Searches , social queries for CONDITIONAL VARIANCE
Search references for CONDITIONAL VARIANCE. Phrases containing CONDITIONAL VARIANCE
See searches and references containing CONDITIONAL VARIANCE!
Search references for CONDITIONAL VARIANCE. Phrases containing CONDITIONAL VARIANCE
See searches and references containing CONDITIONAL VARIANCE!CONDITIONAL VARIANCE
Variance of a random variable given value of other variables
In probability theory and statistics, a conditional variance is the variance of a random variable given the value(s) of one or more other variables. Particularly
Conditional_variance
Type of financial derivative
A conditional variance swap is a type of variance swap or swap derivative product that allows investors to take exposure to volatility in the price of
Conditional_variance_swap
Theorem in probability theory
total variance is a fundamental result in probability theory that expresses the variance of a random variable Y in terms of its conditional variances and
Law_of_total_variance
Time series model
the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
Statistical measure of how far values spread from their average
In probability theory and statistics, variance is a measure of dispersion, meaning it is a measure of how far a set of numbers are spread out from their
Variance
Statistical property
all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Probability theory and statistics concept
corresponding names such as the conditional mean and conditional variance. More generally, one can refer to the conditional distribution of a subset of a
Conditional probability distribution
Conditional_probability_distribution
Expected value of a random variable given that certain conditions are known to occur
In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated
Conditional_expectation
Property of a model
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions
Bias–variance_tradeoff
Measure of covariance of components of a random vector
matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between
Covariance_matrix
Generalization of the one-dimensional normal distribution to higher dimensions
inverting back to get the conditional covariance matrix. Note that knowing that x2 = a alters the variance, though the new variance does not depend on the
Multivariate normal distribution
Multivariate_normal_distribution
testing (FAST) is a variance-based global sensitivity analysis method. The sensitivity value is defined based on conditional variances which indicate the
Fourier amplitude sensitivity testing
Fourier_amplitude_sensitivity_testing
Collection of statistical models
Analysis of variance (ANOVA) is a family of statistical methods used to compare the means of two or more groups by analyzing variance. Specifically, ANOVA
Analysis_of_variance
Statistical model
Hierarchical linear modeling Fixed effects MINQUE Covariance estimation Conditional variance Panel analysis Baltagi, Badi H. (2008). Econometric Analysis of Panel
Random_effects_model
Statistical model containing both fixed effects and random effects
the conditional variance of the outcome is not scalable to the identity matrix. When the conditional variance is known, then the inverse variance weighted
Mixed_model
Smooth function in statistics
statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean. The variance function is
Variance_function
Over-the-counter financial derivative
swap, conditional variance swap, corridor variance swap, forward-start variance swap, option on realized variance and correlation trading. "Variance and
Variance_swap
Measure of variation in statistics
data set or probability distribution is the square root of its variance (the variance being the average of the squared deviations from the mean). A useful
Standard_deviation
first, second or third conditional; there also exist "zero conditional" and mixed conditional sentences. A "first conditional" sentence expresses a future
Uses_of_English_verb_forms
expectation Conditional independence Conditional probability Conditional probability distribution Conditional random field Conditional variance Conditionality principle
List_of_statistics_articles
Risk arising from changes in market volatility affecting the value of financial positions
financial instruments. These are volatility swaps, variance swaps, conditional variance swaps, variance options, VIX futures for equities, and (with some
Volatility_risk
British eugenist, polymath, and behavioural geneticist (1822–1911)
chance, but rather that the regression coefficient, conditional variance, and population variance, were interdependent quantities related by a simple
Francis_Galton
Formula in probability theory
law of total variance. Some writers on probability call this the "conditional covariance formula" or use other names. Note: The conditional expected values
Law_of_total_covariance
Mathematical procedure for reducing the variance of statistical estimators
In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates
Variance_reduction
Middle quantile of a data set or probability distribution
the minimum-variance mean (for large normal samples), which is to say the variance of the median will be ~50% greater than the variance of the mean.
