1864 results about "Regression analysis" patented technology

Computer network method and apparatus provides targeting of appropriate audience based on psychographic or behavioral profiles of end users. The psychographic profile is formed by recording computer activity and viewing habits of the end user. Content of categories of interest and display format in each category are revealed by the psychographic profile, based on user viewing of agate information. Using the profile (with or without additional user demographics), advertisements are displayed to appropriately selected users. Based on regression analysis of recorded responses of a first set of users viewing the advertisements, the target user profile is refined. Viewing by and regression analysis of recorded responses of subsequent sets of users continually auto-targets and customizes ads for the optimal end user audience.

A method and system schedules inbound inquiries, such as inbound telephone calls, for response by agents in an order that is based in part on the forecasted outcome of the inbound inquiries. A scheduling module applies inquiry information to a model to forecast the outcome of an inbound inquiry. The forecasted outcome is used to set a priority value for ordering the inquiry. The priority value may be determined by solving a constrained optimization problem that seeks to maximize an objective function, such as maximizing an agent's productivity to produce sales or to minimize inbound call attrition. A modeling module generates models that forecast inquiry outcomes based on a history and inquiry information. Statistical analysis such as regression analysis determines the model with the outcome related to the nature of the inquiry. Operator wait time is regulated by forcing low priority and / or highly tolerant inbound inquiries to self service.

In a method to improve the calibration of a non-invasive, near infrared (NIR) measurement device, a plurality of data terms is formed for the NIR measurement device. Then the codependence of the data terms is evaluated by forming cross-products terms using the data terms. Next, sets of prespecified sizes are randomly formed from the data terms and the cross-product terms. Each of these sets of terms is evaluated by testing the ability of the set to predict a set of accurate measurements using regression analysis. The method then selects one of the sets based on preselected criteria and uses the selected set to calibrate the NIR measurement device.

A method and system schedules inbound inquiries, such as inbound telephone calls, for response by agents in an order that is based in part on the forecasted outcome of the inbound inquiries. A scheduling module applies inquiry information to a model to forecast the outcome of an inbound inquiry. The forecasted outcome is used to set a priority value for ordering the inquiry. The priority value may be determined by solving a constrained optimization problem that seeks to maximize an objective function, such as maximizing an agent's productivity to produce sales or to minimize inbound call attrition. The inbound call may be placed on a virtual hold or be responded to on a real-time basis based on the inbound inquiry's priority value. A modeling module generates models that forecast inquiry outcomes based on a history and inquiry information. Statistical analysis such as regression analysis determines the model with the outcome related to the nature of the inquiry. Forecasted outcomes are based on the goal of the inbound calls and include factors such as probability an inbound caller will hang up, probability that an inbound caller will alter a business relationship based on hold time, probability that an inbound caller will make a purchase, and the relative probable reward of responding to an inbound call.

A minimally invasive, implantable heart sound and ECG monitor and associated method for deriving blood pressure from heart sound data. The device is equipped with an acoustical sensor for detecting first and second heart sounds which are sampled and stored during sensing windows following R-wave and T-wave detections, respectively. ECG and heart sound data are stored in a continuous, looping memory, and segments of data are stored in long-term memory upon an automatic or manual data storage triggering event. Estimated blood pressure is calculated based on custom spectral analysis and processing of the first and second heart sounds. A calibration method includes measuring a patient's blood pressure using a standard clinical method and performing regression analysis on multiple spectral variables to identify a set of best fit weighted equations for predicting blood pressure. Concurrent ECG and estimated blood pressure may be displayed for review by a physician.

Nonlinear generalized dynamic regression analysis system and method of the present invention preferably uses all available data at all time points and their measured time relationship to each other to predict responses of a single output variable or multiple output variables simultaneously. The present invention, in one aspect, is a system and method for predicting whether an intervention administered to a patient changes the physiological, pharmacological, pathophysiological, or pathopsychological state of the patient with respect to a specific medical condition. The present invention uses the theory of martingales to derive the probabilistic properties for statistical evaluations. The approach uniquely models information in the following domains: (1) analysis of clinical trials and medical records including efficacy, safety, and diagnostic patterns in humans and animals, (2) analysis and prediction of medical treatment cost-effectiveness, (3) the analysis of financial data, (4) the prediction of protein structure, (5) analysis of time dependent physiological, psychological, and pharmacological data, and any other field where ensembles of sampled stochastic processes or their generalizations are accessible. A quantitative medical condition evaluation or medical score provides a statistical determination of the existence or onset of a medical condition.

