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959 results about "Regression analysis" patented technology

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear function) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared distances between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression).

Method for predicting the onset or change of a medical condition

InactiveUS20050119534A1Minimizes adverse reactionMaximize therapeutic responseDrug and medicationsSurgeryMedical recordCost effectiveness
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.

Scheduling method of heat supply unit online load and system

ActiveCN103412526AMastering Thermoelectric PropertiesAvoid the problem of needing to increase the generation loadTotal factory controlProgramme total factory controlRegression analysisPower grid
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.

Accelerated degradation test prediction method based on fuzzy theory

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.

Intelligent early-warning analysis method for meteorological severe convection weather based on machine learning

The invention discloses an intelligent early-warning method for meteorological severe convection weather based on machine learning. The method comprises the steps of receiving severe convection weather early-warning basic data through a server, wherein the basic data comprise lightning positioning data, weather electric field data, automatic station meteorological essential factor real-time data, radar real-time radix data and the like, and supplying a local severe convection weather early-warning threshold for each area according to historical meteorological data based on a regression analysis method; obtaining real-time meteorological comprehensive data through big data regression analysis calculation of various meteorological essential factors, comparing the real-time meteorological comprehensive data with an early-warning threshold, and generating early-warning information. According to the intelligent early-warning method, tracing and data back-pushing are performed on meteorological historical data by means of the regression analysis method of machine learning; dynamic calculation and intelligent adjustment are performed according to a context threshold; data comparison is performed on a radar back-pushing result and automatic station meteorological essential factor real-time data; identification and early-warning are performed on the severe convection weather based on machine learning; and accurate early-warning for the severe convection weather in a preset area is realized.
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