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Topic: Statistical estimation

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Quantitative psychological research

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	Glossary - NCES Statistical Standards
		Estimation is the process of using sample data to provide a single best value for a parameter (such as a mean, proportion, correlation, or effect size), or to provide a range of values in the form of a confidence interval.
		Metropolitan Statistical Areas (MSAs) are those areas that: (1) include a city of at least 50,000 population, or (2) include a Census Bureau-defined urbanized area (of at least 50,000 population) with a total metropolitan population of at least 100,000 (75,000 in New England).
		Statistical disclosure limitation techniques are used to prepare microdata files for release, included are perturbation techniques and coarsening techniques.
	www.nces.ed.gov /statprog/2002/glossary.asp (4471 words)

	EstimateS: Biodiversity Estimation
		EstimateS 8.0.0 features Chao's Sørensen and Jaccard similarity estimators (Ecology Letters 8:148-159, 2005), an option for individual randomization export, usability improvements, and a new 4D Engine.
		Between 1997 and November, 2006, EstimateS was downloaded moret than 20,000 times by users in more than 100 countries around the globe.
		All richness estimators and diversity indices are computed for every level of sample accumulation, averaged over resamplings.
	viceroy.eeb.uconn.edu /estimates (252 words)

	Baylor University \|\| Department of Statistical Science \|\| Courses
		Topics may be selected from the following:descriptive statistics and graphs, probability, regression, correlation, tests of hypotheses, interval estimation, measurement, reliability, experimental design, analysis of variance, nonparametric methods, and multivariate methods.
		Interval estimation, tests of hypotheses, non-parametric methods, linear regression and correlation, categorical data analysis, design of experiments and analysis of variance, and the use of computer packages.
		Statistical models and procedures for describing and analyzing random vector response data.
	www.baylor.edu /statistics/index.php?id=18582 (1310 words)

	Statistical Estimation in Neuroscience
		Estimating this f(y) is not fundamentally different from point estimation, as examined in Section 2 of this tutorial.
		This estimator, which we denote f**, is a smooth function which is known to approximate rather well the tuning curves of motor cortical neurons.
		In fact the bias is the difference between the expected value of the estimate (remember that the estimate is a random variable since it depends on the data D) and the true f(y).
	www.dam.brown.edu /people/elie/stats_tutorial.html (4827 words)

	Interactive Statistical Calculation Pages
		Percentage: Estimation and Testing -- calculates exact binomial confidence intervals and tests of hypothesis for population proportion, from infinite or finite populations.
		Subjective Assessment of Estimates -- (relative precision as a measuring tool for inaccuracy assessment among estimates), tests the claim that at least one estimate is away from the parameter by more than r times (i.e., a relative precision), where r is a subjective positive number less than one.
		Sample Size Determination -- For several situations: ANOVA and 2-population economic sampling, correlation with acceptable absolute precision, estimating the mean or proportion with acceptable absolute or relative Precision, estimating the mean or proportion from finite populations, and testing the mean or proportion based on the Null and an Alternative.
	statpages.org (9672 words)

	Statistical Properties
		homogeneous units each, then after completion of the experiment, when the statistical analysis of results is performed, we are able to isolate the variability in response due to this systematic unit heterogeneity.
		The statistically ``good'' design is also the intuitively appealing one: make separate random assignments of the three cocktails to the three larger plants, and to the three smaller plants, so that each cocktail is used once with a plant of each size.
		The statistical use of the term ``block design'' should now be clear: a block design is a plan for an experiment in which the experimental units have been partitioned into homogeneous sets, telling us which treatment each experimental unit receives.
	designtheory.org /library/extrep/html/node22.html (959 words)

	Statistical Modeling, Causal Inference, and Social Science
		This allows the correlations to be estimated from data in a way that still allows you to provide a reasonable amount of information about the scale parameters.
		Statistical significance gets it wrong because it focuses on null hypotheses (usually artificial), but when you say Type S it almost sounds similar in that you are thinking of truth/falsity with respect to the sign, rather than uncertainty about effects...?
		This is one way that applied statistics proceeds, by exemplary analyses of problems that might not be hugely important on their own terms but serve as useful templates.
	www.stat.columbia.edu /~cook/movabletype/mlm (6524 words)

	Statistical Data Mining Tutorials
		Gaussians, both the friendly univariate kind, and the slightly-reticent-but-nice-when-you-get-to-know-them multivariate kind are extremely useful in many parts of statistical data mining, including many data mining models in which the underlying data assumption is highly non-Gaussian.
		Cross-validation is one of several approaches to estimating how well the model you've just learned from some training data is going to perform on future as-yet-unseen data.
		The resulting estimate is somewhat conservative but still represents an interesting avenue by which computer science has tried to muscle in on the kind of analytical problem that you would normally find in a statistics department.
	www.autonlab.org /tutorials (3144 words)