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.

A photo category classification method including dividing a region of a photo based on content of the photo and extracting a visual feature from the segmented region of the photo, modeling at least one local semantic concept included in the photo according to the extracted visual feature, acquiring a posterior probability value from confidence values acquired from the modeling of the at least one local semantic concept by normalization using regression analysis, modeling a global semantic concept included in the photo by using the posterior probability value of the at least one local semantic concept; and removing classification noise from a confidence value acquired from the modeling the global semantic concept.

Disclosed is a method of measuring a parameter of a litho-graphic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.

In a congestion prediction using measurement data which is acquired by an on-road sensor or a probe car, and which includes none of explicit information about bottleneck points, with respect to time-sequence data on congestion ranges accumulated in the past, data on congestion front-end positions are summarized into plural clusters by the clustering. Representative value in each cluster is assumed as position of each bottleneck. A regression analysis, in which day factors are defined as independent variables, is performed with congestion length from each bottleneck point selected as the target. Here, the day factors refer to factors such as day of the week, national holiday / etc. It then becomes possible to precisely predict a future congestion length.

Illustrative embodiments of the present invention are directed to a method and system for disaggregating climate control energy use from non-climate control energy use for a building. The method includes receiving a series of building energy use values and corresponding outdoor temperature values for a time period. Each of the energy use values and outdoor temperature values is associated with a time interval. The method further includes determining a series of temperature difference values for the time period based on a difference in temperature between a predetermined baseline temperature and each of the outdoor temperature values. A regression analysis is used to determine a climate control coefficient and a non-climate control coefficient from the energy use values and temperature difference values. The climate control coefficient and / or the non-climate control coefficient is used to determine climate control energy use and / or non-climate control energy use for the building.

The invention discloses an abnormal flow detection method and device, computer equipment and a storage medium. The method comprises the following steps of: screening the historical flow data of the monitoring object; obtaining target traffic data, carrying out regression analysis on the target traffic data; establishing a flow prediction model, predicting the flow data of the monitored object by using the flow prediction model; obtaining a prediction result of the flow data of the monitoring object in a preset time period, carrying out statistical analysis is on a prediction result according to a preset statistical mode; obtaining a traffic alarm threshold value of the monitored object, further obtaining a real-time access log of the monitored object when the real-time traffic data of themonitored object is greater than the traffic alarm threshold value, and performing abnormal traffic alarm if the response time of the real-time access log is greater than the response time threshold value. According to the embodiment of the invention, the accuracy of the alarm threshold can be improved, judgment is carried out by combining the response time of the service, and thus the accuracy ofabnormal flow detection is improved.

A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weights and scores a variety of alerts to determine an alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.

A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.

Apparatus, methods, and computer readable media having computer code for calibrating chromatograms to achieve chromatographic peak shape correction, noise filtering, peak detection, retention time determination, baseline correction, and peak area integration. A method for processing a chromatogram, comprises obtaining at least one actual chromatographic peak shape function from one of an internal standard, an external standard, or an analyte represented in the chromatogram; performing chromatographic peak detection using known peak shape functions with regression analysis; reporting regression coefficients from the regression analysis as one of peak area and peak location; and constructing a calibration curve to relate peak area to known concentrations in the chromatogram. A method for constructing an extracted ion chromatogram, comprises calibrating a low resolution mass spectrometer for both mass and peak shape in profile mode; performing mass spectral peak analysis and reporting both mass locations and integrated peak areas; specifying a mass defect window of interest; summing up all detected peaks with mass defects falling within the specified mass defect window to derive summed intensities; and plotting the summed intensities against time to generate a mass defect filtered chromatogram.

A method and apparatus for measuring a blood analyte concentration using an inexpensive, low precision sensor device is provided. One embodiment of the method includes obtaining energy absorption measurement data through a body part of an individual. A plurality of indicator variables are subsequently calculated from the energy absorption measurement data. An indicator variable from the plurality of indicator variables having the highest correlation to blood glucose level is determined, and a first optical term based on the indicator variable with the highest correlation to blood glucose level is allocated to form a regression analysis equation. An estimated value of the blood analyte concentration using the regression analysis equation is calculated.

