A Review of the Effects on IRT Item Parameter Estimates with a Focus on Misbehaving Common Items in Test Equating. [REVIEW] Frontiers in Psychology 1:167-167.score: 42.0
    Many studies have investigated the topic of change or drift in item parameter estimates in the context of item response theory (IRT). Content effects, such as instructional variation and curricular emphasis, as well as context effects, such as the wording, position, or exposure of an item have been found to impact item parameter estimates. The issue becomes more critical when items with estimates exhibiting differential behavior across test administrations are used as common for deriving equating transformations. This paper (...)
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  11. Johan Grasman, Willem B. E. Van Deventer & Vincent van Laar (2012). Estimation of Parameters in a Bertalanffy Type of Temperature Dependent Growth Model Using Data on Juvenile Stone Loach (Barbatula Barbatula). Acta Biotheoretica 60 (4):393-405.score: 36.0
    Parameters of a Bertalanffy type of temperature dependent growth model are fitted using data from a population of stone loach ( Barbatula barbatula ). Over two periods respectively in 1990 and 2010 length data of this population has been collected at a lowland stream in the central part of the Netherlands. The estimation of the maximum length of a fully grown individual is given special attention because it is in fact found as the result of an extrapolation over a (...)
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  12. Christoph Graf, Rudolf Vetschera & Yingchao Zhang (2013). Parameters of Social Preference Functions: Measurement and External Validity. Theory and Decision 74 (3):357-382.score: 33.0
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  13. Mark Johnson & Stefan Riezler (2002). Statistical Models of Syntax Learning and Use. Cognitive Science 26 (3):239-253.score: 30.0
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  14. Gordon M. Becker (1958). Sequential Decision Making: Wald's Model and Estimates of Parameters. Journal of Experimental Psychology 55 (6):628.score: 26.0
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  15. A. N. Golodnikov, P. S. Knopov & V. A. Pepelyaev (2004). Estimation of Reliability Parameters Under Incomplete Primary Information. Theory and Decision 57 (4):331-344.score: 26.0
    We consider the procedure for small-sample estimation of reliability parameters. The main shortcomings of the classical methods and the Bayesian approach are analyzed. Models that find robust Bayesian estimates are proposed. The sensitivity of the Bayesian estimates to the choice of the prior distribution functions is investigated using models that find upper and lower bounds. The proposed models reduce to optimization problems in the space of distribution functions.
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  16. Robert J. Schreiber (1957). Estimates of Expected Value as a Function of Distribution Parameters. Journal of Experimental Psychology 53 (3):218.score: 24.0
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  17. Manfred A. Pfeifer, Klaus Henle & Josef Settele (2007). Populations with Explicit Borders in Space and Time: Concept, Terminology, and Estimation of Characteristic Parameters. Acta Biotheoretica 55 (4).score: 22.0
    Biologists studying short-lived organisms have become aware of the need to recognize an explicit temporal extend of a population over a considerable time. In this article we outline the concept and the realm of populations with explicit spatial and temporary boundaries. We call such populations “temporally bounded populations”. In the concept, time is of the same importance as space in terms of a dimension to which a population is restricted. Two parameters not available for populations that are only spatially defined (...)
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  18. Dora Matzke, Jonathon Love, Thomas V. Wiecki, Scott D. Brown, Gordon D. Logan & Eric-Jan Wagenmakers (2013). Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal Reaction Time Distributions. Frontiers in Psychology 4.score: 22.0
    The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke, Dolan, Logan, Brown, and Wagenmakers (in press) have developed a Bayesian parametric approach that allows for the estimation of the entire distribution (...)
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  19. Charles E. Collyer (1988). Parameter Estimates Depend Both on the Source Model and on the Fitted Model: An Example. Bulletin of the Psychonomic Society 26 (4):289-292.score: 21.0
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  20. Chunni Wang, Yujun He, Jun Ma & Long Huang (forthcoming). Parameters Estimation, Mixed Synchronization, and Antisynchronization in Chaotic Systems. Complexity:n/a-n/a.score: 21.0
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  21. Donald D. Dorfman, Lynn L. Beavers & Carl Saslow (1973). Estimation of Signal Detection Theory Parameters From Rating-Method Data: A Comparison of the Method of Scoring and Direct Search. Bulletin of the Psychonomic Society 1 (3):207-208.score: 18.0
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  22. Christine Schiltz Sandrine Mejias (2013). Estimation Abilities of Large Numerosities in Kindergartners. Frontiers in Psychology 4.score: 18.0
    The approximate number system (ANS) is thought to be a building block for the elaboration of formal mathematics. However, little is known about how this core system develops and if it can be influenced by external factors at a young age (before the child enters formal numeracy education). The purpose of this study was to examine numerical magnitude representations of 5 to 6 year old children at 2 different moments of Kindergarten considering children’s early number competence as well as schools’ (...)