Median
Form of global sensitivity analysis
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis
Variance-based sensitivity analysis
Variance-based_sensitivity_analysis
Statistical model for a binary dependent variable
concerned with partitioning variance via the sum of squares calculations – variance in the criterion is essentially divided into variance accounted for by the
Logistic_regression
Sentences of the form "if x, then y"
headings zero conditional, first conditional (or conditional I), second conditional (or conditional II), third conditional (or conditional III) and mixed
English_conditional_sentences
Techniques to study geometric data
spatiotemporal dependence in the conditional variance of a process, extending the concept of Autoregressive conditional heteroskedasticity (ARCH) from time
Spatial_analysis
by the zoning ordinance. Such a variance has much in common with a special-use permit (sometimes known as a conditional use permit). Some municipalities
Variance_(land_use)
Probability of an event occurring, given that another event has already occurred
In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption
Conditional_probability
Statistical property
has its own mean and variance. Mathematically, the variance of the sampling mean distribution obtained is equal to the variance of the population divided
Standard_error
Statistical hypothesis test
statistical test that compares variances. It is used to determine if the variances of two samples, or if the ratios of variances among multiple samples, are
F-test
Linear regression model with a single explanatory variable
mean β and variance σ 2 / ∑ i ( x i − x ¯ ) 2 , {\textstyle \sigma ^{2}\left/\sum _{i}(x_{i}-{\bar {x}})^{2}\right.,} where σ2 is the variance of the error
Simple_linear_regression
Option pricing model
The transition probability p ( t , S t ) {\displaystyle p(t,S_{t})} conditional to S 0 {\displaystyle S_{0}} satisfies the forward Kolmogorov equation
Local_volatility
Ecological concept
genetic drift. In the Wright-Fisher idealized population model, the conditional variance of the allele frequency p ′ {\displaystyle p'} , given the allele
Effective_population_size
Statistical hypothesis test
t-tests, though strictly speaking that name should only be used if the variances of the two populations are also assumed to be equal; the form of the test
Student's_t-test
Difference between estimated transaction costs and the amount actually paid
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Slippage_(finance)
Probability distribution
inverse gamma distribution over the variance, and a normal distribution over the mean, conditional on the variance) and with the same four parameters just
Normal_distribution
Stochastic volatility model used in derivatives markets
Jaehyuk; Wu, Lixin (July 2021). "The equivalent constant-elasticity-of-variance (CEV) volatility of the stochastic-alpha-beta-rho (SABR) model". Journal
SABR_volatility_model
Normalized measure of the dispersion of a probability distribution
dispersion, dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized
Index_of_dispersion
Statistics model
from the previous iteration is used to supply estimates of the conditional variances, Var ( Y | X = x ) {\displaystyle \operatorname {Var} (Y|X=x)}
Linear_probability_model
Measure of the joint variability
behavior. The magnitude of the covariance is the geometric mean of the variances that are shared for the two random variables, where a larger magnitude
Covariance
Mathematical framework for investment risk
Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of financial assets such that the expected
Modern_portfolio_theory
Statistical estimation technique
a linear function of X {\displaystyle \mathbf {X} } and that the conditional variance of the error term given X {\displaystyle \mathbf {X} } is a known
Generalized_least_squares
Regression analysis technique
specified as a function θ(X). This implies that the conditional expectation and conditional variance of the observed fraction of successes, Y/n, are E (
Binomial_regression
Probability theory concept
hypothesis. It is the opposite of conditional dependence. Conditional independence is usually formulated in terms of conditional probability, as a special case
Conditional_independence
Form of computer-based test that adapts to the examinee's ability level
function of the discrimination parameter of the item, as well as the conditional variance and pseudo-guessing parameter (if used).[citation needed] After an
Computerized_adaptive_testing
Arbitrage strategy
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Basis_trading
Treasury basis trading: bond and futures arbitrage strategy
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Treasury_basis_trade
Concept in mathematical modelling
given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used. The complementary part of the total
Explained_variation
Unbiased statistical estimator minimizing variance
minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than
Minimum-variance unbiased estimator
Minimum-variance_unbiased_estimator
Concept in probability theory
probability is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which
Law_of_total_probability
German and American economist (born 1971)
American Economic Review, 2019) shows that the conditional mean and conditional variance of GDP growth are negatively correlated, so that lower quantiles
Tobias_Adrian
Class of statistical models
conditional on X; Xβ is the linear predictor, a linear combination of unknown parameters β; g is the link function. In this framework, the variance is
Generalized_linear_model
Bond issued by a corporation
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Corporate_bond
Method of data analysis
original variables that explains the most variance. The second principal component explains the most variance in what is left once the effect of the first
Principal_component_analysis
Econometric analysis of financial risk
choice under uncertainty. Autoregressive conditional heteroskedasticity models (ARCH) allow conditional variance to depend on past shocks, capturing volatility
Econometrics_of_risk
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
distribution) are considered low-variance, while those with CV > 1 (such as a hyper-exponential distribution) are considered high-variance[citation needed]. Some
Coefficient_of_variation
Form of funded credit derivative
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Credit-linked_note
Theorem in statistics
theorem. An example of this is to show that the sample mean and sample variance of a normal distribution are independent statistics, which is done in the
Basu's_theorem
Machine learning paradigm
regression (predicting a numerical value, e.g., a house price), and conditional density estimation (predicting the probability distribution of the output
Supervised_learning
Total amount of debt owed to lenders by a government/state
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Government_debt
General linear model that blends ANOVA and regression
decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance. Intuitively, ANCOVA
Analysis_of_covariance
Statistical modeling method
commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability
Linear_regression
Fundamental theorem in probability theory and statistics
with expected value (average) μ {\displaystyle \mu } and finite positive variance σ 2 {\displaystyle \sigma ^{2}} , and let X ¯ n {\displaystyle {\bar {X}}_{n}}
Central_limit_theorem
Statistical property
of transformations); for example, the sample variance is a biased estimator for the population variance. These are all illustrated below. An unbiased
Bias_of_an_estimator
Hypothetical interest rate on a risk-free investment
of the description of utility of stock holding to the expected mean and variance of the returns of the portfolio. In reality, there may be other utility
Risk-free_rate
Parameter estimation via sample statistics
estimate" of an unknown quantity, for example, the population mean, the variance of a distribution, or a model parameter (in a parametric model). Point
Point_estimation
Statistical framework for analyzing data from dyads
predictors; as predictors are added, comparing unconditional and conditional variance–covariance estimates can be used to quantify how much non-independence
Actor–partner interdependence model
Actor–partner_interdependence_model
Number of values in the final calculation of a statistic that are free to vary
intermediate steps in the estimation of the parameter itself. For example, if the variance is to be estimated from a random sample of N {\textstyle N} independent
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Type of probability distribution
other variables, and the conditional probability distribution giving the probabilities for any subset of the variables conditional on particular values of
Joint probability distribution
Joint_probability_distribution
Statistical method for resampling
therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such
Jackknife_resampling
Capital budgeting analysis term
Put–call parity, Vanna–Volga Swaps Amortising Asset Basis Commodity Conditional variance Constant maturity Correlation Credit default Currency Dividend Equity
Real_options_valuation
Measure of linear correlation
{\displaystyle r_{xy}} by substituting estimates of the covariances and variances based on a sample into the formula above. Given paired data { ( x 1 ,
Pearson correlation coefficient
Pearson_correlation_coefficient
Statistician and econometrician
jeconom.2009.11.002. Chib, Siddhartha; Greenberg, Edward (2013). "On conditional variance estimation in nonparametric regression" (PDF). Statistics and Computing
Siddhartha_Chib
Descriptions of properties of datasets
Engle, Robert F. (July 1982). "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation". Econometrica.
