 # Second-Order Asymptotically Optimal Statistical Classification

Motivated by real-world machine learning applications, we analyze approximations to the non-asymptotic fundamental limits of statistical classification. In the binary version of this problem, given two training sequences generated according to two unknown distributions P_1 and P_2, one is tasked to classify a test sequence which is known to be generated according to either P_1 or P_2. This problem can be thought of as an analogue of the binary hypothesis testing problem but in the present setting, the generating distributions are unknown. Due to finite sample considerations, we consider the second-order asymptotics (or dispersion-type) tradeoff between type-I and type-II error probabilities for tests which ensure that (i) the type-I error probability for all pairs of distributions decays exponentially fast and (ii) the type-II error probability for a particular pair of distributions is non-vanishing. We generalize our results to classification of multiple hypotheses with the rejection option.

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## I Introduction

In the simple binary hypothesis testing problem, one is given a source sequence and one knows that it is either generated in an i.i.d. fashion from one of two known distributions or . One is then asked to design a test to make this decision. There is a natural trade-off between the type-I and type-II error probabilities. This is quantified by the Chernoff-Stein lemma  in the Neyman-Pearson setting in which the type-I error probability decays exponentially fast in with exponent given by if the type-II error probability is upper bounded by some fixed . Blahut  established the tradeoff between the exponents of the type-I and type-II error probabilities. Strassen  derived a refinement of the Chernoff-Stein lemma. This area of study is now commonly known as second-order asymptotics and it quantifies the backoff from one incurs at finite sample sizes and non-vanishing type-II error probabilities . In all these analyses, the likelihood ratio test  is optimal.

However, in real-world machine learning applications, the generating distributions are not known. For the binary classification framework, one is given two training sequences, one generated from and the other from . Using these training sequences, one attempts to classify a test sequence according to whether one believes that it is generated from either or .

### I-a Main Contributions

Instead of algorithms, in this paper, we are concerned with the information-theoretic limits of the binary classification problem. This was first considered by Gutman who proposed a type-based (empirical distribution-based) test [5, Eq. (6)] and proved that this test is asymptotically optimal in the sense that any other test that achieves the same exponential decay for the type-I error probability for all pairs of distributions, necessarily has a larger type-II error probability for any fixed pair of distributions. Inspired by Gutman’s  and Strassen’s  seminal works, and by practical applications where the number of training and test samples is limited (due to the prohibitive cost in obtaining labeled data), we derive refinements to the tradeoff between the type-I and type-II error probabilities for such tests. In particular, we derive the exact second-order asymptotics [3, 6, 7] for binary classification. Our main result asserts that Gutman’s test is second-order optimal. The proofs follow by judiciously modifying and refining Gutman’s arguments in  in both the achievability and converse proofs. In the achievability part, we apply a Taylor expansion to a generalized form of the Jensen-Shannon divergence  and apply the Berry-Esseen theorem to analyze Gutman’s test. The converse part follows by showing that Gutman’s type-based test is approximately optimal in a certain sense to be made precise in Lemma 7. This study provides intuition for the non-asymptotic fundamental limits and our results have the potential to allow practitioners to gauge the effectiveness of various classification algorithms.

Second, we discuss three consequences of our main result. The first asserts that the largest exponential decay rate of the maximal type-I error probability is a generalized version of the Jensen-Shannon divergence, defined in (3) to follow. This result can be seen as a counterpart of Chernoff-Stein lemma  which is applicable to binary hypothesis testing. Next, we show that our main result can be applied to obtain a second-order asymptotic expansion for the fundamental limits of the two sample homogeneity testing problem [9, Sec. II-C] and the closeness testing problem [10, 11, 12]. Finally, we consider the dual setting of the main result in which the type-I error probabilities are non-vanishing while the type-II error probabilities decay exponentially fast. In this case, the largest exponential decay rate of the type-II error probabilities for Gutman’s rule is given by a Rényi divergence  of a certain order related to the ratio of the lengths of the training and test sequences.