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  23. Eyal Shahar (2007). Estimating Causal Parameters Without Target Populations. Journal of Evaluation in Clinical Practice 13 (5):814-816.score: 18.0
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  24. Woosuk Park (2012). Abduction and Estimation in Animals. Foundations of Science 17 (4):321-337.score: 16.0
    One of the most pressing issues in understanding abduction is whether it is an instinct or an inference. For many commentators find it paradoxical that new ideas are products of an instinct and products of an inference at the same time. Fortunately, Lorenzo Magnani’s recent discussion of animal abduction sheds light on both instinctual and inferential character of Peircean abduction. But, exactly for what reasons are Peirce and Magnani so convinced that animal abduction can provide us with a novel perspective? (...)
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  25. Romeijn, J.-W., Statistics as Inductive Inference.score: 15.0
    This chapter1 concerns the relation between statistics and inductive logic. I start by describing induction in formal terms, and I introduce a general notion of probabilistic inductive inference. This provides a setting in which statistical procedures and inductive logics can be cap- tured. Speciacally, I discuss three statistical procedures (hypotheses testing, parameter estimation, and Bayesian statistics) and I show to what extend they can be captured by certain inductive logics. I end with some suggestions on how inductive.
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  26. Stanley A. Mulaik (2001). The Curve-Fitting Problem: An Objectivist View. Philosophy of Science 68 (2):218-241.score: 15.0
    Model simplicity in curve fitting is the fewness of parameters estimated. I use a vector model of least squares estimation to show that degrees of freedom, the difference between the number of observed parameters fit by the model and the number of explanatory parameters estimated, are the number of potential dimensions in which data are free to differ from a model and indicate the disconfirmability of the model. Though often thought to control for parameter estimation, the AIC (...)
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  27. Peter Spirtes, Clark Glymour & Richard Scheines, Automated Search for Causal Relations - Theory and Practice.score: 15.0
    nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...)
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  28. Richard Scheines, The Tetrad Project: Constraint Based Aids to Causal Model Specification.score: 15.0
    The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model’s parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model’s specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural (...)
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  29. John R. Vokey (1998). Statistics Without Probability: Significance Testing as Typicality and Exchangeability in Data Analysis. Behavioral and Brain Sciences 21 (2):225-226.score: 15.0
    Statistical significance is almost universally equated with the attribution to some population of nonchance influences as the source of structure in the data. But statistical significance can be divorced from both parameter estimation and probability as, instead, a statement about the atypicality or lack of exchangeability over some distinction of the data relative to some set. From this perspective, the criticisms of significance tests evaporate.
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  30. Otis Dudley Duncan (1986). Probability, Disposition, and the Inconsistency of Attitudes and Behavior. Synthese 68 (1):65 - 98.score: 15.0
    Inconsistency of attitudes and behavior is due to the probabilistic connection between responses or actions and the (not directly observable) dispositions on which they depend. Latent variable models provide criteria for recognizing when attitude and behavior depend on the same disposition. Statistical tests of such models and techniques of parameter estimation are described. The viewpoint proposed here and illustrated with empirical examples contrasts with the prevalent reliance on correlational models and methods.
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  31. Joseph F. Hanna (1978). On Transmitted Information as a Measure of Explanatory Power. Philosophy of Science 45 (4):531-562.score: 15.0
    This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic (...)
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  32. Mélanie Zetlaoui, Nicolas Picard & Avner Bar-Hen (forthcoming). Asymptotic Distribution of Density-Dependent Stage-Grouped Population Dynamics Models. Acta Biotheoretica.score: 15.0
    Matrix models are widely used in biology to predict the temporal evolution of stage-structured populations. One issue related to matrix models that is often disregarded is the sampling variability. As the sample used to estimate the vital rates of the models are of finite size, a sampling error is attached to parameter estimation, which has in turn repercussions on all the predictions of the model. In this study, we address the question of building confidence bounds around the predictions (...)
