Correlation and Regression
... • Table of actual # of S’s in each criterion group vs. predicted group membership – Shows, in detail, how well regression predicted data ...
... • Table of actual # of S’s in each criterion group vs. predicted group membership – Shows, in detail, how well regression predicted data ...
Querying Data Graphs with Arithmetical Regular Expressions
... Tuples of paths can be compared by regular relations [Elgot and Mezei, 1965; Frougny and Sakarovitch, 1993]. Examples of such relations are path equality, length comparisons, prefix (i.e., a path is a prefix of another path), fixed edit distance etc. Regular relations on n-tuples of paths can be de ...
... Tuples of paths can be compared by regular relations [Elgot and Mezei, 1965; Frougny and Sakarovitch, 1993]. Examples of such relations are path equality, length comparisons, prefix (i.e., a path is a prefix of another path), fixed edit distance etc. Regular relations on n-tuples of paths can be de ...
A method for estimating insect abundance and patch occupancy
... Often data do not support only one model as clearly best for analysis (Burnham & Anderson, 2002). Therefore, there is always uncertainty about the operating model that has given rise to the observations because only a sample from the population is observed (Sileshi 2006). This raises the issue of co ...
... Often data do not support only one model as clearly best for analysis (Burnham & Anderson, 2002). Therefore, there is always uncertainty about the operating model that has given rise to the observations because only a sample from the population is observed (Sileshi 2006). This raises the issue of co ...
NBER WORKING PAPER SERIES WHAT ARE WE WEIGHTING FOR? Gary Solon
... about half those for WLS. Apparently, weighting by population made the estimates much less precise! And as discussed by Dickens (1990), this is quite a common phenomenon. To see what’s going on here, let’s start with the classic heteroskedasticity-based argument for weighting when the dependent vari ...
... about half those for WLS. Apparently, weighting by population made the estimates much less precise! And as discussed by Dickens (1990), this is quite a common phenomenon. To see what’s going on here, let’s start with the classic heteroskedasticity-based argument for weighting when the dependent vari ...
Fuzzy Information Approaches to Equipment Condition Monitoring and Diagnosis
... allows propagation of uncertainties along extended chains of reasoning, and eases implementation of large knowledge bases. Several techniques for representing uncertainty in expert systems have been proposed in the AI literature including Bayesian analysis and certainty measures [4]. For the most p ...
... allows propagation of uncertainties along extended chains of reasoning, and eases implementation of large knowledge bases. Several techniques for representing uncertainty in expert systems have been proposed in the AI literature including Bayesian analysis and certainty measures [4]. For the most p ...
Artificial Intelligence for Artificial Artificial Intelligence
... values of using our ballot model is much higher than the majority vote (82.2% against 63.6%). This shows that even though the two approaches achieve the same accuracy on all 45 votes, the ballot model has superior belief in its answer. While the confidence values are different the ballot model seems ...
... values of using our ballot model is much higher than the majority vote (82.2% against 63.6%). This shows that even though the two approaches achieve the same accuracy on all 45 votes, the ballot model has superior belief in its answer. While the confidence values are different the ballot model seems ...
PDF
... stretching back to the birth of econometrics itself as an emerging science in the 1920s and 1930s (Working, 1922; Schultz, 1938). Since then, a very large literature has developed focusing on estimating commodity supply and demand systems; forecasting commodity supplies and prices; and evaluating th ...
... stretching back to the birth of econometrics itself as an emerging science in the 1920s and 1930s (Working, 1922; Schultz, 1938). Since then, a very large literature has developed focusing on estimating commodity supply and demand systems; forecasting commodity supplies and prices; and evaluating th ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).