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Articles 91 - 105 of 105
Full-Text Articles in Physical Sciences and Mathematics
Generalized Least-Squares Regressions Ii: Theory And Classification, Nataniel Greene
Generalized Least-Squares Regressions Ii: Theory And Classification, Nataniel Greene
Publications and Research
In the first paper of this series, a variety of known and new symmetric and weighted least-squares regression methods were presented with efficient derivations. This paper continues and generalizes the previous work with a theory for deriving, analyzing, and classifying all symmetric and weighted least-squares regression methods.
Computational Insight With Monte Carlo Simulations, Boyan Kostadinov
Computational Insight With Monte Carlo Simulations, Boyan Kostadinov
Publications and Research
We introduce Monte Carlo simulations for estimating areas by playing a game of "darts". We also introduce simulations of random walks. We use compact, vectorized programming, based on the R language, for all computer simulations and visualizations, aimed at high school students. This presentation is based on the Invited, prime time lecture given at the summer camp for gifted high school students at City College of New York, July 13, 2011.
Neath Studies, Teaches The Uncertainties Of Life, Aldemaro Romero Jr.
Neath Studies, Teaches The Uncertainties Of Life, Aldemaro Romero Jr.
Publications and Research
No abstract provided.
Statistician Recommends A Dose Of Skepticism, Aldemaro Romero Jr.
Statistician Recommends A Dose Of Skepticism, Aldemaro Romero Jr.
Publications and Research
No abstract provided.
Methods Of Assessing And Ranking Probable Sources Of Error, Nataniel Greene
Methods Of Assessing And Ranking Probable Sources Of Error, Nataniel Greene
Publications and Research
A classical method for ranking n potential events as sources of error is Bayes' theorem. However, a ranking based on Bayes' theorem lacks a fundamental symmetry: the ranking in terms of blame for error will not be the reverse of the ranking in terms of credit for lack of error. While this is not a flaw in Bayes' theorem, it does lead one to inquire whether there are related methods which have such symmetry. Related methods explored here include the logical version of Bayes' theorem based on probabilities of conditionals, probabilities of biconditionals, and ratios or differences of credit to …
An Overview Of Conditionals And Biconditionals In Probability, Nataniel Greene
An Overview Of Conditionals And Biconditionals In Probability, Nataniel Greene
Publications and Research
Conditional and biconditional statements are a standard part of symbolic logic but they have only recently begun to be explored in probability for applications in artificial intelligence. Here we give a brief overview of the major theorems involved and illustrate them using two standard model problems from conditional probability.
A Dynamic-Trend Exponential Smoothing Model, Don Miller, Dan Williams
A Dynamic-Trend Exponential Smoothing Model, Don Miller, Dan Williams
Publications and Research
Forecasters often encounter situations in which the local pattern of a time series is not expected to persist over the forecasting horizon. Since exponential smoothing models emphasize recent behavior, their forecasts may not be appropriate over longer horizons. In this paper, we develop a new model in which the local trend line projected by exponential smoothing converges asymptotically to an assumed future long-run trend line, which might be an extension of a historical long-run trend line. The rapidity of convergence is governed by a parameter. A familiar example is an economic series exhibiting persistent long-run trend with cyclic variation. This …
Hierarchical Linear Modeling In Organizational Research: Longitudinal Data Outside The Context Of Growth Modeling, Irvin Sam Schonfeld, David Rindskopf
Hierarchical Linear Modeling In Organizational Research: Longitudinal Data Outside The Context Of Growth Modeling, Irvin Sam Schonfeld, David Rindskopf
Publications and Research
Organizational researchers, including those carrying out occupational stress research, often conduct longitudinal studies. Hierarchical linear modeling (HLM; also known as multilevel modeling and random regression) can efficiently organize analyses of longitudinal data by including within- and between-person levels of analysis. A great deal of longitudinal research has been conducted in the context of growth studies in which change in the dependent variable is examined in relation to the passage of time. HLM can treat longitudinal data, including data outside the context of the growth study, as nested data, reducing the problem of censoring. Within-person equation coefficients can represent the impact …
Level Adjusted Exponential Smoothing: A Method For Judgmentally Adjusting Exponential Smoothing Models For Planned Discontinuities, Dan Williams, Don Miller
Level Adjusted Exponential Smoothing: A Method For Judgmentally Adjusting Exponential Smoothing Models For Planned Discontinuities, Dan Williams, Don Miller
Publications and Research
Forecasters often make judgmental adjustments to exponential smoothing forecasts to account for the effects of a future planned change. While this approach may produce sound initial forecasts, it can result in diminished accuracy for forecast updates. A proposed technique lets the forecaster include policy change adjustments within an exponential smoothing model. For 20 real data series representing Virginia Medicaid expenses, initial forecasts and forecast updates are developed using the proposed technique and several alternatives, and they are updated through various simulated level shifts. The proposed technique was more accurate than the alternatives in updating forecasts when a shift in level …
Performance Indices For On-Ice Hockey Statistics, William (Bill) H. Williams
Performance Indices For On-Ice Hockey Statistics, William (Bill) H. Williams
Publications and Research
No abstract provided.
Generating Unbiased Ratio And Regression Estimators, William (Bill) H. Williams
Generating Unbiased Ratio And Regression Estimators, William (Bill) H. Williams
Publications and Research
Standard ratio and regression are only conditionally unbiased. The paper uses split sample techniques to develop unbiased versions.
How Bad Can Good Data Really Be?, William (Bill) H. Williams
How Bad Can Good Data Really Be?, William (Bill) H. Williams
Publications and Research
Bias has different sources. Measurement errors create "bad" data and biased estimates. But selection biases occur even with "good" data and can be both subtle and large in magnitude.
A Simple Method For The Construction Of Empirical Confidence Limits For Economic Forecasts, William (Bill) H. Williams, M. L. Goodman
A Simple Method For The Construction Of Empirical Confidence Limits For Economic Forecasts, William (Bill) H. Williams, M. L. Goodman
Publications and Research
A simple method for the construction of empirical confidence intervals for time series forecasts is described. The procedure is to go through the series making a forecast from each point in time. The comparison of these forecasts with the known actual observations will yield an empirical distribution of forecasting errors. This distribution can then be used to set confidence intervals for subsequent forecasts. The technique appears to be particularly useful when the mechanism generating the series cannot be fully identified from the available data or when limits based on more standard considerations are difficult to obtain.
Analysis Of Time Usage In Bell System Business Offices, William (Bill) H. Williams, Hwei Chen
Analysis Of Time Usage In Bell System Business Offices, William (Bill) H. Williams, Hwei Chen
Publications and Research
No abstract provided.
On Two Methods Of Unbiased Estimation With Auxiliary Variates, William (Bill) H. Williams
On Two Methods Of Unbiased Estimation With Auxiliary Variates, William (Bill) H. Williams
Publications and Research
No abstract provided.