Finally, we generalize our second-order asymptotic result for binary classification to classification of multiple hypotheses with the rejection option. We first consider tests satisfying the following conditions (i) the error probability under each hypothesis decays exponentially fast with the same exponent for all tuples of distributions and (ii) the rejection probability under each hypothesis is upper bounded by a different constant for a particular tuple. We derive second-order approximations of the largest error exponent for all hypotheses and show that a generalization of Gutman’s test by Unnikrishnan in [14, Theorem 4.1] is second-order optimal. The proofs follow by generalizing those for binary classification and carefully analyzing the rejection probabilities. In addition, similarly to the binary case, we also consider a dual setting, in which under each hypothesis, the error probability is non-vanishing for all tuples of distributions and the rejection probability decays exponentially fast for a particular tuple.

### I-B Related Works

The most related work is  where Gutman showed that his type-based test is asymptotically optimal for the binary classification problem and its extension to classification of multiple hypotheses with rejection for Markov sources. Ziv  illustrated the relationship between binary classification and universal data compression. The Bayesian setting of the binary classification problem was studied by Merhav and Ziv . Subsequently, Kelly, Wagner, Tularak and Viswanath  considered the binary classification problem with large alphabets. Unnikrishnan  generalized the result of Gutman by considering classification for multiple hypotheses where there are multiple test sequences. Finally, Unnikrishnan and Huang  approximated the type-I error probability of the binary classification problem using weak convergence analysis.

### I-C Organization of the Rest of the Paper

The rest of our paper is organized as follows. In Section II, we set up the notation, formulate the binary classification problem and present existing results by Gutman . In Section III, we discuss the motivation for our setting and present our second-order result for binary classification. We also discuss some consequences of our main result. In Section IV, we generalize our result for binary classification to classification of multiple hypotheses with the rejection option. The proofs of our results are provided in Section V. The proofs of some supporting lemmas are deferred to the appendices.

## Ii Problem Formulation and Existing Results

### Ii-a Notation

Random variables and their realizations are in upper (e.g., ) and lower case (e.g., ) respectively. All sets are denoted in calligraphic font (e.g., ). We use to denote the complement of . Let

be a random vector of length

. All logarithms are base . We use

to denote the cumulative distribution function (cdf) of the standard Gaussian and

its inverse. Let be the corresponding complementary cdf. We use to denote the complementary cdf of a chi-squared random variable with degrees of freedom and its inverse. Given any two integers , we use to denote the set of integers and use to denote

. The set of all probability distributions on a finite set

is denoted as . Notation concerning the method of types follows . Given a vector , the type or empirical distribution is denoted as . The set of types formed from length- sequences with alphabet is denoted as . Given , the set of all sequences of length with type , the type class, is denoted as . The support of the probability mass function is denoted as .

### Ii-B Problem Formulation

The main goal in binary hypothesis testing is to classify a sequence as being independently generated from one of two distinct distributions . However, different from classical binary hypothesis testing [19, 2] where the two distributions are known, in binary classification , we do not know the two distributions. We instead have two training sequences and generated in an i.i.d. fashion according to and respectively. Therefore, the two hypotheses are

• : the test sequence and the 1 training sequence are generated according to the same distribution;

• : the test sequence and the 2 training sequence are generated according to the same distribution.

We assume that for some .111 In the following, we will often write for brevity, ignoring the integer constraints on and . The task in the binary classification problem is to design a decision rule (test) . Note that a decision rule partitions the sample space into two disjoint regions: where any triple favors hypothesis and where any triple favors hypothesis .

Given any decision rule and any pair of distributions , we have two types of error probabilities, i.e.,

 β1(ϕn|P1,P2) :=P1{ϕn(XN1,XN2,Yn)=H2}, (1) β2(ϕn|P1,P2) :=P2{ϕn(XN1,XN2,Yn)=H1}, (2)

where for , we define where for all . The two error probabilities in (1) and (2) are respectively known as the type-I and type-II error probabilities.

