arXiv subjects

Nils Lid Hjort

Publications and source records attributed to Nils Lid Hjort.

At least 37 records · Page 2Linked to original sources

arXiv2026

Focused Information Criteria for the Linear Hazard Regression Model

The linear hazard regression model developed by Aalen is becoming an increasingly popular alternative to the Cox multiplicative hazard regression model. There are no methods in the literature for selecting among different candidate models of this nonparametric type, however. In the present paper a focused information criterion is developed for this task. The criterion works for each specified covariate vector, by estimating the mean squared error for each candidate model's estimate of the associated cumulative hazard rate; the finally selected model is the one with lowest estimated mean squared error. Averaged versions of the criterion are also developed.

stat.ME↗

arXiv2026

Bayesian approaches to non- and semiparametric density estimation [with a rejoinder to my discussants]

This invited paper proposes and discusses several Bayesian attempts at nonparametric and semiparametric density estimation. The main categories of these ideas are as follows: 1) Build a nonparametric prior around a given parametric model. We look at cases where the nonparametric part of the construction is a Dirichlet process or relatives thereof. (2) Express the density as an additive expansion of orthogonal basis functions, and place priors on the coefficients. Here attention is given to a certain robust Hermite expansion around the normal distribution. Multiplicative expansions are also considered. (3) Express the unknown density as locally being of a certain parametric form, then construct suitable local likelihood functions to express information content, and place local priors on the local parameters.

math.ST↗

arXiv2026

ML, PL, QL in Markov chain models

In many spatial and spatial-temporal models, and more generally in models with complex dependencies, it may be too difficult to carry out full maximum likelihood (ML) analysis. Remedies include the use of pseudo-likelihood (PL) and quasi-likelihood (QL) (also called the composite likelihood). The present article studies the ML, the PL and the QL methods for general Markov chain models, partly motivated by the desire to understand the precise behaviour of PL and QL methods in settings where this can be analysed. We present limiting normality results and compare performances in different settings. The PL and QL methods can be seen as maximum penalised likelihood methods. We find that the QL strategy is typically preferable to the PL, and that it loses very little to the ML, while earning in model robustness. It has also appeal and potential as a modelling tool. Our methods are illustrated for analysis of DNA sequence evolution type models.

stat.ME↗

arXiv2026

And Quiet Does Not Flow the Don: Statistical Analysis of a Quarrel Between Nobel Prize Laureates

The Nobel Prize in literature 1965 was awarded Mikhail Sholokhov (1905-1984), for the epic novel Tikhij Don about Cossack life and the birth of a new Soviet society (And Quiet Flows the Don, or The Quiet Don, in different translations). Sholokhov has been compared to Tolstoy and was at least one and two generations ago called `the greatest of our writers' in the Soviet Union. In Russia alone his books have been published in more than a thousand editions, selling in total more than sixty million copies. He was an elected member of the USSR Supreme Soviet, the USSR Academy of Sciences, and of the CPSU Central Committee. But in the autumn of 1974 an article was published in Paris, Stremya `Tihogo Dona' (Zagadki romana (`The Rapids of Quiet Don: the Enigmas of the Novel'), by the author and critic D$^*$. He claimed that Tikhij Don was not at all Sholokhov's work, but that it rather was written by Fiodor Kriukov, a more obscure author who fought against bolshevism and died in 1920. The article was given credibility and prestige by none other than Aleksandr Solzhenitsyn (a Nobel prize winner five years after Sholokhov), who wrote a preface giving full support to D$^*$'s conclusion. Scandals followed, also touching the upper echelons of Soviet society, and Sholokhov's reputation was faltering abroad (see e.g. Doris Lessing's (1997) comments; `vibrations of dislike instantly flowed between us'). Are we in fact faced with one of the most flagrant cases of plagiarism in the history of literature?