	Statistical Estimation and Characterisation Techniques for use during Accident Response (SECTAR)
		The SECTAR project investigated the potential of statistical techniques to improve estimates of the extent of radioactive contamination in the early stages of an accident.
		Trials were carried out using the statistical techniques alone and in conjunction with the traditional assessment methods of atmospheric dispersion and foodchain modelling.
		Results from this process of ‘data assimilation’, the combined use of measurements and modelling to make estimates, indicate that several of the methods tested have the potential to be useful to the Food Standards Agency.
	www.hpa.org.uk /radiation/publications/hpa_rpd_reports/2005/hpa_rpd_009.htm (238 words)

	Department of Statistics \| Graduate Course Descriptions
		Topics include random, stratified, cluster, and systematic sampling; estimation of means and variances; optimal allocation of resources; problems of nonsampling errors; and ratio and regression estimation.
		Multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector and dispersion matrix; correlation, partial correlation and multiple correlation coefficients; Hotelling's T2; MANOVA; discriminant function; repeated measurements analysis; principal components and canonical correlation; factor analysis; and multidimensional scaling.
		Estimation and testing of hypothesis when the function form of the population distribution function is not completely specified.
	www.sbm.temple.edu /dept/statistics/graduate/course.html (1375 words)

	Iowa State University Courses and Programs
		Statistical concepts in modern society; descriptive statistics and graphical displays of data; the normal distribution; data collection; elementary probability; elements of statistical inference; estimation and hypothesis testing; linear regression and correlation; contingency tables.
		Descriptive statistics; elementary probability distributions; principles of experimentation; confidence intervals and significance tests; one-, two-, and many-factor studies; regression analysis; use of statistical software on the university computers; team project involving multi-factor experimentation and analysis.
		Statistical concepts and models; estimation; hypothesis tests with continuous and discrete data; simple and multiple linear regression and correlation; introduction to analysis of variance.
	www.iastate.edu /~catalog/9597/stat.html (2429 words)

	1 Statistical inference, estimation, hypothesis testing
		The theory of statistics takes the real data arising from the practical situation and uses these data to validate a specific model, to make 'rational guesses' or 'estimates' of the numerical values of relevant parameters, or even to originate a model.
		Statistical decision-making increases the reliability of the research, because we can generalise the results of our data to the larger population.
		These results are of direct interest in the planning of sampling enquires, as they enable the investigator to estimate the precision attainable with a sample of a given size, and hence help him to decide how large a sample should be taken.
	www.uku.fi /~mauranen/advbis/advbis1.htm (1180 words)

	Estimation theory - Wikipedia, the free encyclopedia
		Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data.
		The entire purpose of estimation theory is to arrive at an estimator, and preferably an implementable one that could actually be used.
		An optimal estimator would indicate that all available information in the measured data has been extracted, for if there was unused information in the data then the estimator would not be optimal.
	en.wikipedia.org /wiki/Statistical_estimation (973 words)

	Statistical estimation (Site not responding. Last check: )
		In a more general case, they are second-moment statistics of the corresponding random processes.
		The second step is to approach model estimation as an optimization problem and to use an iterative method for solving it.
		The goal is to obtain a reasonable model estimate after only a small number of iterations.
	sepwww.stanford.edu /public/docs/sep107/paper_html/node13.html (335 words)

	OECD Glossary of Statistical Terms - Estimation
		Estimation is concerned with inference about the numerical value of unknown population values from incomplete data such as a sample.
		Therefore the sample estimates need to be inflated to represent the whole population of interest.
		Estimation is the means by which this inflation occurs.
	stats.oecd.org /glossary/detail.asp?ID=860 (144 words)

	Advanced Statistical Analysis of Experimental Data
		The goal of this course is to introduce several adequate and recently emerged statistical techniques not covered by traditional statistical courses, such as statistical analysis of images.
		Estimation of implicit relationships specified by differential equations.
		This is one of solid standard texts on principles of statistical inference with many examples from physics and engineering.
	www.dartmouth.edu /~engs299 (505 words)

	RMT 7:2 Estimation Methods, Statistical Independence and Global Fit (Site not responding. Last check: )
		The Infit and Outfit statistics for this element are global mean-square fit statistics (chi-squares divided by degrees of freedom) with z-score-equivalent significance levels.
		Statistical consistency is the property that, when applied to an infinite amount of data, the estimation algorithm will give a "right" answer.
		I agree that, when two raters rate the same performance, their ratings are not statistically independent in general.
	www.rasch.org /rmt/rmt72n.htm (990 words)

	Statistical Estimation
		Assume that the standard deviation of the population is known to be 75.
		Construct a 99% confidence interval estimate of the proportion of all voters who favor A. Enter the labels for the worksheet.
		Suppose you want to construct a 95% confidence interval estimate of the mean of a population (known to have a standard deviation of 16.2) with a desired margin of error of ± 3.
	www.cob.sjsu.edu /anaya_j/StatEst.htm (1439 words)