### Ii-C Existing Results and Definitions

The goal of binary classification is to design a classification rule based on the training sequences. This rule is then used on the test sequence to decide whether or is true. We revisit the study of the fundamental limits of the problem here. Towards this goal, Gutman  proposed a decision rule using marginal types of , and . To present Gutman’s test, we need the following generalization of the Jensen-Shannon divergence . Given any two distributions and any number , let the generalized Jensen-Shannon divergence be

 GJS(P1,P2,α) :=αD(P1∥∥αP1+P21+α)+D(P2∥∥αP1+P21+α). (3)

Given a threshold and any triple , Gutman’s decision rule is as follows:

 ϕGutn(xN1,xN2,yn) :=⎧⎨⎩H1if GJS(^TxN1,^Tyn,α)≤λH2if GJS(^TxN1,^Tyn,α)>λ. (4)

To state Gutman’s main result, we define the following “exponent” function

 F(P1,P2,α,λ) :=min(Q1,Q2)∈P(X)2:GJS(Q1,Q2,α)≤λαD(Q1∥P1)+D(Q2∥P2). (5)

Note that for and that is continuous (a consequence of [20, Lemma 12] in which is continuous if is continuous and is compact).

Gutman [5, Lemma 2 and Theorem 1] showed that the rule in (4) is asymptotically optimal (error exponent-wise) if the type-I error probability vanishes exponentially fast over all pairs of distributions.

###### Theorem 1.

Gutman’s decision rule satisfies the following two properties:

1. Asymptotic/Exponential performance: For any pair of distributions ,

 liminfn→∞−1nlogβ1(ϕGutn|P1,P2) ≥λ, (6) liminfn→∞−1nlogβ2(ϕGutn|P1,P2) ≥F(P1,P2,α,λ). (7)
2. Asymptotic/Exponential Optimality: Fix a sequence of decision rules such that for all pairs of distributions ,

 liminfn→∞−1nlogβ1(ϕn|~P1,~P2)≥λ, (8)

then for any pair of distributions ,

 β2(ϕn|P1,P2)≥β2(ϕGutn|P1,P2), (9)

where is Gutman’s test with threshold defined in (4) which achieves (6)–(7).

We remark that using Sanov’s theorem [21, Chapter 11], one can easily show that, for any pairs of distributions and any , Gutman’s decision rule in (4) satisfies (6) as well as

 limn→∞−1nlogβ2(ϕGutn|P1,P2)=F(P1,P2,α,λ). (10)

Note that Theorem 1 is analogous to Blahut’s work  in which the trade-off of the error exponents for the binary hypothesis testing problem was thoroughly analyzed.

## Iii Binary Classification

### Iii-a Definitions and Motivation

In this paper, motivated by practical applications where the lengths of source sequences are finite (obtaining labeled training samples is prohibitively expensive), we are interested in approximating the non-asymptotic fundamental limits in terms of the tradeoff between type-I and type-II error probabilities of optimal tests. In particular, out of all tests whose type-I error probabilities decay exponentially fast for all pairs of distributions and whose type-II error probability is upper bounded by a constant for a particular pair of distributions, what is the largest decay rate of the sequence of the type-I error probabilities? In other words, we are interested in the following fundamental limit

 λ∗(n,α,ε|P1,P2):=sup{λ∈R+:∃ ϕn s.t. β1(ϕn|~P1,~P2) ≤exp(−nλ), ∀ (~P1,~P2)∈P(X)2, and β2(ϕn|P1,P2) ≤ε}. (11)

 liminfn→∞λ∗(n,α,ε|P1,P2)≥GJS(P1,P2,α). (12)

As a corollary of our result in Theorem 2, we find that the result in (12) is in fact tight and the limit exists. In this paper, we refine the above asymptotic statement and, in particular, provide second-order approximations to .