The invention provides a pre-stack inversion thin layer oil / gas-bearing possibility identifying method. The pre-stack inversion thin layer oil / gas-bearing possibility identifying method comprises the following steps of: (1) constructing interpreting data and logging data by utilizing earthquake, and establishing an initial elastic parameter model based on a deposition mode; (2) obtaining an initial model and a model restrain range of random inversion through determination inversion based on a Baysian principle; (3) carrying out random sampling on three-parameter-related Monte Carlo simulationestablished by utilizing rock physical diagnosis and regression analysis so as to obtain a logging scale model; (4) calculating to obtain an elastic parameter model under an earthquake scale by utilizing a Hash in-Shtrikman boundary average method; (5) simulating forwards an angular domain pre-stack earthquake record and a actual earthquake record to calculate a cost function and calculate acceptance probability of the elastic parameter model, and determining a new logging scale elastic parameter model according to the acceptance probability; (6) iterating repeatedly the steps of (3), (4) and(5) and determining an optimal logging scale elastic parameter mode as a final result of earthquake three-parameter pre-stack inversion; and (7) carrying out thin layer oil / gas-bearing possibility identification of an oil reservoir by utilizing a three-parameter three-dimensional space intersection method.

A method of grading an essay using an automated essay scoring system is provided. The method comprises the steps of deriving a set of predetermined features from the essay, wherein the predetermined feature set comprises one or more features that are independent from the test prompt, scoring the feature set with a scoring equation, wherein a multiple regression analysis with graded essay data produces weights for the scoring equation, generating a raw score for the essay; and processing the raw score for the essay into a score category based on an adaptive cutoff algorithm. Also provided is a method of generating a model in which to grade essays, wherein the data used to generate the model is independent from the test prompt or essay topic.

An output of a photovoltaic (PV) device is predicted by applying Fourier analysis to historical data to obtain frequencies and a mean of the frequencies in the data. Regression analysis is applied to the data to obtain a regression coefficient. Then, the prediction is a sum of the mean at the time step and a deviation from the mean at a previous time step, wherein the means are represented and approximated by selected frequencies, and the deviation for the previous time step is weighted by the regression coefficient.

A phishing webpage detection method based on Hungary matching algorithm is characterized by firstly extracting the text feature signatures, image feature signatures and general webpage feature signatures of the rendered webpages and more comprehensively depicting the features after access to webpages; and then computing the optimal matching of bipartite graphs by Hungary algorithm to search for the matched feature pairs among different webpage signatures and more objectively measuring the similarity among the webpages on the basis, thereby improving the phishing webpage detection efficiency. The method is also characterized by determining the inside weights of the text features, image features and global image features by utilizing the area under curve and determining the relative weightsamong the text similarity, image similarity and global image similarity during webpage similarity computation by utilizing logarithmic regression analysis. The precision and the recall rate are greatly improved in the method provided by the invention.

Machine executable method of analyzing growth curve data to identify the transition from a baseline phase into a growth phase. Applications of the method include analysis of results from time-dependent monitoring of amplicon synthesis in a nucleic acid amplification reaction to quantify a starting amount of a nucleic acid template in a test sample. The method advantageously simplifies the quantitation by circumventing the need to establish thresholds used for calculating initiation of the growth phase, to calculate derivatives, or to perform linear regression analysis.

A method of deriving a prediction model and calculating a predicted lens power to provide a desired post-operative spherical equivalent to correct myopia in a phakic eye of a patient using an intraocular lens includes measuring and determining the predictive significance of certain pre-operative characteristics of the eye, including cycloplegic and manifest spherical equivalent, vertex distance, anterior chamber depth, axial length, and keratometry. The prediction model is derived using multiple regression analysis on the pre-operative and post operative data. Measured data corresponding to a particular patient is used in the lens power prediction model to calculate the predicted lens power for implantation in the patient.

Methods and systems for establishing a contemporaneous Real Estate property valuations are disclosed. In accordance with one aspect of the invention, this is based upon first identifying a set of relevant value indicators, or Fields, and then producing a comprehensive Multiple Regression Analysis which statistically measures the relative value of Real Estate properties included in the Set.

