323,004 research outputs found
Car Accident Lawyer Chicago
No full text<p>At Drake & Collopy, P.C., we have devoted our careers to zealously advocating for the rights of those who have been injured due to the negligence of others. We understand the enormous impact an accident and injury can have on one’s life, and we are passionate about seeking justice on behalf of our clients. </p>
Review of: Predicting Presidential Elections and Other Things
No full textRay Fair is one of my favorite econometricians. He has an excellent website, fairmodel.econ.yale.edu, where he freely shares his models. He writes clearly and his methods are reported in detail. He is concerned with the proper use of econometric methods. For example, he has long been opposed to the common practice of making ex post subjective adjustments to forecasts from econometric models. A review of empirical evidence supports his position (Armstrong and Collopy 1998).presidential elections, forecasting
Integration of Statistical Methods and Judgment for Time Series
No full textWe consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can have a substantial impact on the accuracy of the resulting forecasts. Integration harms accuracy when judgment is biased or its use is unstructured. Equal-weights combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data involve high uncertainty or high instability, we recommend revising judgment, revising extrapolations, or combining. When good domain knowledge is available for the future as well as for the past, we recommend rule- based forecasting or econometric methods.statistical methods, statistics, time series, forecasting, empirical research
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations
No full textThis paper examines the feasibility of rule -based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule- based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule - based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.Rule-based forecasting, time series
Recent Decisions
No full textComments on recent decisions by Sidney Baker, Arthur L. Beaudette, Mark Harry Berens, Francis W. Collopy, Patrick F. Coughlin, Benedict R. Danko, Joseph M. Gaydos, William T. Huston, Francis J. Keating, John E. Lindberg, James D. Matthews, Lawrence S. May, Jr., Maurice J. Moriarty, George J. Murphy, Jr., William J. O\u27Connor, Charles James Perrin, Albert R. Ritcher, Henry Martin Shine, Jr., Cyril C. Vidra, and Dale A. Winnie
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recent Decisions
No full textComments on recent decisions by Sidney Baker, Arthur L. Beaudette, Mark Harry Berens, Francis W. Collopy, Patrick F. Coughlin, Benedict R. Danko, Joseph M. Gaydos, William T. Huston, Francis J. Keating, John E. Lindberg, James D. Matthews, Lawrence S. May, Jr., Maurice J. Moriarty, George J. Murphy, Jr., William J. O\u27Connor, Charles James Perrin, Albert R. Ritcher, Henry Martin Shine, Jr., Cyril C. Vidra, and Dale A. Winnie
Causal Forces: Structuring Knowledge for Time-series Extrapolation
No full textThis paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal effects were expected, as in longer- range forecasts. One rule suggested by this analysis was: “Do not extrapolate trends if they are contrary to causal forces.” We tested this rule by comparing forecasts from a method that implicitly assumes supporting trends (Holt’s exponential smoothing) with forecasts from the random walk. Use of the rule improved accuracy for 20 series where the trends were contrary; the MdAPE (Median Absolute Percentage Error) was 18% less for the random walk on 20 one-year ahead forecasts and 40% less for 20 six-year-ahead forecasts. We then applied the rule to four other data sets. Here, the MdAPE for the random walk forecasts was 17% less than Holt’s error for 943 short-range forecasts and 43% less for 723 long-range forecasts. Our study suggests that the causal assumptions implicit in traditional extrapolation methods are inappropriate for many applications.Causal forces Combining Contrary trends Damped trends Exponential smoothing Judgment Rule-based forecasting Selecting methods
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