The likelihood ratio criterion and the asymptotic expansion of its distribution

Summary Asymptotic expansion of the distribution of the likelihood ratio criterion (LRC) for testing a composite hypothesis is derived under null hypothesis and a correction factor ρ which makes the term of order 1/n in the asymptotic expansion of the distribution of it vanish is obtained. The problem is extended to the case of a general composite hypothesis and of Pitman's local alternatives. The asymptotic distribution of LRC for a simple hypothesis is studied under a fixed alternative. The Institute of Statistical Mathematics     

Some applications of the asymptotic distribution of likelihood functions to the asymptotic efficiency of estimates

Summary In the present paper, a Markov process is considered whose distribution depends on a k-dimensional parameter . Under suitable regularity conditions on the process, which include the differentiability in quadratic mean of a certain random function, attention is first focused to the class of estimates of which, properly normalized, are asymptotically normal. Within this class the subclass of estimates of which are asymptotically efficient is characterized. As a criterion of asymptotic efficiency we use the covariance of the asymptotic distribution of the estimates.Secondly, a class of estimates of is considered defined by the requirement that the estimates, properly normalized, have an asymptotic distribution which need not be normal. Again within this class the subclass of asymptotically efficient estimates of is characterized. The adopted criterion of asymptotic efficiency here is the asymptotic concentration of the estimates about certain hyperplanes.Finally, the above results are specialized to the independent identically distributed case.This paper was part of an invited address presented at the annual meeting of the Institute of Mathematical Statistics in Madison, Wisconsin, August 27–30, 1968.I would like to thank my colleague R. A. Johnson for constructive discussions in connection with this paper.     

The asymptotic distribution of the likelihood ratio for autoregressive time series with a regression trend

It is shown that the likelihood ratio of an autoregressive time series of finite order with a regression trend is asymptotically normal. This result is used to derive the power of a test for positive correlation of the residuals under local autoregressive alternatives. The test is based on the Durbin-Watson statistics.     

An asymptotic expansion for the distribution of asymptotic maximum likelihood estimators of vector parameters

It is shown that the probability that a suitably standardized asymptotic maximum likelihood estimator of a vector parameter (i.e., an estimator which approximates the solution of the likelihood equation in a reasonably good way) lies in a measurable convex set can be approximated by an integral involving a multidimensional normal density function and a series in $\text{n}{}^{\mathrm{<mtext>?1</mtext><mtext>2</mtext>}}$ with certain polynomials as coefficients.     

The distribution of the likelihood ratio for additive processes

Let {X(t), 0 ≤ t ≤ T} and {Y(t), 0 ≤ t ≤ T} be two additive processes over the interval [0, T] which, as measures over D[0, T], are absolutely continuous with respect to each other. Let μ_X and μ_Y be the measures over D[0, T] determined by the two processes. The characteristic function of $\text{ln}\text{(}\text{dμ}{}_{\mathrm{X}}\text{dμ}{}_{\mathrm{Y}}\text{)}$ with respect to μ_Y is obtained in terms of the determining parameters of the two processes.     

Local asymptotic normality for progressively censored likelihood ratio statistics and applications

Let X_n,1 ≤ X_n,2 ≤ … ≤ X_n,n be the ordered variables corresponding to a random sample of size n with respect to a family of probability measures {P_θ:θ ∈ Θ} where Θ is an open subset of the real line. In many practical situations the X_n,i are the observables and experimentation must be curtailed prior to X_n,n. If τ_n is a stopping variable adapted to the σ-fields {σ(X_n,1,…,X_n,k): 1 ≤ k ≤ n} and P_n,θ the projection of P_θ onto σ(X_n,1,…,X_n,τn), the local asymptotic normality of the stopped progressively censored likelihood ratio statistics is established with θ, $\text{θ}{}_{\mathrm{n}}\text{= θ + un}{}^{\mathrm{<mtext>?1</mtext><mtext>2</mtext>}}\text{∈ Θ}$ and θ, u held fixed, under certain conditions on the underlying distribution and on τ_n. Conditions are also given to ensure the local asymptotic normality of likelihood ratio statistics where the underlying observations are given in a series scheme.     

