List of important publications in statistics
... emphasizing hypothesis testing via likelihood ratio tests and the properties of power functions: Admissiblity, unbiasedness and ... Statistical Decision Functions Author: Abraham Wald Publication data: 1950. John Wiley & Sons. Description: Exposition of ... JSTOR 2281868 Description: First description of the now ubiquitous Kaplan-Meier estimator of survival functions from data with ... Aldrich, John (1997). "R.A. Fisher and the making of maximum likelihood 1912-1922". Statistical Science. 12 (3): 162-176. doi: ...
Likelihoodfunction
When considered a function of N for fixed n, this is a likelihood function. The maximum likelihood estimate for N is n (by ... In statistics, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model given ... In such a situation, the likelihood function factors into a product of individual likelihood functions. The logarithm of this ... The relative likelihood is closely related to the likelihood ratio used in the likelihood-ratio test. The likelihood ratio is ...
NumXL
Mixed model - likelihood function. Goodness of fit - LLF, AICc, and model's diagnosis. Interpolation functions - Flat forward- ... and partial autocorrelation function (PACF). Cross-correlation functions - XCF and EWXCF MD, RMD, MAD Statistical functions - ... Goodness of fit - Likelihood function (LLF), Akaike information criterion (AICc) and model's diagnosis Simple linear regression ... Likelihood function (LLF), Akaike information criterion (AICc) and model's diagnosis. Forecast and back-testing Simulation ...
Thompson sampling
The elements of Thompson sampling are as follows: a likelihood function P ( r , θ , a , x ) {\displaystyle P(r,\theta ,a,x)} ; ... is the likelihood function. Thompson sampling consists in playing the action a ∗ ∈ A {\displaystyle a^{\ast }\in {\mathcal {A ... likelihoods of the actions a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} , and then by sampling the action a T ... likelihoods of the observations o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} and ignoring the (causal) ...
Deviance information criterion
... is the likelihood function. C {\displaystyle C\,} is a constant that cancels out in all calculations that compare different ... AIC and BIC require calculating the likelihood at its maximum over θ {\displaystyle \theta \,} , which is not readily available ... Note, that the p in this expression is the predictive distribution rather than the likelihood above. Akaike information ...
Whittle likelihood
In statistics, Whittle likelihood is an approximation to the likelihood function of a stationary Gaussian time series. It is ... In a stationary Gaussian time series model, the likelihood function is (as usual in Gaussian models) a function of the ... likelihood function weighted least squares matched filter discrete Fourier transform power spectral density coloured noise ... Hurvich, C. (2002). "Whittle's approximation to the likelihood function" (PDF). NYU Stern. Calder, M.; Davis, R. A. (1997), "An ...
Fisher information
... or probability mass function) for X conditional on the value of θ. This is also the likelihood function for θ. It describes the ... the likelihood function is a probability density function, and therefore ∫ f d x = 1 {\displaystyle \int f\,dx=1} . A basic ... Formally, the partial derivative with respect to θ of the natural logarithm of the likelihood function is called the "score". ... Statistical systems of a scientific nature (physical, biological, etc.) whose likelihood functions obey shift invariance have ...
Probability
These data are incorporated in a likelihood function. The product of the prior and the likelihood, normalized, results in a ... The first law was published in 1774 and stated that the frequency of an error could be expressed as an exponential function of ... The second law of error was proposed in 1778 by Laplace and stated that the frequency of the error is an exponential function ... The objective wave function evolves deterministically but, according to the Copenhagen interpretation, it deals with ...
Oscar Kempthorne
... neo-Fisherian statistics emphasizes likelihood functions of parameters. Second, Kempthorne was skeptical of Bayesian statistics ... which use not only likelihoods but also probability distributions on parameters. Nonetheless, while subjective probability and ...