stat.AP↗

arXiv2026

Locally parametric nonparametric density estimation

This paper develops a nonparametric density estimator with parametric overtones. Suppose $f(x,θ)$ is some family of densities, indexed by a vector of parameters $θ$. We define a local kernel smoothed likelihood function which for each $x$ can be used to estimate the best local parametric approximant to the true density. This leads to a new density estimator of the form $f(x,\hatθ(x))$, thus inserting the best local parameter estimate for each new value of $x$. When the bandwidth used is large this amounts to ordinary full likelihood parametric density estimation, while for moderate and small bandwidths the method is essentially nonparametric, using only local properties of data and the model. Alternative ways more general than via the local likelihood are also described. The methods can be seen as ways of nonparametrically smoothing the parameter within a parametric class. Properties of this new semiparametric estimator are investigated. Our preferred version has approximately the same variance as the ordinary kernel method but potentially a smaller bias. The new method is seen to perform better than the traditional kernel method in a broad nonparametric vicinity of the parametric model employed, while at the same time being capable of not losing much in precision to full likelihood methods when the model is correct. Other versions of the method are equivalent to using particular higher order kernels in a semiparametric framework. The methodology we develop can be seen as the density estimation parallel to local likelihood and local weighted least squares theory in nonparametric regression.

stat.ME↗

arXiv2026

Bayesian analysis for a generalised Dirichlet process prior

A family of random probabilities is defined and studied. This family contains the Dirichlet process as a special case, corresponding to an inner point in the appropriate parameter space. The extension makes it possible to have random means with larger or smaller skewnesses as compared to skewnesses under the Dirichlet prior, and also in other ways amounts to additional modelling flexibility. The usefulness of such random probabilities for nonparametric Bayesian statistics is discussed. The posterior distribution is complicated, but inference can nevertheless be carried out via simulation, and some exact formulae are derived for the case of random means. The class of nonparametric priors provides an instructive example where the speed with which the posterior forgets its prior with increasing data sample size depends on special aspects of the prior, which is a different situation from that of parametric inference.

math.ST↗

arXiv2026

Bayesian bivariate survival estimation

There is no easy extension of Kaplan-Meier and Nelson-Aalen estimators to the bivariate case, and estimating bivariate survival distributions nonparametrically is associated with various non-trivial problems. The Dabrowska estimator will for example associate negative mass to some subsets. Bayesian methods hold some promise as they will avoid the negative mass problem, butare also prone to difficulties. We simplify and extend an example by Pruitt to show that the posterior distribution from a Dirichlet process prior is inconsistent. We construct a different nonparametric prior via Beta processes and provide an updating scheme that utilizes only the most relevant parts of the likelihood, and show that this leads to a consistent estimator.

math.ST↗

arXiv2026

A note on kernel density estimators with optimal bandwidths

We show that the cumulative distribution function corresponding to a kernel density estimator with optimal bandwidth lies outside any confidence interval, around the empirical distribution function, with probability tending to 1 as the sample size increases.

math.ST↗

arXiv2026

Overdispersed and Markovian Children

Take a look around you -- in your family, your school or workplace, in the streets, and you see boys & girls in about equal proportion, and without any easily visible gender patterns in case of siblings. So, to the famous first order of statistical approximation, we're all the results of hierarchical cascades of independent coin tosses through history, with each little fate determined by a 0.50-0.50 coin. This is not entirely correct, as one discovers with careful analysis and enough data: the coins of fate are (a little) imbalanced; they vary (a little) from family to family; there is a (slight) dependence in your children's gender sequence; and there are (slightly) more only-girls and only-boys families than predicted from binomial conditions. In this article I use the opportunity to talk also about how sample sizes influence p-values and statistical detection power.

stat.AP↗

arXiv2026

The asymptotic effect of tuning parameters

Tuning parameters are parameters involved in an estimating procedure for the purpose of reducing the risk of some other estimator. Examples include the degree of penalization in penalized regression and likelihood problems, as well as the balance parameter in hybrid methods. Typically tuning parameters are set to the minimizers of some estimator of the risk, a step which introduces additional randomness and makes standard methodology inapplicable. We derive precise asymptotic theory for this situation. Our framework allows for smooth, but otherwise arbitrary, loss functions and for the risk to be estimated by cross-validation procedures. Results include consistency of the optimal estimator towards a well-defined quantity and asymptotic normality after proper scaling and centring. We give explicit forms and estimators for the limiting variance matrix and results sharply characterizing the distance from the training error to the cross-validated estimator of the risk.

