X - Binus Repository
... Consequences of Violation of the Assumptions • Violation of the Assumptions – Non-normality (error not normally distributed) – Heteroscedasticity (variance not constant) • Usually happens in cross-sectional data – Autocorrelation (errors are not independent) • Usually happens in time-series data • ...
... Consequences of Violation of the Assumptions • Violation of the Assumptions – Non-normality (error not normally distributed) – Heteroscedasticity (variance not constant) • Usually happens in cross-sectional data – Autocorrelation (errors are not independent) • Usually happens in time-series data • ...
Solutions for Each Step of An Association Study
... chosen for detailed sequence analysis to identify all SNPs in these regions within a particular population. Regions are chosen based on proximity to statistically significant SNPs and, often, functional annotations. Those SNPs can then be analyzed for statistical association with the phenotype and r ...
... chosen for detailed sequence analysis to identify all SNPs in these regions within a particular population. Regions are chosen based on proximity to statistically significant SNPs and, often, functional annotations. Those SNPs can then be analyzed for statistical association with the phenotype and r ...
Managing the Machines
... predictions. An approach called “deep learning” has been particularly important to the changes of the past five years. Figure 1 shows improvements in image classification, pedestrian detection, and object detection. The IMAGENET competition has gone from 72% successful image classification in 2010 t ...
... predictions. An approach called “deep learning” has been particularly important to the changes of the past five years. Figure 1 shows improvements in image classification, pedestrian detection, and object detection. The IMAGENET competition has gone from 72% successful image classification in 2010 t ...
Audio Compression
... Uses local score matrix (scores for each element of the two strings), global score matrix (score of a complete match between two strings) Techniques of fragmentation/consolidation Eg. ...
... Uses local score matrix (scores for each element of the two strings), global score matrix (score of a complete match between two strings) Techniques of fragmentation/consolidation Eg. ...
Removal Efficiency in Industrial Scale Liquid Jet K. S. Agrawal
... standards. They are very efficient even for fine particulate removal. The removal efficiency is not only depending on scrubber geometry but also on the flow rates. There are some models which are available for the same and they are based on statistical techniques. The collection efficiency model use ...
... standards. They are very efficient even for fine particulate removal. The removal efficiency is not only depending on scrubber geometry but also on the flow rates. There are some models which are available for the same and they are based on statistical techniques. The collection efficiency model use ...
Evolutionary Optimization of Radial Basis Function Classifiers for
... squares training. 3) Feature selection for RBF networks by means of EA is investigated in only five of the publications: [34] and [35] describe class-dependent feature selection by masking of features; other examples are [47], [48], and [72]. 4) Rule extraction from trained networks is shown in [43] ...
... squares training. 3) Feature selection for RBF networks by means of EA is investigated in only five of the publications: [34] and [35] describe class-dependent feature selection by masking of features; other examples are [47], [48], and [72]. 4) Rule extraction from trained networks is shown in [43] ...
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.).