Gamma distribution
The likelihood function for N iid observations (x1, ..., xN) is L ( k , θ ) = ∏ i = 1 N f ( x i ; k , θ ) {\displaystyle L(k,\ ... is the gamma function evaluated at k. The cumulative distribution function is the regularized gamma function: F ( x ; k , θ ... The cumulative distribution function is the regularized gamma function: F ( x ; α , β ) = ∫ 0 x f ( u ; α , β ) d u = γ ( α , β ... from which we calculate the log-likelihood function ℓ ( k , θ ) = ( k − 1 ) ∑ i = 1 N ln ⁡ ( x i ) − ∑ i = 1 N x i θ − N k ln ...
Negative binomial distribution
The likelihood function for N iid observations (k1, ..., kN) is L ( r , p ) = ∏ i = 1 N f ( k i ; r , p ) {\displaystyle L(r,p ... prod _{i=1}^{N}f(k_{i};r,p)\,\!} from which we calculate the log-likelihood function ℓ ( r , p ) = ∑ i = 1 N ln ⁡ ( Γ ( k i + r ... mass function and obtain the following mass function of the distribution of houses (for n ≥ 5): f ( n ) = ( ( n − 5 ) + 5 − 1 n ... The cumulative distribution function can be expressed in terms of the regularized incomplete beta function: f ( k ; r , p ) ≡ ...
Approximate Bayesian computation
ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which ... All ABC based methods approximate the likelihood function by simulations, the outcomes of which are compared with the observed ... For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models ... In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability ...
Bayesian inference in marketing
This is done through the calculation shown below, where P ( D , H ) {\displaystyle P(D,H)} is the likelihood function. This ... which states that models or inferences for datasets leading to the same likelihood function should generate the same ... "Statistical Decision Functions", in: Kotz, S. And Johnson, N. L. (Eds.) (1992). Breakthroughs in Statistics: Foundations and ... is misread by judging A's likelihood by how well the evidence X matches A, but crucially without considering the prior ...
Bayesian linear regression
The prior belief about the parameters is combined with the data's likelihood function according to Bayes theorem to yield the ... Here, the model is defined by the likelihood function p ( y , X , β , σ ) {\displaystyle p(\mathbf {y} ,\mathbf {X} ,{\ ... A prior ρ ( β , σ 2 ) {\displaystyle \rho ({\boldsymbol {\beta }},\sigma ^{2})} is conjugate to this likelihood function if it ... denotes the gamma function. Because we have chosen a conjugate prior, the marginal likelihood can also be easily computed by ...
Unified neutral theory of biodiversity
For this purpose, the approptiate likelihood function should be used. For the metacommunity this was given above. For the local ... Then the formula above would allow us to assess the likelihood of different values of θ. There are thus S = 3 species and ϕ 1 ... is the gamma function, and γ = ( J − 1 ) m / ( 1 − m ) {\displaystyle \gamma =(J-1)m/(1-m)} . This formula is however an ... The maximum likelihood estimate for θ is about 1.1478. We could have labelled the species another way and counted the ...
Monotone likelihood ratio
Monotone likelihood functions are used to construct uniformly most powerful tests, according to the Karlin-Rubin theorem. ... This task is simplified if the family has the monotone likelihood ratio property (MLRP). A family of density functions { f θ ( ... In statistics, the monotone likelihood ratio property is a property of the ratio of two probability density functions (PDFs). ... H_{1}:\theta >\theta _{0}.} Monotone likelihood-functions are used to construct median-unbiased estimators, using methods ...
Theta
The statistical parameter frequently used in writing the likelihood function. The Watterson estimator for the population ... A special function of several complex variables. One of the Chebyshev functions in prime number theory. The potential ... The ordinal collapsing function developed by Solomon Feferman The upper-case letter Θ is used as a symbol for: Quantity or ...
Bayesian information criterion
It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC). When ... That is, the integral of the likelihood function p ( x , θ , M ) {\displaystyle p(x,\theta ,M)} times the prior probability ... The BIC is an increasing function of the error variance σ e 2 {\displaystyle \sigma _{e}^{2}} and an increasing function of k. ... the maximized value of the likelihood function of the model M {\displaystyle M} , i.e. L ^ = p ( x , θ ^ , M ) {\displaystyle ...