The invention provides a scheduling method of a heat supply unit online load and a system. The scheduling method and the system are suitable for implementation of reasonable scheduling of a heat supply unit in a heating period. According to the method of the invention, based on the combination of real-time monitored data of parameters of the heat supply unit in a power grid, each heat supply index of a computer group, steam turbine variable working condition method and the actual operation status of the unit, an ordering power by heat mathematic model can be obtained, and therefore, power grid scheduling personnel can perform scheduling through adopting a minimum load mode, a load fast decreasing / increasing mode, and an energy-saving mode, and a basis can be provided for decision making in heat supply unit scheduling; according to weather forecast released by the meteorological department, and based on the combination of regression analysis results of historical data of the heat supply unit, weather, temperature and wind can be applied to heat supply quantity prediction; and according to predicted heat supply quantity, and based on the ordering power by heat mathematic model, electric power and electric quantity of a next day or a next month of the unit can be predicted.

The invention discloses an accelerated degradation test prediction method based on fuzzy theory, which comprises the following steps: collecting test data; performing the analysis of regression aimingat performance degradation data under each stress level; extrapolating the performance degradation rate of the product under a normal stress level; estimating a diffusion coefficient sigma in an excursion Brownian motion with drift by adopting a maximum likelihood estimation method; establishing an accelerated degradation test life and reliability predication model based on the fussy theory; andpredicting the life and the reliability of the product by adopting the fussy life and reliability prediction model. The method firstly introduces the fussy concept into an accelerated degradation testto enable the prediction result of the accelerated degradation test to be more reasonable, avoids the condition of rash routine reliability estimation result through considering the fussiness fuzziness of the performance degradation threshold, solves the problem of failed performance degradation in the engineering reality, and is suitable for the accelerated degradation tests of step stress and progressive stress and unaccelerated performance degradation prediction for the problem of the performance degradation failure.

A method for calibrating and analyzing data from a mass spectrometer, comprising the steps of acquiring raw profile mode data containing mass spectral responses of ions with or without isotopes; calculating theoretical isotope distributions for each of at least one calibration ion based on elemental composition; convoluting the theoretical isotope distributions with an initial peak shape function to obtain theoretical isotope profiles for each ion; constructing a peak component matrix including the theoretical isotope profiles for calibration ions as peak components; performing a regression analysis between the raw profile mode mass spectral data and the peak component matrix; and reporting the regression coefficients as the relative concentrations for each of the components. A mass spectrometry system operated in accordance with the method and a computer readable medium having program code thereon for performing the method.

Information presented to a user via an information access system is ranked according to a prediction of the likely degree of relevance to the user's interests. A profile of interests is stored for each user having access to the system. Items of information to be presented to a user are ranked according to their likely degree of relevance to that user and displayed in order of ranking. The prediction of relevance is carried out by combining data pertaining to the content of each item of information with other data regarding correlations of interests between users. A value indicative of the content of a document can be added to another value which defines user correlation, to produce a ranking score for a document. Alternatively, multiple regression analysis or evolutionary programming can be carried out with respect to various factors pertaining to document content and user correlation, to generate a prediction of relevance. The user correlation data is obtained from feedback information provided by users when they retrieve items of information. Preferably, the user provides an indication of interest in each document which he or she retrieves from the system.

A method of determining a volume of fluid associated with a fluid storage and dispensing system, the volume of fluid having a height in the system, wherein the system include measurement apparatus for measuring the height. The method includes collecting a plurality of height measurement data from the measurement apparatus in a form readable by a computer; storing the plurality of height measurement data in a compressed matrix format in a computer memory; and performing regression analysis using the compressed matrix format to calculate the volume of fluid associated with the system.

A system and related method for improving tire uniformity includes identifying at least one candidate process effect and a corresponding process harmonic number for each process effect. A given uniformity parameter, such as radial or lateral run-out, balance, mass variation, radial lateral or tangential force variation, is measured for each tire in a test set, such that the measurements contain tire harmonics as well as a process harmonics corresponding to each candidate process effect. Rectangular coordinate coefficients are electronically constructed for each said process harmonic, after which point the rectangular coordinates corresponding to each process harmonic are solved for (e.g., by using regression-based analysis). The magnitude of each said process harmonic is estimated, and a final magnitude estimate for each process harmonic can be determined by summarizing (e.g., by taking the average or median value) the respectively estimated magnitudes for each process harmonic across all test tires.