	Stable methods of statistical models estimation. Совместные проекты. Наука и техника
		The topological method of the stable coefficients estimation of the statistical models in the conditions of the initial multicollinear factors is stated for the first time.
		The use of the complex functions and optimal coordinates of the factor space for stable estimation of the statistical models were presented as well.
		Stable estimation of the statistical models in the arbitrary convex regions of the factor space
	www.n-t.org /sp/lesmi/ume.htm (1060 words)

	Course Offerings - Department of Biostatistics, University of Pittsburgh
		The purpose of the course is to present the theory and practice of statistical learning algorithms, placing "statistical learning" or "data mining" techniques in the proper context with regard to their origins in simple classical methods like linear regression, to clarify the strengths and weaknesses from theoretical and practical sides.
		Although this course is primarily theoretical practical issues are discussed such as convergence problems when obtaining,maximum likelihood estimations, impact of missing data, and the role of some of the recent computationally intensive methods.
		Topics covered include the multivariate normal distribution, estimation of the mean vector and covariance matrix, distributions and uses of simple, partial and multiple conclation correlation coefficients, the generalized T2 statistic, the distribution of the sample generalized variance, multivariate analysis of variance and the multivariate Behrens-Fisher problem.
	www.biostat.pitt.edu /courses.htm (2916 words)

	Counting Fish by Statistical Estimation
		Hence, we estimate that the are approximately 150 fish in the pond.
		In statistical terms, a sample in which each member of the population has the same chance of being chosen as any other member is called a random sample.
		One way to reduce the variability of our estimates of n is to take several samples of size 15 and replace x in formula (4) by x¯, which is the average of the values of x obtained in the samples.
	www.math.temple.edu /links/CETP/fish.html (1189 words)

	Statistical estimation
		There are three kinds of estimations as following; however, basically, I just could solve this problem because I just have this information of Fu-Bun bank case from Taiwan.
		The last important distribution is chi-square, select “CHINV” and “Statistical” under the “function” screen.
		All of the estimations in difference distribution are very important.
	www.calpoly.edu /~dwaldorf/stathelp/7.estim.htm (469 words)

	USC AME and Robotics Study Group : Statistical Point Estimation (Site not responding. Last check: )
		A Bayes estimator is an estimator that is chosen to minimize the posterior mean of some mesure of how far the estimator is from the parameter, such as squared error or absolute error.
		The goal of this tutorial is to understand parameter estimation from a Bayesian, Least Squares persepctive where the variables of interest follow normal distributions.
		This is by far the largest and most widely used family of estimators.
	www-robotics.usc.edu /~studygrp/index.php?n=Main.StatisticalPointEstimation (471 words)

	STBIAS correct for estimation bias
		STBIAS=Y causes an approximate correction for estimation bias in JMLE estimates to be applied to measures and calibrations.
		The problem with JMLE is not that persons and items are estimated simultaneously but that the possibility of infinite score vectors is not eliminated from the estimation space (as Haberman demonstrates in his "Analysis of Qualitative Data").
		So the estimation of person abilities with known item difficulties (as is done in the person reporting stage of software based on CMLE, PMLE and MMLE) produces statistically biased person measures.
	www.winsteps.com /winman/stbias.htm (850 words)

	Statistical Estimation (Site not responding. Last check: )
		Many important statistical problems can be expressed as the problem of determining some characteristic of a population when it is not possible or feasible to measure every individual in the population.
		Since it is not possible or feasible to contact every individual in the respective populations, the only reasonable alternative is to select in some way a sample from the population and use the information contained within the sample to estimate the population characteristic of interest.
		At first thought, it would seem that what should be done here is to select a representative sample from the population, since such a sample would mirror the properties of the population.
	www.utdallas.edu /~ammann/cs3341/node44.html (290 words)

	STATISTICS
		STAT 111 Lectures in Applied Statistics (1) NW Weekly lectures illustrating the importance of statisticians in a variety of fields, including medicine and the biological, physical, and social sciences.
		Statistical methods based on the idea of probability as a measure of uncertainty.
		Seminar series covering technical and non-technical aspects of statistical consulting, including skills for effective communication with clients, report writing, statistical tips and rules of thumb, issues in survey sampling, and issues in working as a statistical consultant in academic, industrial, and private-practice settings.
	www.washington.edu /students/crscat/stat.html (3176 words)

	Amazon.com: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory: Books: Steven M. Kay (Site not responding. Last check: )
		This text is geared towards a one-semester graduate-level course in statistical signal processing and estimation theory.
		A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.
		Modern estimation theory can be found at the heart of many electronic signal processing systems designed to extract information.
	www.amazon.com /Fundamentals-Statistical-Signal-Processing-Estimation/dp/0133457117 (1663 words)

	Hierarchical Linear Models: Statistical Estimation and Interpretation
		Hierarchical Linear Models are appropriate to a wide variety of social science research questions that require the estimation of quantitative data with a hierarchical structure.
		For example, the academic performance of students is a function of both the individual capacities of students and the characteristics of the schools they attend.
		The estimation procedures for Hierarchical Linear Models is not available in any standard statistical packages such as SPSS or SAS.
	www.indiana.edu /~ucs/stf/sociology.html (620 words)

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