math.ST↗

arXiv2026

The exact amount of t-ness that the normal model can tolerate

Suppose that the normal model is used for data $Y_1,\ldots,Y_n$, but that the true distribution is a t-distribution with location and scale parameters $ξ$ and $σ$ and $m$ degrees of freedom. The normal model corresponds to $m=\infty$. Using a local asymptotic framework where $m$ is allowed to increase with $n$ two classes of estimands are identified. One small class, which in particular contains the functions of $ξ$ alone, is only affected by t-ness to the second order, and maximum likelihood estimation in the two- or three-parameter models become equivalent. For all other estimands it is shown that if $m\ge1.458\sqrt{n}$, then maximum likelihood estimation using the incorrect normal model is still more precise than using the correct three-parameter model. This is furthermore shown to be true in regression models with t-distributed residuals. We also propose and analyse compromise estimators that in various ways interpolate between the normal and the nonnormal models. A separate section extends the t-ness results to general normal scale mixtures, in which case the tolerance radius around the normal error distribution takes the form of an upper bound $0.3429/\sqrt{n}$ for the variance of the scale mixture distribution. Proving our results requires somewhat nonstandard `corner asymptotics' since behaviour of estimators must be studied when the crucial parameter $γ=1/m$ is close to zero, which is not an inner point of the parameter space, and the maximum likelihood estimator of $m$ is equal to $\infty$ with positive probability.

stat.ME↗

arXiv2026

Estimation in moderately misspecified models

Suppose data are fitted to some parametric model but that the true model happens to be one with an additional parameter. When a parameter is to be estimated one can use likelihood estimation in the wider model or in the narrow model. Including the extra parameter in the model means less bias but larger sampling variability. Two basic questions are addressed in this article. (i) Just how much misspecification can the narrow model tolerate? In the context of a large-sample moderate misspecification framework we find a surprisingly simple, sharp, and general answer. There is effectively a `tolerance radius' around a given narrow model, inside of which narrow estimation is more precise than wide estimation for all estimands. This is computed in a selection of examples that also demonstrate the degree of robustness of important standard methods against moderate incorrectness of the model under which they are optimal. (ii) Are there other estimators that work well both under narrow and wide circumstances? We discuss several possibilities and propose some new procedures. All methods are compared in a broad large-sample performance study.

stat.ME↗

arXiv2026

Normalised Local Hazard Plots

The purpose of this paper is to develop and illustrate certain classes of graphical plots that can be used for model verification in quite general survival data and life history data models. By suitably comparing nonparametric and parametric estimates of hazard rate functions over time a hazard comparison function can be constructed which under parametric model assumptions is approximately a zero-mean normal process. The test curves we propose are locally normalised versions of such hazard comparison functions. Under model conditions the test function is approximately a standard normal for each time point. This makes the normalised local hazard curves easy to interpret.We give explicit constructions for the most commonly used models of survival analysis, including the exponential, the Weibull, the Gompertz, the gamma, and for parametric Cox regression. Algorithms carrying this out have been developed in Splus. Various theoretical and practical issues are discussed, including detection power and extensions to time-discrete models. Illustrations are given on simulated and real data.

stat.ME↗

arXiv2026

On inference in parametric survival data models

The usual parametric models for survival data are of the following form. Some parametrically specified hazard rate $α(s,θ)$ is assumed for possibly censored random life times $X_1^0,\ldots,X_n^0$; one observes only $X_i=\min\{X_i^0,c_i\}$ and $δ_i=I\{X_i^0\le c_i\}$ for certain censoring times $c_i$ that either are given or come from some censoring distribution. We study the following problems: What do the maximum likelihood estimator and other estimators really estimate when the true hazard rate $α(s)$ is different from the parametric hazard rates? What is the limit distribution of an estimator under such outside-the-model circumstances? How can traditional model-based analyses be made model-robust? Does the model-agnostic viewpoint invite alternative estimation approaches? What are the consequences of carrying out model-based and model-robust bootstrapping? How do theoretical and empirical influence functions generalise to situations with censored data? How do methods and results carry over to more complex models for life history data like regression models and Markov chains?