To conclude this section, we explain why we consider instead of characterizing a seemingly more natural quantity, namely, the largest decay rate of type-I error probability when the type-II error probability is upper bounded by a constant for a particular pair of distributions , i.e.,

 β∗2(n,α,ε|P1,P2) :=inf{r∈[0,1]:∃ ϕn s.t. β1(ϕn|P1,P2)≤r, β2(ϕn|P1,P2)≤ε}. (13)

In the binary classification problem, when we design a test , we do not know the pair of distributions from which the training sequences are generated. Thus, unlike the simple hypothesis testing problem [3, 22], we cannot design of a test tailored to a particular pair of distributions. Instead, we are interested in designing universal tests which have good performances for all pairs of distributions for the type-I (resp. type-II) error probability and at the same time, constrain the type-II (resp. type-I) error probability with respect to a particular pair of distributions .

### Iii-B Main Result

We need the following definitions before presenting our main result. Given any and any pair of distributions , define the following two information densities

 ıi(x|P1,P2,α) :=log(1+α)Pi(x)αP1(x)+P2(x),i∈. (14)

Furthermore, given any pair of distributions , define the following dispersion function

(linear combination of the variances of the information densities)

 V(P1,P2,α) =αVarP1[ı1(X|P1,P2,α)]+VarP2[ı2(X|P1,P2,α)]. (15)
###### Theorem 2.

For any , any and any pair of distributions , we have

 λ∗(n,α,ε|P1,P2)=GJS(P1,P2,α)+√V(P1,P2,α)nΦ−1(ε)+O(lognn). (16)

Theorem 2 is proved in Section V-A. In (16), and are respectively known as the first- and second-order terms in the asymptotic expansion of . Since in most applications, and so the second-order term represents a backoff from the exponent at finite sample sizes . As shown by Polyanskiy, Poor and Verdú  (also see ), in the channel coding context, these two terms usually constitute a reasonable approximation to the non-asymptotic fundamental limit at moderate . This will also be corroborated numerically for the current problem in Section III-C. Several other remarks are in order.

First, we remark that since the achievability part is based on Gutman’s test, this test in (4) is second-order optimal. This means that it achieves the optimal second-order term in the asymptotic expansion of .

Second, as a corollary of our result, we obtain that for any ,

 limn→∞λ∗(n,α,ε|P1,P2)=GJS(P1,P2,α). (17)

In other words, a strong converse for holds. This result can be understood as the counterpart of the Chernoff-Stein lemma  for the binary classification problem (with strong converse). In the following, we comment on the influence of the ratio of the number of training and test samples in terms of the dominant term in . Note that the generalized Jensen-Shannon divergence admits the following properties:

• is increasing in ;

• and .

Thus, we conclude that the longer the lengths of training sequences (relative to the test sequence), the better the performance in terms of exponential decay rate of type-I error probabilities for all pairs of distributions. In the extreme case in which , i.e., the training sequence is arbitrarily short compared to the test sequence, we conclude that type-I error probability cannot decay exponentially fast. However, in the other extreme in which , we conclude that type-I error probabilities for all pairs of distributions decay exponentially fast with the dominant (first-order) term being . This implies that we can achieve the optimal decay rate determined by the Chernoff-Stein lemma  for binary hypothesis testing. Intuitively, this occurs since when

, we can estimate the true pair of distributions with arbitrarily high accuracy (using the large number training samples). In fact, we can say even more. Based on the formula in (

15), we deduce that, , the relative entropy variance, so we recover Strassen’s seminal result [3, Theorem 1.1] concerning the second-order asymptotics of binary hypothesis testing.