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  33. Carl W. Helstrom (1974). “Simultaneous Measurement” From the Standpoint of Quantum Estimation Theory. Foundations of Physics 4 (4):453-463.score: 15.0
    The purpose of the simultaneous measurement of noncommuting quantum observables can be viewed as the joint estimation of parameters of the density operator of the quantum system. Joint estimation involves the application of a multiply parameterized operator-valued measure. An example related to the simultaneous estimation of the position and velocity of a particle is given. Conceptual difficulties attending simultaneous measurement of noncommuting observables are avoided by this formation.
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  34. Nilly Adam, Angelina D. Castro & Donald L. Clark (1974). Production, Estimation, and Reproduction of Time Intervals During Inhalation of a General Anesthetic in Man. Journal of Experimental Psychology 102 (4):609.score: 15.0
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  35. Ian Begg (1974). Estimation of Word Frequency in Continuous and Discrete Tasks. Journal of Experimental Psychology 102 (6):1046.score: 15.0
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  36. C. R. Bell (1965). Time Estimation and Increases in Body Temperature. Journal of Experimental Psychology 70 (2):232.score: 15.0
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  37. A. Berman (1939). The Relation of Time Estimation to Satiation. Journal of Experimental Psychology 25 (3):281.score: 15.0
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  38. William Bevan & Edward D. Turner (1964). Assimilation and Contrast in the Estimation of Number. Journal of Experimental Psychology 67 (5):458.score: 15.0
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  39. Patrick A. Boudewyns & Harry L. Madison (1970). Luminance as a Reinforcement Parameter in Probability Learning. Journal of Experimental Psychology 83 (3, Pt.1):502-503.score: 15.0
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  40. Chi‐Ming Chang, Wen‐Chou Lin, Hsu‐Sung Kuo, Ming‐Fang Yen & Tony Hsiu‐Hsi Chen (2007). Estimation and Prediction System for Multi‐State Disease Process: Application to Analysis of Organized Screening Regime. Journal of Evaluation in Clinical Practice 13 (6):867-881.score: 15.0
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  41. A. Cordón‐Franco, A. Fernández‐Margarit & F. F. Lara‐Martín (2011). A Note on Parameter Free Π1‐Induction and Restricted Exponentiation. Mathematical Logic Quarterly 57 (5):444-455.score: 15.0
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  42. Eric D. Curton & Daniel S. Lordahl (1974). Effects of Attentional Focus and Arousal on Time Estimation. Journal of Experimental Psychology 103 (5):861.score: 15.0
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  43. James W. Dees (1966). Accuracy of Absolute Visual Distance and Size Estimation in Space as a Function of Stereopsis and Motion Parallax. Journal of Experimental Psychology 72 (3):466.score: 15.0
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  44. Ruthanne K. S. Dewolfe & Carl P. Duncan (1959). Time Estimation as a Function of Level of Behavior of Successive Tasks. Journal of Experimental Psychology 58 (2):153.score: 15.0
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  45. Margaret E. Dow & Jesse E. Gordon (1957). The Interaction of Association Value and Stimulus Configuration in Size Estimation. Journal of Experimental Psychology 54 (5):332.score: 15.0
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  46. Wesley M. DuCharme & Cameron R. Peterson (1969). Proportion Estimation as a Function of Proportion and Sample Size. Journal of Experimental Psychology 81 (3):536.score: 15.0
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  47. Robert J. Filer & Donald W. Meals (1949). The Effect of Motivating Conditions on the Estimation of Time. Journal of Experimental Psychology 39 (3):327.score: 15.0
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  48. Arthur B. Fitt (1917). The Estimation of Distances by Sight and Passive Touch: Some Investigations Into the Evolution of the Sense of Touch. Journal of Experimental Psychology 2 (4):264-288.score: 15.0
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  49. J. J. Harton (1938). The Influence of the Difficulty of Activity on the Estimation of Time. Journal of Experimental Psychology 23 (3):270.score: 15.0
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  50. F. Nowell Jones & Morris J. Woskow (1966). Some Effects of Context on the Slope in Magnitude Estimation. Journal of Experimental Psychology 71 (2):177.score: 15.0
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