THE ASYMPTOTIC DISTRIBUTIONS OF EMPIRICAL LIKELIHOOD RATIO STATISTICS IN THE PRESENCE OF MEASUREMENT ERROR

Suppose that several different imperfect instruments and one perfect instrument are independently used to measure some characteristics of a population. Thus, measurements of two or more sets of samples with varying accuracies are obtained. Statistical inference should be based on the pooled samples. In this article, the authors also assumes that all the imperfect instruments are unbiased. They consider the problem of combining this information to make statistical tests for parameters more relevant. They define the empirical likelihood ratio functions and obtain their asymptotic distributions in the presence of measurement error.     

The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption

This paper examines asymptotic distributions of the likelihood ratio criteria, which are proposed under normality, for several hypotheses on covariance matrices when the true distribution of a population is a certain nonnormal distribution. It is well known that asymptotic distributions of test statistics depend on the fourth moments of the true population's distribution. We study the effects of nonnormality on the asymptotic distributions of the null and nonnull distributions of likelihood ratio criteria for covariance structures.     

Characterization of asymptotic distribution functions of ratio block sequences

In this paper we give necessary and sufficient conditions for the block sequence of the set X = {x ₁ < x ₂ < … < x _n < …} ⊂ ℕ to have an asymptotic distribution function in the form x ^λ.     

On the local asymptotic behavior of the likelihood function for Meixner Lévy processes under high-frequency sampling

We discuss the local asymptotic behavior of the likelihood function associated with all the four characterizing parameters (α,β,δ,μ) of the Meixner Lévy process under high-frequency sampling scheme. We derive the optimal rate of convergence for each parameter and the Fisher information matrix in a closed form. The skewness parameter β exhibits a slower rate alone, relative to the other three parameters free of sampling rate. An unusual aspect is that the Fisher information matrix is constantly singular for full joint estimation of the four parameters. This is a particular phenomenon in the regular high-frequency sampling setting and is of essentially different nature from low-frequency sampling. As soon as either α or δ is fixed, the Fisher information matrix becomes diagonal, implying that the corresponding maximum likelihood estimators are asymptotically orthogonal.     

On the ratio integral formula for the likelihood ratio of a maximal invariant

The usual ratio of an integral formula for the likelihood ratio of a maximal invariant in a group model is shown to be correct under assumption that the denominator integral is finite almost everywhere. The limitation of this assumption is discussed, and an application to invariant suffciency is given.     

On regularity conditions for complementarity problems

In the context of complementarity problems, various concepts of solution regularity are known, each of them playing a certain role in the related theoretical and algorithmic developments. Despite the existence of rich literature on this subject, it appears that the exact relations between some of these regularity concepts remained unknown. In this note, we not only summarize the existing results on the subject but also establish the missing relations filling all the gaps in the current understanding of how different regularity concepts relate to each other. In particular, we demonstrate that strong regularity is in fact equivalent to nonsingularity of all matrices in the natural outer estimates of the generalized Jacobians of the most widely used residual mappings for complementarity problems. On the other hand, we show that CD-regularity of the natural residual mapping does not imply even BD-regularity of the Fischer–Burmeister residual mapping. As a result, we provide the complete picture of relations between the most important regularity conditions for mixed complementarity problems, with a special emphasis on those conditions used to justify the related numerical methods. A special attention is paid to the particular cases of a nonlinear complementarity problem and of a Karush–Kuhn–Tucker system.     

On asymptotic distribution on the a-adic integers

We show that the values of a polynomial with a-adic coefficients at integer and rational prime arguments are asymptotically distributed on the a-adic integers and that the integer parts of certain sequences known to be uniformly distributed modulo one, are uniformly distributed on the a-adic integers.