Finally, we remark that the binary classification problem is closely related with the so-called two sample homogeneity testing problem [9, Sec. II-C] and the closeness testing problem [10, 11, 12] where given two i.i.d. generated sequences and , one aims to determine whether the two sequences are generated according to the same distribution or not. Thus, in this problem, we have the following two hypotheses:

• : the two sequences and are generated according to the same distribution;

• : the two sequences and are generated according to different distributions.

The task in such a problem is to design a test . Given any and any , the false-alarm and miss detection probabilities for such a problem are

 βFA(ϕn|P1) :=PP1{ϕn(XN,Yn)=H2}, (18) βMD(ϕn|P1,P2) :=PP1,P2{ϕn(XN,Yn)=H1}, (19)

where in , the random variables and are both distributed i.i.d. according to and in , and are distributed i.i.d. according to and respectively. Paralleling our setting for the binary classification problem, we can study the following fundamental limit of the two sample hypothesis testing problem:

 ξ∗(n,α,ε|P1,P2):=sup{λ∈R+:∃ ϕn s.t. βFA(ϕn|~P1) βMD(ϕn|P1,P2) ≤ε}. (20)
###### Corollary 3.

For any , any and any , we have

 ξ∗(n,α,ε|P1,P2) =GJS(P1,P2,α)+√V(P1,P2,α)nΦ−1(ε)+O(lognn). (21)

Since the proof is similar to that of Theorem 2, we omit it. Corollary 3 implies that Gutman’s test is second-order optimal for the two sample homogeneity testing problem. We remark that for the binary classification problem without rejection (i.e., we are not allowed to declare the neither nor is true), the problem is essentially the same as the two sample hypothesis testing problem except that we have one more training sequence. However, as shown in Theorem 2, the second training sequence is not useful in order to obtain second-order optimal result. This asymmetry in binary classification problem is circumvented if one also considers a rejection option as will be demonstrated in Section IV. Fig. 1: (a) Type-II error probability for Gutman’s test with target error probability ε=0.2. The error bars denote 1standard deviation above and below the mean over the independent experiments; (b) Natural logarithm of the maximal type-I error probability for Gutman’s test. The error bars denote 10 standard deviations above and below the mean.

### Iii-C Numerical Simulation for Theorem 2

In this subsection, we present a numerical example to illustrate the performance of Gutman’s test in (4) and the accuracy of our theoretical results. We consider binary sources with alphabet . Throughout this subsection, we set .

In Figure 1(a), we plot the type-II error probability for a particular pair of distributions where and . The threshold is chosen to be the second-order asymptotic expansion

 ^λ:=GJS(P1,P2,α)+√V(P1,P2,α)nΦ−1(ε), (22)

with target error probability being set to . Each point in Figure 1(a) is obtained by estimating the average error probability in the following manner. For each length of the test sequence , we estimate the type-II error probability of a single Gutman’s test in (4) using independent experiments. From Figure 1(a), we observe that the simulated error probability for Gutman’s test is close to the target error probability of as the length of the test sequence increases. We believe that there is a slight bias in the results as we have not taken the third-order term, which scales as into account in the threshold in (22).

In Figure 1(b), we plot the natural logarithm of the theoretical upper bound and the maximal empirical type-I error probability over all pairs of distributions . We set the fixed pair of distributions to be and and choose . We ensured that the threshold in (22) is small enough so that even if is large, the type-I error event occurs sufficiently many times and thus the numerical results are statistically significant. From Figure 1(b), we observe that the simulated probability lies below the theoretical one as expected. The gap can be explained by the fact that the method of types analysis is typically loose non-asymptotically due to a large polynomial factor. A more refined analysis based on strong large deviations [24, Theorem 3.7.2] would yield better estimates on exponentially decaying probabilities but we do not pursue this here. However, we do note that as becomes large, the slopes of the simulated and theoretical curves become increasingly close to each other (simulated slope at is ; theoretical slope at is ), showing that on the exponential scale, our estimate of the maximal type-I error probability is relatively tight.