Homogeneity and heterogeneity (statistics)
Homogeneity_and_heterogeneity_(statistics)
Definite article in English
English Conditional sentences Copula Do-support Inversion Periphrasis Zero-marking Orthography Abbreviations Capitalization Comma Hyphen Variance African-American
The
Mathematical model for stochastic processes
where instead of the distribution of the response one specifies the conditional variance function, V a r ( Y ∣ X ) = σ 2 ( μ ) {\displaystyle {\rm {{Var}(Y\mid
Generalized functional linear model
Generalized_functional_linear_model
Risk reduction technique
perfect synchrony, a diversified portfolio will have less variance than the weighted average variance of its constituent assets, and often less volatility
Diversification_(finance)
Statistical theorem
kind of estimator of a parameter θ {\displaystyle \theta } , then the conditional expectation of δ ( X ) {\displaystyle \delta (X)} given T ( X ) {\displaystyle
Rao–Blackwell_theorem
Risk measure estimating the average loss in the worst tail of the distribution
shortfall is also called conditional value at risk (CVaR), average value at risk (AVaR), tail value at risk (TVaR), conditional tail expectation (CTE),
Expected_shortfall
Inverse of the average of the inverses of a set of numbers
Assuming that the variance is not infinite and that the central limit theorem applies to the sample then using the delta method, the variance is Var ( H
Harmonic_mean
Statistical property quantifying how much a collection of data is spread out
statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data
Statistical_dispersion
Approximation method in statistics
the errors have expectation zero conditional on the independent variables, are uncorrelated and have equal variances, the best linear unbiased estimator
Least_squares
Variance of random sum
(X_{1})} . The Blackwell-Girshick equation can be derived using conditional variance and variance decomposition. If the X i {\displaystyle X_{i}} are natural
Blackwell-Girshick_equation
Approach in data mining
conditional probability density p(y|x) from which the prediction using the conditional expected value can be obtained, with the conditional variance providing
Cluster-weighted_modeling
American academic
systems, ratio measures of fuzziness, the shape of fuzzy sets, the conditional variance of fuzzy systems, and the geometric view of (finite) fuzzy sets as
Bart_Kosko
Set of statistical processes for estimating the relationships among variables
have an expected value of zero, conditional on covariates: E ( e i | X i ) = 0 {\displaystyle E(e_{i}|X_{i})=0} The variance of the residuals e i {\displaystyle
Regression_analysis
Family of statistical methods based on sampling of available data
used in statistical inference to estimate the bias and standard error (variance) of a statistic, when a random sample of observations is used to calculate
Resampling_(statistics)
English embedded clause type marking non-real possibilities
in related languages, especially Old English and Latin. This includes conditional clauses, wishes, and reported speech. Modern descriptive grammars limit
English_subjunctive
Probability distribution
a normal family as a compound distribution when marginalizing over the variance parameter. Student's t distribution has the probability density function
Student's_t-distribution
Estimate of an interval in which future observations will fall
future observation X in a normal distribution N(μ,σ2) with known mean and variance may be calculated from γ = P ( ℓ < X < u ) = P ( ℓ − μ σ < X − μ σ < u
Prediction_interval
Monte Carlo algorithm
mean and variance, the conditional distribution of one node given the others after compounding out both the mean and variance will be a Student's t-distribution
Gibbs_sampling
Statistical relationship
variables of a numerical dataset normalized to the square root of their variances. Equivalently, Pearson's correlation coefficient can be calculated by
Correlation
Mathematical concept
definition of probability spaces gives rise to the natural concept of conditional probability. Every set A with non-zero probability (that is, P(A) > 0)
Probability_space
Averages of repeated trials converge to the expected value
The variance of the sum is equal to the sum of the variances, which is asymptotic to n 2 / log n {\displaystyle n^{2}/\log n} . The variance of the
Law_of_large_numbers
Method of statistical inference
importance of conditional probability by writing "I wish to call attention to ... and especially the theory of conditional probabilities and conditional expectations
Bayesian_inference
Fourth standardized moment in statistics
For non-normal samples, the variance of the sample variance depends on the kurtosis; for details, please see variance. Pearson's definition of kurtosis
Kurtosis
Estimator for quality of a statistical model
(with zero mean), then the model has three parameters: b0, b1, and the variance of the Gaussian distributions. Thus, when calculating the AIC value of
Akaike_information_criterion
travel, tourism, insurance
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
CONDITIONAL VARIANCE
travel, tourism, insurance