stat.ME↗

arXiv2026

Sometimes nonparametrics beat parametrics, even when the model is right

A basic issue in both teaching of and practice of statistics is the interplay between modelling assumptions and inference performance. The general message conveyed is that stronger assumptions lead to better statistical performance of the relevant estimators, tests and confidence intervals, provided that these assumptions hold. On the other hand, fewer assumptions often lead to safer and more robust methods that are good also outside narrow conditions, but not quite as good as specialist methods that exploit such narrower conditions, if these are fulfilled. This interplay is nicely illustrated in the context of density estimation, where parametric and nonparametric methods can be contrasted. The parametric ones have mean squared errors of size $O(n^{-1})$ in terms of sample size $n$ if the parametric model is right, but are not even consistent outside the model. The nonparametric methods are everywhere consistent and have mean squared errors of size $O(n^{-4/5})$ for broad classes of estimands. The point we are making here is that this picture is not universally true! We show that a simple kernel density estimator can perform better than a directly estimated parametric density on the latter's home turf, for small sample sizes, in the sense of mean integrated squared error. Our main example is that of estimating an unknown normal density. In the process of developing and discussing this somewhat counter-intuitive and half-paradoxical example we touch on several tangential issues of interest, pertaining to exact small-sample analysis of density estimators.

math.ST↗

arXiv2026

Should the Olympic sprint skaters run the 500 meter twice?

The Olympic 500 meter sprint competition is the `Formula One event' of speed skating, and is watched by millions of television viewers. A draw decides who should start in inner lane and who in outer lane. Many skaters dread the last inner lane, where they need to tackle heavier centrifugal forces than their companions in the last outer lane, at maximum speed around 55 km/hour, at a time when fatigue may set in. The aim of this article is to investigate this potential difference between last inner and last outer lane. For this purpose data from eleven Sprint World Championships 1984--1994 are exploited. A bivariate mixed effects model is used that in addition to the inner-outer lane information takes account of different ice and weather conditions on different days, unequal levels for different skaters, and the passing times for the first 100 meter. The underlying `unfairness parameter', estimated with optimal precision, is about 0.05 seconds, and is indeed significantly different from zero; it is about three times as large as its estimated standard deviation. This is enough for medals to change necks. Results from the work reported on here played a decisive role in leading the International Skating Union and the International Olympic Committee to change the rules for the 500 meter sprint event; as of the Nagano 1998 Olympic Games, the sprinters are to skate twice, with one start in inner lane and one in outer lane. The best average result determines the final list, and the best skaters from the first run are paired to skate last in the second run. It has also been decided that the same rules shall apply for the single distance 500 meter World Championships;these are arranged yearly from 1996 onwards.

stat.AP↗

arXiv2026

Performance of Efron and Tibshirani's semiparametric density estimator

Recently, Efron and Tibshirani (Annals of Statistics, 1996) proposed a semiparametric density estimator, which works by multiplying an initial kernel type estimate with a parametric exponential type correction factor, chosen so as to match certain empirical moments. While Efron and Tibshirani investigate and illustrate many aspects of their method, the basic questions of performance, and comparison with other density estimators, were not directly addressed in their article. The purpose of the present paper is to provide formulae for bias and variance and hence mean squared error for the estimator. This additional insight into the method makes it easy to compare its performance with that of other recently proposed semiparametric constructions. A brief comparison study is carried out here. It indicates that the new method, used with lower order polynomials in the exponential correction term, is often better than the kernel estimator, in a reasonable neighbourhood around the normal distribution, but that its performance as a density estimator is more than equalled by other methods. In particular, the recently developed Hjort and Glad estimator (Annals of Statistics, 1995), using a parametric start times a nonparametric correction, wins in eight out of nine test cases, from the list of such suggested by Wand and Jones (Annals of Statistics, 1992).

math.ST↗

arXiv2026

But some are more equal than others

All men are created equal, proclaimed Jefferson in 1776 -- but some are more equal than others, added Orwell in Animal Farm in 1945. So what's the probability that two skaters are exactly equal, to the third decimal places, after four distances?

math.HO↗