### Iii-D Analysis of Gutman’s Test in A Dual Setting

In addition to analyzing , one might also be interested in decision rules whose type-I error probabilities for all pairs of distributions are non-vanishing and whose type-II error probabilities for a particular pair of distributions decays exponentially fast. To be specific, for any decision rule , we consider the following non-asymptotic fundamental limit:

 τ∗(n,α,ε|ϕn,P1,P2) :=sup{τ∈R+:β1(ϕn|~P1,~P2)≤ε, ∀ (~P1,~P2)∈P(X)2 and β2(ϕn|P1,P2)≤exp(−nτ) }. (23)

This can be considered as a dual to the problem studied in Sections III-A to III-C. We characterize the asymptotic behavior of when .

To do so, we recall that the Rényi divergence of order   is defined as

 Dγ(P1∥P2):=1γ−1log(∑x∈XPγ1(x)P1−γ2(x)). (24)

Note that , the usual relative entropy.

###### Proposition 4.

For any , any and any pair of distributions ,

 limn→∞τ∗(n,α,ε|ϕGutn,P1,P2)=Dα1+α(P1∥P2). (25)

The proof of Proposition 4 is provided in Section V-B. Several remarks are in order.

First, the performance of Gutman’s test in (4) under this dual setting is dictated by , which is different from in Theorem 2. Intuitively, this is because of two reasons. Firstly, for the type-I error probabilities to be upper bounded by a non-vanishing constant for all pairs of distributions, one needs to choose (implied by the weak convergence analysis in ). Consequently, the type-II exponent then satisfies

 limλ↓0F(P1,P2,α,λ) =minQ∈P(X)αD(Q∥P1)+D(Q∥P2)=Dα1+α(P1∥P2). (26)

Second, as , the exponent and thus the type-II error probability does not decay exponentially fast. However, when , the exponent and thus we can achieve the optimal exponential decay rate of the type-II error probability as if and were known (implied by the Chernoff-Stein lemma ).

Finally, we remark that Proposition 4 is not comparable to Theorem 2 since the settings are different. Furthermore, Proposition 4 applies only to Gutman’s test while Theorem 2 contains an optimization over all tests or classifiers.

## Iv Classification of Multiple Hypotheses with the Rejection Option

In this section, we generalize our second-order asymptotic result for binary classification in Theorem 2 to classification of multiple hypotheses with rejection [5, Theorem 2].

### Iv-a Problem Formulation

Given training sequences generated i.i.d. according to distinct distributions , in classification of multiple hypotheses with rejection, one is asked to determine whether a test sequence is generated i.i.d. according to a distribution in or some other distribution. In other words, there are hypotheses:

• for each : the test sequence and training sequence are generated according to the same distribution;

• : the test sequence is generated according to a distribution different from those in which the training sequences are generated from.

In the following, for simplicity, we use to denote , to denote and to denote . Recall that for brevity. The main task in classification of multiple hypotheses with rejection is thus to design a test . Note that any such test partitions the sample space into disjoint regions: acceptance regions where favors hypothesis and a rejection region where favors hypothesis .

Given any test and any tuple of distributions , we have the following error probabilities and rejection probabilities: for each ,

 βj(ψn|P) :=Pj{ψn(XN,Yn)∉{Hj,Hr}}, (27) ζj(ψn|P) :=Pj{ψn(XN,Yn)=Hr}, (28)

where similarly to (1) and (2), for , we define where is distributed i.i.d. according to for all . We term the probabilities in (27) and (28) as type- error and rejection probabilities respectively for each .