Median
Such constructions exist for probability distributions having monotone likelihood-functions. One such procedure is an analogue ... A C function is a real valued function, defined on the set of real numbers R, with the property that for any real t f − 1 ... The first and third inequalities come from Jensen's inequality applied to the absolute-value function and the square function, ... In practice, the function f ( v ) {\displaystyle f(v)} will often not be known but it can be estimated from an observed ...
Maximum likelihood estimation
Implementing MLE for your own likelihood function using R A selection of likelihood functions in R Myung, I. J. (2003). " ... A maximum likelihood estimator is an extremum estimator obtained by maximizing, as a function of θ, the objective function (c.f ... Since the logarithm function itself is a continuous strictly increasing function over the range of the likelihood, the values ... The method of maximum likelihood is based on the likelihood function. We are given a statistical model, i.e. a family of ...
Robert Wedderburn (statistician)
Wedderburn, RWM (1974). "Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method". Biometrika. 61 (3 ... and then expanded this subject to develop the idea of quasi-likelihood. Wedderburn was born in Edinburgh, where he attended ...
Multiple-try Metropolis
... is the likelihood function. Define w ( x , y ) = π ( x ) Q ( x , y ) λ ( x , y ) {\displaystyle w(\mathbf {x} ,\mathbf {y} )=\ ... Suppose Q ( x , y ) {\displaystyle Q(\mathbf {x} ,\mathbf {y} )} is an arbitrary proposal function. We require that Q ( x , y ... is a non-negative symmetric function in x {\displaystyle \mathbf {x} } and y {\displaystyle \mathbf {y} } that can be chosen by ... "A multi-point Metropolis scheme with generic weight functions". Statistics & Probability Letters. 82 (7): 1445-1453. doi: ...
Mode choice
... this is our likelihood function. The likelihood function for n independent observations in a logit model is L ∗ = ∏ n = 1 N P i ... The MNL approach is to make a maximum likelihood estimate of this functional form. The likelihood function is: L ∗ = ∏ n = 1 N ... The log-likelihood function is maximized setting the partial derivatives to zero: ∂ ℓ ∂ β = ∑ i = 1 n ( Y i − P ^ i ) = 0 {\ ... As noted above, we think of observable utility as being a function: v A = β 0 + β 1 ( c A − c T ) + β 2 ( t A − t T ) + β 3 I ...
Variance function
With a specified variance function and link function we can develop, as alternatives to the log-likelihood function, the score ... In general, maximum likelihood estimation requires that a likelihood function be defined. This requirement then implies that ... In addition, we describe the applications and use of variance functions in maximum likelihood estimation and quasi-likelihood ... In statistics, the variance function is a smooth function which depicts the variance of a random quantity as a function of its ...
Wrapped Cauchy distribution
page needed] J. Copas (1975). "On the unimodality of the likelihood function for the Cauchy distribution". Biometrika. 62 (3): ... Expressing the above pdf in terms of the characteristic function of the Cauchy distribution yields: f W C ( θ ; μ , γ ) = 1 2 π ... The maximum-likelihood estimate for the median ( μ ^ {\displaystyle {\hat {\mu }}} ) and scale parameter ( γ ^ {\displaystyle ... Ferguson, Thomas S. (1978). "Maximum Likelihood Estimates of the Parameters of the Cauchy Distribution for Samples of Size 3 ...
Uncertainty quantification
Module 2: Gaussian process modeling for the discrepancy function Similarly with the first module, the discrepancy function is ... one can derive the maximum likelihood estimates for { β δ , σ δ , ω k δ , k = 1 , … , d } {\displaystyle \left\{{\boldsymbol {\ ... denotes the additive discrepancy function (aka bias function), and ε {\displaystyle \varepsilon } denotes the experimental ... The objective is to estimate the discrepancy function δ ( x ) {\displaystyle \delta (\mathbf {x} )} , and as a by-product, the ...
CUSUM
... represents the likelihood function, but this is common usage. Note that this differs from SPRT by always using zero function as ... CUSUM does not require the use of the likelihood function. As a means of assessing CUSUM's performance, Page defined the ...