Similarly to Section III, we are interested in the following question. For all tests satisfying (i) for each , the type- error probability decays exponentially fast with the exponent being at least for all tuples of distributions and (ii) for each , the type- rejection probability is upper bounded by a constant for a particular tuple of distributions, what is the largest achievable exponent ? In other words, given , we are interested in the following fundamental limit:

 λ∗(n,α,ε|P):=sup{λ∈R+:∃ψn s.t. ∀j∈[M],βj(ψn|~P) ≤exp(−nλ),∀ ~P∈P(X)M, ζj(ψn|P) ≤εj}. (29)

### Iv-B Main Result

For brevity, let . Given any , for each , let

 θj(P,α) :=mini∈[M]:i≠jGJS(Pi,Pj,α). (30)

Consider any such that the minimizer for in (30) is unique for each and denote the unique minimizer for as . For simplicity, we use to denote when the dependence on is clear.

From Gutman’s result in [5, Thereoms 2 and 3], we conclude that

 ≥minj∈[M]GJS(Pi∗(j),Pj,α)=min(i,j)∈MGJS(Pi,Pj,α). (31)

In this section, we refine the above asymptotic statement, and in particular, derive the second-order approximations to the fundamental limit .

Given any tuple of distributions and any vector , let

 J1(P,α) :=argminj∈[M]GJS(Pi∗(j),Pj,α), (32) J2(P,α) :=argminj∈J1(P,α)√V(Pi∗(j),Pj,α)Φ−1(εj). (33)
###### Theorem 5.

For any , any and any tuple of distributions satisfying that the minimizer for is unique for each , we have

 λ∗(n,α,ε|P) =GJS(Pi∗(j),Pj,α)+√V(Pi∗(j),Pj,α)nΦ−1(εj)+O(lognn), (34)

where (34) holds for any .

The proof of Theorem 5 is given in Section V-C. Several remarks are in order.

First, in the achievability proof, we make use of a test proposed by Unnikrishnan [14, Theorem 4.1] and show that it is second-order optimal for classification of multiple hypotheses with rejection.

Second, we remark that it is not straightforward to obtain the results in Theorem 5 by using the same set of techniques to prove Theorem 2. The converse proof of Theorem 5 is a generalization of that for Theorem 2. However, the achievability proof is more involved. As can be gleaned in our proof in Section V-C, the test by Unnikrishnan (see (107)) outputs rejection if the second smallest value of is smaller than a threshold . The main difficulty lies in identifying the index of the second smallest value in . Note that for each realization of , such an index can potentially be different. However, we show that for any tuple of distributions satisfying the condition in Theorem 5, if the training sequences are generated in an i.i.d. fashion according to , with probability tending to one, the index of the second smallest value in under hypothesis is given by . Equipped this important observation, we establish our achievability proof by proceeding similarly to that of Theorem 2.

Finally, we remark that one might also consider tests which provide inhomogeneous performance guarantees under different hypotheses in terms of the error probabilities for all tuples of distributions and, at the same time, constrains the sum of all rejection probabilities to be upper bounded by some . In this direction, the fundamental limit of interest is

 Λ(n,α,ε|P):={λM∈RM+:∃ ψn s.t. ∀j∈[M],βj(ψn|~P) ∑j∈[M]ζj(ψn|P) ≤ε}. (35)

Characterizing the second-order asymptotics of the set for is challenging. However, when , using similar proof techniques as that for Theorem 5, we can characterize the following second-order region [18, Chapter 6]

 L(α,ε|P1,P2):={(L1,L2)∈R+ :∃ {ψn}∞n=1 s.t. ∀(~P1,~P2)∈P(X)2, liminfn→∞1√n(log1β1(ψn|~P1,~P2)−nGJS(P1,P2,α))≥L1, liminfn→∞1√n(log1β2(ψn|~P1,~P2)−nGJS(P2,P1,α))≥L2, limsupn→∞∑j∈ζj(ψn|P1,P2)≤ε}. (36)

Indeed, one can consider the following generalization of Gutman’s test [5, Theorem 2]

 ψGutn(xN1,xN2,yn) :=⎧⎪ ⎪ ⎪ ⎪⎨⎪ ⎪ ⎪ ⎪⎩H1if GJS(^TxN2,^Ty